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Grid Like StrategyIt is possible to use progressive position sizing in order to recover from past losses, a well-known position sizing system being the "martingale", which consists of doubling your position size after a loss, this allows you to recover any previous losses in a losing streak + winning an extra. This system has seen a lot of attention from the trading community (mostly from beginners), and many strategies have been designed around the martingale, one of them being "grid trading strategies". While such strategies often shows promising results on paper, they are often subjects to many frictions during live trading that makes them totally unusable and dangerous to the trader. The motivations behind posting such a strategy isn't to glorify such systems, but rather to present the problems behind them, many users come to me with their ideas and glorious ways to make money, sometimes they present strategies using the martingale, and it is important to present the flaws of this methodology rather than blindly saying "you shouldn't use it". Strategy Settings Point determines the "grid" size and should be adjusted accordingly to the scale of the symbol you are applying the strategy to. Higher value would require larger price movements in order to trigger a trade, as such higher values will generate fewer trades. The order size determines the number of contracts/shares to purchase. The martingale multiplier determines the factor by which the position size is multiplied after a loss, using values higher to 2 will "squarify" your balance, while a value of 1 would use a constant position sizing. Finally, the anti-martingale parameter determines whether the strategy uses a reverse martingale or not, if set to true then the position size is multiplied after any wins. The Grid Grid strategies are commons and do not present huge problems until we use certain position sizing methods such as the martingale. A martingale is extremely sensitive to any kind of friction (frictional costs, slippage...etc), the grid strategy aims to provide a stable and simple environment where a martingale might possibly behave well. The goal of a simple grid strategy is to go long once the price crossover a certain level, a take profit is set at the level above the current one and stop loss is placed at the level below the current one, in a winning scenario the price reach the take profit, the position is closed and a new one is opened with the same setup. In a losing scenario, the price reaches the stop loss level, the position is closed and a short one is opened, the take profit is set at the level below the current one, and a stop loss is set at the level above the current one. Note that all levels are equally spaced. It follows from this strategy that wins and losses should be constant over time, as such our balance would evolve in a linear fashion. This is a great setup for a martingale, as we are theoretically assured to recover all the looses in a losing streak. Martingale - Exponential Decays - Risk/Reward By using a martingale we double our position size (exposure) each time we lose a trade, if we look at our balance when using a martingale we see significant drawdowns, with our balance peaking down significantly. The martingale sequence is subject to exponential growth, as such using a martingale makes our balance exposed to exponential decays, that's really bad, we could basically lose all the initially invested capital in a short amount of time, it follows from this that the theoretical success of a martingale is determined by what is the maximum losing streak you can endure Now consider how a martingale affects our risk-reward ratio, assuming unity position sizing our martingale sequence can be described by 2^(x-1) , using this formula we would get the amount of shares/contracts we need to purchase at the x trade of a losing streak, we would need to purchase 256 contracts in order to recover from a losing streak of size 9, this is enormous when you take into account that your wins are way smaller, the risk-reward ratio is totally unfair. Of course, some users might think that a losing streak of size 9 is pretty unlikely, if the probability of winning and losing are both equal to 0.5, then the probability of 9 consecutive losses is equal to 0.5^9 , there are approximately 0.2% of chance of having such large losing streak, note however that under a ranging market such case scenario could happen, but we will see later that the length of a losing streak is not the only problem. Other Problems Having a capital large enough to tank 9any number of consecutive losses is not the only thing one should focus on, as we have to take into account market prices and trading dynamics, that's where the ugly part start. Our first problem is frictional costs, one example being the spread, but this is a common problem for any strategy, however here a martingale is extra sensitive to it, if the strategy does not account for it then we will still double our positions costs but we might not recover all the losses of a losing streak, instead we would be recovering only a proportion of it, under such scenario you would be certain to lose over time. Another problem are gaps, market price might open under a stop-loss without triggering it, and this is a big no-no. Equity of the strategy on AMD, in a desired scenario the equity at the second arrow should have been at a higher position than the equity at the first arrow. In order for the strategy to be more effective, we would need to trade a market that does not close, such as the cryptocurrency market. Finally, we might be affected by slippage, altho only extreme values might drastically affect our balance. The Anti Martingale The strategy lets you use an anti-martingale, which double the position size after a win instead of a loss, the goal here is not to recover from a losing strike but instead to profit from a potential winning streak. Here we are exposing your balance to exponential gross but you might also lose a trade at the end a winning streak, you will generally want to reinitialize your position size after a few wins instead of waiting for the end of a streak. Alternative You can use other-kind of progressions for position sizing, such as a linear one, increasing your position size by a constant number each time you lose. More gentle progressions will recover a proportion of your losses in a losing streak. You can also simulate the effect of a martingale without doubling your position size by doubling your target profit, if for example you have a 10$ profit-target/stop-loss and lose a trade, you can use a 20$ profit target to recover from the lost trade + gain a profit of 10$. While this approach does not introduce exponential decay in your balance, you are betting on the market reaching your take profits, considering the fact that you are doubling their size you are expecting market volatility to increase drastically over time, as such this approach would not be extremely effective for high losing streak. Conclusion You will see a lot of auto-trading strategies that are based on a grid approach, they might even use a martingale. While the backtests will look appealing, you should think twice before using such kind of strategy, remember that frictional costs will be a huge challenge for the strategy, and that it assumes that the trader has an important initial capital. We have also seen that the risk/reward ratio is theoretically the worst you can have on a strategy, having a low reward and a high risk. This does not mean that progressive position sizing is bad, but it should not be pushed to the extreme. It is nice to note that the martingale is originally a betting system designed for casino games, which unlike trading are not subject to frictional costs, but even casino players don't use it, so why would you? Thx for reading
Pine Script® stratejisi
alexgrover tarafından
14
Other altcoins BTC capitalization histogram [peregringlk]Introduction ========== This study is intented to be used in combination with my other study "Other alts compensated cap". Read its description, in particular, it's rationale, to understand why I have removed the big capitalized altcoins from these studies. The middle indicator in the image is that other study, while the indicator in the buttom of the image is that one. It shows, in form of histogram, the BTC capitalization change rate (per candle, using closes) of the "OTHERS" altcoins together with the inverse of the BTCUSD price change rate per candle. NOTE: I call the change rate to the multiplier factor of price from bar to bar. For example, a change rate of 1.20 means +20% respect to "yesterday", and a change rate of 0.80 means -20%. The idea is to know what are altcoin markets (against BTC) doing after each BTC price change. Definitions ========= I will use ALT from now one as the name of an index or fictional coin that represents the average price of all other altcoins combined. I'll use then ALTUSD to represent the price against USD of such fictional coin (= the OTHERS capitalization, as if the USD capitalization of altcoins were the USD price of ALT), and ALTBTC to represent the same price but against BTC (calculated by taking ALTUSD/BITSTAMP:BTCUSD; the choosing of BITSTAMP is because it's the market with a longer history in tradingview). Since I use the "OTHERS" security, I cannot know the real altcoin index so I can only estimate by using the capitalization. CIX100 could be a solution, but it is too recent in time as to inspect past price actions. Description ========= For example, let's assume BTCUSD decreases by 20% today. It would cause a fall in ALTUSD of 20% (just maths). So, what should it happen in ALTBTC to preserve the original ALTUSD price? People should buy alts in BTC markets by a factor of 1/0.8 = 1.25. Or in other words, unless there are a +25% grow in ALTBTC, ALTUSD would see a decrease in value. This is what the histogram shows. The red columns shows the ALTBTC change rate per candle, while each green column shows what is the required change rate in ALTBTC required to preserve its ALTUSD value (capitalization). In other words, the green columns are the "targets" to preserve USD capitalization in ALTBTC, while the red histogram shows the actual changes. Also, it shows two curves. There are just the change rate accumulation during some customizable interval (the same for both lines, and 7 by default; or the "week" for daily candles). The green line is the accumulated "target" change rate within that period of time (the accumulated product of the last `interval` change rates), and the red line is the actual change rate for the same `interval` candles. Interpretation ============ If red column values are bigger than the green ones (green column is negative, and red column is positive; or both are positives but the red one "put outs", or both are negative but the red column doesn't "put out"), OTHERS USD capitalization has increased. If red column values are lower than the green ones (green column is positive and red column is negative; or both are positives but the red one doesn't "put out"; or both are negative but the red column "put outs"), OTHERS USD capitalization has decreased. The same for the continuous lines: if the red line is above the green one, OTHERS USD capitalization has increased during "the past week". Otherwise, it has decreased. The added value of this indicator is that it allows you to know "why". For example, if a green column is positive, and its corresponding red column is positive as well, but below the green one, the capitalization has decreased but BECAUSE the btc price has fallen, not because there was a sellof in alts. Actually, there was some buys (the ALTBTC price increased); it just it was not enough to counteract the btc fall. That can be clearly seen in the remarked candle in the plot, the "coronavirus" sellof. The BTCUSD fall was huge (the hugest in BTC history), and the green column is telling you that to preserve the capitalization a lot of buys were required. However, that didn't happen. Actually, the OTHER alts were pretty quiet (the red column is tiny), causing a massive indirect loss of capitalization. Also, with the curves, you can know if there was a total gainning or loss of capitalization during the past few days or candles. Also you can try to spot the beginning of alts seasons by crosses between red and green lines: if the red lines crosses above the green one (because there was a continuous sequence of red columns above green ones), it means that, potentially, were are at the beginning of an alt season because people are accumulating. Table of cases =========== - if the green column is positive (BTCUSD is down) - if the red column is positive (ALTBTC is up) - bigger than the green column: ALTBTC buys are stronger than required by arbitrage and have counteracted and overcome the BTC fall. - shorter than the green column: there have been some buys but not enough, so the BTCUSD fall has not been fully counteracted. - if the red column is negative (ALTBTC is down): the loss is double: BTCUSD have lost value + ALTBTC is bleeding. - If the green column is negative (BTCUSD is up) - if the red column is negative (ALTBTC is down) - bigger than the green column: ALTBTC sells are so strong that have counteracted the BTC increase in value, causing a loss of USD value. - shorter than the green column: there have been sells but overall the ALTUSD price has increased. - if the red column is positive (ALTBTC is up): the gain is double: BTCUSD has gain value + ALTBTC is also growing.
Pine Script® göstergesi
peregringlk tarafından
Martingale Strategy Simulator [BackQuant]Martingale Strategy Simulator Purpose This indicator lets you study how a martingale-style position sizing rule interacts with a simple long or short trading signal. It computes an equity curve from bar-to-bar returns, adapts position size after losing streaks, caps exposure at a user limit, and summarizes risk with portfolio metrics. An optional Monte Carlo module projects possible future equity paths from your realized daily returns. What a martingale is A martingale sizing rule increases stake after losses and resets after a win. In its classical form from gambling, you double the bet after each loss so that a single win recovers all prior losses plus one unit of profit. In markets there is no fixed “even-money” payout and returns are multiplicative, so an exact recovery guarantee does not exist. The core idea is unchanged: Lose one leg → increase next position size Lose again → increase again Win → reset to the base size The expectation of your strategy still depends on the signal’s edge. Sizing does not create positive expectancy on its own. A martingale raises variance and tail risk by concentrating more capital as a losing streak develops. What it plots Equity – simulated portfolio equity including compounding Buy & Hold – equity from holding the chart symbol for context Optional helpers – last trade outcome, current streak length, current allocation fraction Optional diagnostics – daily portfolio return, rolling drawdown, metrics table Optional Monte Carlo probability cone – p5, p16, p50, p84, p95 aggregate bands Model assumptions Bar-close execution with no slippage or commissions Shorting allowed and frictionless No margin interest, borrow fees, or position limits No intrabar moves or gaps within a bar (returns are close-to-close) Sizing applies to equity fraction only and is capped by your setting All results are hypothetical and for education only. How the simulator applies it 1) Directional signal You pick a simple directional rule that produces +1 for long or −1 for short each bar. Options include 100 HMA slope, RSI above or below 50, EMA or SMA crosses, CCI and other oscillators, ATR move, BB basis, and more. The stance is evaluated bar by bar. When the stance flips, the current trade ends and the next one starts. 2) Sizing after losses and wins Position size is a fraction of equity: Initial allocation – the starting fraction, for example 0.15 means 15 percent of equity Increase after loss – multiply the next allocation by your factor after a losing leg, for example 2.00 to double Reset after win – return to the initial allocation Max allocation cap – hard ceiling to prevent runaway growth At a high level the size after k consecutive losses is alloc(k) = min( cap , base × factor^k ) . In practice the simulator changes size only when a leg ends and its PnL is known. 3) Equity update Let r_t = close_t / close_{t-1} − 1 be the symbol’s bar return, d_{t−1} ∈ {+1, −1} the prior bar stance, and a_{t−1} the prior bar allocation fraction. The simulator compounds: eq_t = eq_{t−1} × (1 + a_{t−1} × d_{t−1} × r_t) . This is bar-based and avoids intrabar lookahead. Costs, slippage, and borrowing costs are not modeled. Why traders experiment with martingale sizing Mean-reversion contexts – if the signal often snaps back after a string of losses, adding size near the tail of a move can pull the average entry closer to the turn Behavioral or microstructure edges – some rules have modest edge but frequent small whipsaws; size escalation may shorten time-to-recovery when the edge manifests Exploration and stress testing – studying the relationship between streaks, caps, and drawdowns is instructive even if you do not deploy martingale sizing live Why martingale is dangerous Martingale concentrates capital when the strategy is performing worst. The main risks are structural, not cosmetic: Loss streaks are inevitable – even with a 55 percent win rate you should expect multi-loss runs. The probability of at least one k-loss streak in N trades rises quickly with N. Size explodes geometrically – with factor 2.0 and base 10 percent, the sequence is 10, 20, 40, 80, 100 (capped) after five losses. Without a strict cap, required size becomes infeasible. No fixed payout – in gambling, one win at even odds resets PnL. In markets, there is no guaranteed bounce nor fixed profit multiple. Trends can extend and gaps can skip levels. Correlation of losses – losses cluster in trends and in volatility bursts. A martingale tends to be largest just when volatility is highest. Margin and liquidity constraints – leverage limits, margin calls, position limits, and widening spreads can force liquidation before a mean reversion occurs. Fat tails and regime shifts – assumptions of independent, Gaussian returns can understate tail risk. Structural breaks can keep the signal wrong for much longer than expected. The simulator exposes these dynamics in the equity curve, Max Drawdown, VaR and CVaR, and via Monte Carlo sketches of forward uncertainty. Interpreting losing streaks with numbers A rough intuition: if your per-trade win probability is p and loss probability is q=1−p , the chance of a specific run of k consecutive losses is q^k . Over many trades, the chance that at least one k-loss run occurs grows with the number of opportunities. As a sanity check: If p=0.55 , then q=0.45 . A 6-loss run has probability q^6 ≈ 0.008 on any six-trade window. Across hundreds of trades, a 6 to 8-loss run is not rare. If your size factor is 1.5 and your base is 10 percent, after 8 losses the requested size is 10% × 1.5^8 ≈ 25.6% . With factor 2.0 it would try to be 10% × 2^8 = 256% but your cap will stop it. The equity curve will still wear the compounded drawdown from the sequence that led to the cap. This is why the cap setting is central. It does not remove tail risk, but it prevents the sizing rule from demanding impossible positions Note: The p and q math is illustrative. In live data the win rate and distribution can drift over time, so real streaks can be longer or shorter than the simple q^k intuition suggests.. Using the simulator productively Parameter studies Start with conservative settings. Increase one element at a time and watch how the equity, Max Drawdown, and CVaR respond. Initial allocation – lower base reduces volatility and drawdowns across the board Increase factor – set modestly above 1.0 if you want the effect at all; doubling is aggressive Max cap – the most important brake; many users keep it between 20 and 50 percent Signal selection Keep sizing fixed and rotate signals to see how streak patterns differ. Trend-following signals tend to produce long wrong-way streaks in choppy ranges. Mean-reversion signals do the opposite. Martingale sizing interacts very differently with each. Diagnostics to watch Use the built-in metrics to quantify risk: Max Drawdown – worst peak-to-trough equity loss Sharpe and Sortino – volatility and downside-adjusted return VaR 95 percent and CVaR – tail risk measures from the realized distribution Alpha and Beta – relationship to your chosen benchmark If you would like to check out the original performance metrics script with multiple assets with a better explanation on all metrics please see Monte Carlo exploration When enabled, the forecast draws many synthetic paths from your realized daily returns: Choose a horizon and a number of runs Review the bands: p5 to p95 for a wide risk envelope; p16 to p84 for a narrower range; p50 as the median path Use the table to read the expected return over the horizon and the tail outcomes Remember it is a sketch based on your recent distribution, not a predictor Concrete examples Example A: Modest martingale Base 10 percent, factor 1.25, cap 40 percent, RSI>50 signal. You will see small escalations on 2 to 4 loss runs and frequent resets. The equity curve usually remains smooth unless the signal enters a prolonged wrong-way regime. Max DD may rise moderately versus fixed sizing. Example B: Aggressive martingale Base 15 percent, factor 2.0, cap 60 percent, EMA cross signal. The curve can look stellar during favorable regimes, then a single extended streak pushes allocation to the cap, and a few more losses drive deep drawdown. CVaR and Max DD jump sharply. This is a textbook case of high tail risk. Strengths Bar-by-bar, transparent computation of equity from stance and size Explicit handling of wins, losses, streaks, and caps Portable signal inputs so you can A–B test ideas quickly Risk diagnostics and forward uncertainty visualization in one place Example, Rolling Max Drawdown Limitations and important notes Martingale sizing can escalate drawdowns rapidly. The cap limits position size but not the possibility of extended adverse runs. No commissions, slippage, margin interest, borrow costs, or liquidity limits are modeled. Signals are evaluated on closes. Real execution and fills will differ. Monte Carlo assumes independent draws from your recent return distribution. Markets often have serial correlation, fat tails, and regime changes. All results are hypothetical. Use this as an educational tool, not a production risk engine. Practical tips Prefer gentle factors such as 1.1 to 1.3. Doubling is usually excessive outside of toy examples. Keep a strict cap. Many users cap between 20 and 40 percent of equity per leg. Stress test with different start dates and subperiods. Long flat or trending regimes are where martingale weaknesses appear. Compare to an anti-martingale (increase after wins, cut after losses) to understand the other side of the trade-off. If you deploy sizing live, add external guardrails such as a daily loss cut, volatility filters, and a global max drawdown stop. Settings recap Backtest start date and initial capital Initial allocation, increase-after-loss factor, max allocation cap Signal source selector Trading days per year and risk-free rate Benchmark symbol for Alpha and Beta UI toggles for equity, buy and hold, labels, metrics, PnL, and drawdown Monte Carlo controls for enable, runs, horizon, and result table Final thoughts A martingale is not a free lunch. It is a way to tilt capital allocation toward losing streaks. If the signal has a real edge and mean reversion is common, careful and capped escalation can reduce time-to-recovery. If the signal lacks edge or regimes shift, the same rule can magnify losses at the worst possible moment. This simulator makes those trade-offs visible so you can calibrate parameters, understand tail risk, and decide whether the approach belongs anywhere in your research workflow.
Pine Script® göstergesi
BackQuant tarafından
FibADX MTF Dashboard — DMI/ADX with Fibonacci DominanceFibADX MTF Dashboard — DMI/ADX with Fibonacci Dominance (φ) This indicator fuses classic DMI/ADX with the Fibonacci Golden Ratio to score directional dominance and trend tradability across multiple timeframes in one clean panel. What’s unique • Fibonacci dominance tiers: • BULL / BEAR → one side slightly stronger • STRONG when one DI ≥ 1.618× the other (φ) • EXTREME when one DI ≥ 2.618× (φ²) • Rounded dominance % in the +DI/−DI columns (e.g., STRONG BULL 72%). • ADX column modes: show the value (with strength bar ▂▃▅… and slope ↗/↘) or a tier (Weak / Tradable / Strong / Extreme). • Configurable intraday row (30m/1H/2H/4H) + D/W/M toggles. • Threshold line: color & width; Extended (infinite both ways) or Not extended (historical plot). • Theme presets (Dark / Light / High Contrast) or full custom colors. • Optional panel shading when all selected TFs are strong (and optionally directionally aligned). How to use 1. Choose an intraday TF (30/60/120/240). Enable D/W/M as needed. 2. Use ADX ≥ threshold (e.g., 21 / 34 / 55) to find tradable trends. 3. Read the +DI/−DI labels to confirm bias (BULL/BEAR) and conviction (STRONG/EXTREME). 4. Prefer multi-TF alignment (e.g., 4H & D & W all strong bull). 5. Treat EXTREME as a momentum regime—trail tighter and scale out into spikes. Alerts • All selected TFs: Strong BULL alignment • All selected TFs: Strong BEAR alignment Notes • Smoothing selectable: RMA (Wilder) / EMA / SMA. • Percentages are whole numbers (72%, not 72.18%). • Shorttitle is FibADX to comply with TV’s 10-char limit. Why We Use Fibonacci in FibADX Traditional DMI/ADX indicators rely on fixed numeric thresholds (e.g., ADX > 20 = “tradable”), but they ignore the relationship between +DI and −DI, which is what really determines trend conviction. FibADX improves on this by introducing the Fibonacci Golden Ratio (φ ≈ 1.618) to measure directional dominance and classify trend strength more intelligently. ⸻ 1. Fibonacci as a Natural Strength Threshold The golden ratio φ appears everywhere in nature, growth cycles, and fractals. Since financial markets also behave fractally, Fibonacci levels reflect natural crowd behavior and trend acceleration points. In FibADX: • When one DI is slightly larger than the other → BULL or BEAR (mild advantage). • When one DI is at least 1.618× the other → STRONG BULL or STRONG BEAR (trend conviction). • When one DI is 2.618× or more → EXTREME BULL or EXTREME BEAR (high momentum regime). This approach adds structure and consistency to trend classification. ⸻ 2. Why 1.618 and 2.618 Instead of Random Numbers Other traders might pick thresholds like 1.5 or 2.0, but φ has special mathematical properties: • φ is the most irrational ratio, meaning proportions based on φ retain structure even when scaled. • Using φ makes FibADX naturally adaptive to all timeframes and asset classes — stocks, crypto, forex, commodities. ⸻ 3 . Trading Advantages Using the Fibonacci Golden Ratio inside DMI/ADX has several benefits: • Better trend filtering → Avoid false DI crossovers without conviction. • Catch early momentum shifts → Spot when dominance ratios approach φ before ADX reacts. • Consistency across markets → Because φ is scalable and fractal, it works everywhere. ⸻ 4. How FibADX Uses This FibADX combines: • +DI vs −DI ratio → Measures directional dominance. • φ thresholds (1.618, 2.618) → Classifies strength into BULL, STRONG, EXTREME. • ADX threshold → Confirms whether the move is tradable or just noise. • Multi-timeframe dashboard → Aligns bias across 4H, D, W, M. ⸻ Quick Blurb for TradingView FibADX uses the Fibonacci Golden Ratio (φ ≈ 1.618) to classify trend strength. Unlike classic DMI/ADX, FibADX measures how much one side dominates: • φ (1.618) = STRONG trend conviction • φ² (2.618) = EXTREME momentum regime This creates an adaptive, fractal-aware framework that works across stocks, crypto, forex, and commodities. ⚠️ Disclaimer : This script is provided for educational purposes only. It does not constitute financial advice. Use at your own risk. Always do your own research before making trading decisions. Created by @nomadhedge
Pine Script® göstergesi
nomadhedge tarafından
1
Prime NumbersPrime Numbers highlights prime numbers (no surprise there 😅), tokens and the recent "active" feature in "input". 🔸 CONCEPTS 🔹 What are Prime Numbers? A prime number (or a prime) is a natural number greater than 1 that is not a product of two smaller natural numbers. Wikipedia: Prime number 🔹 Prime Factorization The fundamental theorem of arithmetic states that every integer larger than 1 can be written as a product of one or more primes. More strongly, this product is unique in the sense that any two prime factorizations of the same number will have the same number of copies of the same primes, although their ordering may differ. So, although there are many different ways of finding a factorization using an integer factorization algorithm, they all must produce the same result. Primes can thus be considered the "basic building blocks" of the natural numbers. Wikipedia: Fundamental theorem of arithmetic Math Is Fun: Prime Factorization We divide a given number by Prime Numbers until only Primes remain. Example: 24 / 2 = 12 | 24 / 3 = 8 12 / 3 = 4 | 8 / 2 = 4 4 / 2 = 2 | 4 / 2 = 2 | 24 = 2 x 3 x 2 | 24 = 3 x 2 x 2 or | or 24 = 2² x 3 | 24 = 2² x 3 In other words, every natural/integer number above 1 has a unique representation as a product of prime numbers, no matter how the number is divided. Only the order can change, but the factors (the basic elements) are always the same. 🔸 USAGE The Prime Numbers publication contains two use cases: Prime Factorization: performed on "close" prices, or a manual chosen number. List Prime Numbers: shows a list of Prime Numbers. The other two options are discussed in the DETAILS chapter: Prime Factorization Without Arrays Find Prime Numbers 🔹 Prime Factorization Users can choose to perform Prime Factorization on close prices or a manually given number. ❗️ Note that this option only applies to close prices above 1, which are also rounded since Prime Factorization can only be performed on natural (integer) numbers above 1. In the image below, the left example shows Prime Factorization performed on each close price for the latest 50 bars (which is set with "Run script only on 'Last x Bars'" -> 50). The right example shows Prime Factorization performed on a manually given number, in this case "1,340,011". This is done only on the last bar. When the "Source" option "close price" is chosen, one can toggle "Also current price", where both the historical and the latest current price are factored. If disabled, only historical prices are factored. Note that, depending on the chosen options, only applicable settings are available, due to a recent feature, namely the parameter "active" in settings. Setting the "Source" option to "Manual - Limited" will factorize any given number between 1 and 1,340,011, the latter being the highest value in the available arrays with primes. Setting to "Manual - Not Limited" enables the user to enter a higher number. If all factors of the manual entered number are in the 1 - 1,340,011 range, these factors will be shown; however, if a factor is higher than 1,340,011, the calculation will stop, after which a warning is shown: The calculated factors are displayed as a label where identical factors are simplified with an exponent notation in superscript. For example 2 x 2 x 2 x 5 x 7 x 7 will be noted as 2³ x 5 x 7² 🔹 List Prime Numbers The "List Prime Numbers" option enables users to enter a number, where the first found Prime Number is shown, together with the next x Prime Numbers ("Amount", max. 200) The highest shown Prime Number is 1,340,011. One can set the number of shown columns to customize the displayed numbers ("Max. columns", max. 20). 🔸 DETAILS The Prime Numbers publication consists out of 4 parts: Prime Factorization Without Arrays Prime Factorization List Prime Numbers Find Prime Numbers The usage of "Prime Factorization" and "List Prime Numbers" is explained above. 🔹 Prime Factorization Without Arrays This option is only there to highlight a hurdle while performing Prime Factorization. The basic method of Prime Factorization is to divide the base number by 2, 3, ... until the result is an integer number. Continue until the remaining number and its factors are all primes. The division should be done by primes, but then you need to know which one is a prime. In practice, one performs a loop from 2 to the base number. Example: Base_number = input.int(24) arr = array.new() n = Base_number go = true while go for i = 2 to n if n % i == 0 if n / i == 1 go := false arr.push(i) label.new(bar_index, high, str.tostring(arr)) else arr.push(i) n /= i break Small numbers won't cause issues, but when performing the calculations on, for example, 124,001 and a timeframe of, for example, 1 hour, the script will struggle and finally give a runtime error. How to solve this? If we use an array with only primes, we need fewer calculations since if we divide by a non-prime number, we have to divide further until all factors are primes. I've filled arrays with prime numbers and made libraries of them. (see chapter "Find Prime Numbers" to know how these primes were found). 🔹 Tokens A hurdle was to fill the libraries with as many prime numbers as possible. Initially, the maximum token limit of a library was 80K. Very recently, that limit was lifted to 100K. Kudos to the TradingView developers! What are tokens? Tokens are the smallest elements of a program that are meaningful to the compiler. They are also known as the fundamental building blocks of the program. I have included a code block below the publication code (// - - - Educational (2) - - - ) which, if copied and made to a library, will contain exactly 100K tokens. Adding more exported functions will throw a "too many tokens" error when saving the library. Subtracting 100K from the shown amount of tokens gives you the amount of used tokens for that particular function. In that way, one can experiment with the impact of each code addition in terms of tokens. For example adding the following code in the library: export a() => a = array.from(1) will result in a 100,041 tokens error, in other words (100,041 - 100,000) that functions contains 41 tokens. Some more examples, some are straightforward, others are not ) // adding these lines in one of the arrays results in x tokens , 1 // 2 tokens , 111, 111, 111 // 12 tokens , 1111 // 5 tokens , 111111111 // 10 tokens , 1111111111111111111 // 20 tokens , 1234567890123456789 // 20 tokens , 1111111111111111111 + 1 // 20 tokens , 1111111111111111111 + 8 // 20 tokens , 1111111111111111111 + 9 // 20 tokens , 1111111111111111111 * 1 // 20 tokens , 1111111111111111111 * 9 // 21 tokens , 9999999999999999999 // 21 tokens , 1111111111111111111 * 10 // 21 tokens , 11111111111111111110 // 21 tokens //adding these functions to the library results in x tokens export f() => 1 // 4 tokens export f() => v = 1 // 4 tokens export f() => var v = 1 // 4 tokens export f() => var v = 1, v // 4 tokens //adding these functions to the library results in x tokens export a() => const arraya = array.from(1) // 42 tokens export a() => arraya = array.from(1) // 42 tokens export a() => a = array.from(1) // 41 tokens export a() => array.from(1) // 32 tokens export a() => a = array.new() // 44 tokens export a() => a = array.new(), a.push(1) // 56 tokens What if we could lower the amount of tokens, so we can export more Prime Numbers? Look at this example: 829111, 829121, 829123, 829151, 829159, 829177, 829187, 829193 Eight numbers contain the same number 8291. If we make a function that removes recurrent values, we get fewer tokens! 829111, 829121, 829123, 829151, 829159, 829177, 829187, 829193 //is transformed to: 829111, 21, 23, 51, 59, 77, 87, 93 The code block below the publication code (// - - - Educational (1) - - - ) shows how these values were reduced. With each step of 100, only the first Prime Number is shown fully. This function could be enhanced even more to reduce recurrent thousands, tens of thousands, etc. Using this technique enables us to export more Prime Numbers. The number of necessary libraries was reduced to half or less. The reduced Prime Numbers are restored using the restoreValues() function, found in the library fikira/Primes_4. 🔹 Find Prime Numbers This function is merely added to show how I filled arrays with Prime Numbers, which were, in turn, added to libraries (after reduction of recurrent values). To know whether a number is a Prime Number, we divide the given number by values of the Primes array (Primes 2 -> max. 1,340,011). Once the division results in an integer, where the divisor is smaller than the dividend, the calculation stops since the given number is not a Prime. When we perform these calculations in a loop, we can check whether a series of numbers is a Prime or not. Each time a number is proven not to be a Prime, the loop starts again with a higher number. Once all Primes of the array are used without the result being an integer, we have found a new Prime Number, which is added to the array. Doing such calculations on one bar will result in a runtime error. To solve this, the findPrimeNumbers() function remembers the index of the array. Once a limit has been reached on 1 bar (for example, the number of iterations), calculations will stop on that bar and restart on the next bar. This spreads the workload over several bars, making it possible to continue these calculations without a runtime error. The result is placed in log.info() , which can be copied and pasted into a hardcoded array of Prime Number values. These settings adjust the amount of workload per bar: Max Size: maximum size of Primes array. Max Bars Runtime: maximum amount of bars where the function is called. Max Numbers To Process Per Bar: maximum numbers to check on each bar, whether they are Prime Numbers. Max Iterations Per Bar: maximum loop calculations per bar. 🔹 The End ❗️ The code and description is written without the help of an LLM, I've only used Grammarly to improve my description (without AI :) )
Pine Script® göstergesi
fikira tarafından
13
Granular MA Ribbon🎗️ The Granular MA Ribbon provides a structured view of price action on lower timeframes by incorporating both price-based and volume-weighted moving averages, offering a more nuanced view of market trends and momentum shifts. Furthermore, by using 15-minute intervals for its calculations, it ensures that intraday traders receive a smooth and responsive representation of higher timeframe trends. ⚠️ Note that this indicator is specifically optimized for the 15-minute and 1-hour charts; applying it to longer or shorter periods will distort its calculations and reduce its effectiveness. Adjust visibility settings accordingly. 🧰 Unlike traditional moving averages that may lag or fail to reflect real-time shifts in price dynamics, the Granular MA Ribbon includes a one-day exponential moving average (1D EMA), a one-day volume-weighted moving average (1D VWMA), and a one-week exponential moving average (1W EMA). Together, these elements allow traders to stay aligned with the broader market while making precise intraday trading decisions. 🤷🏻 Why Two Daily Moving Averages? 🔊 Instead of relying on a single moving average, this indicator uses both an EMA and a VWMA to provide a clearer picture of price movement. The EMA reacts quickly to price changes, making it a useful tool for identifying short-term momentum shifts. The VWMA, meanwhile, accounts for volume, ensuring that price movements supported by higher trading activity carry greater weight in the trend calculation. 💪🏻 When the EMA and VWMA diverge significantly, it signals strong momentum. If they begin to converge, it suggests that momentum is weakening or that price may be entering consolidation. The space between these two moving averages is filled with a ribbon, making it easier to see shifts in trend strength. A wide ribbon typically indicates strong momentum, while a narrowing ribbon suggests the trend may be losing steam. 🧮 Calculation Rationale 🔎 The 1D EMA and 1D VWMA are constructed using 15-minute blocks to maintain accuracy on lower timeframes. A full trading day consists of 96 fifteen-minute intervals. Instead of relying on daily candle data, which would reduce the granularity of the moving averages, this method allows the indicator to reflect intra-day trends more accurately. By breaking the day into smaller increments, the moving averages adapt more smoothly to changes in price and volume, making them more reliable for traders working on shorter timeframes. 🔍 The weekly EMA follows the same logic, adjusting based on the selected five-day or seven-day setting. If the market follows a standard five-day trading week, the one-week EMA is calculated using 480 fifteen-minute bars. If the market trades seven days a week, such as in crypto, the weekly EMA is adjusted accordingly to reflect 672 fifteen-minute bars. This setting ensures that traders using the indicator across different asset classes receive accurate trend information. 🫤 Sideways Markets 🔄 When the broader market is in a range-bound state, with no clear trend on the one-day or one-week chart, this indicator helps traders make sense of the short-term price structure. In these conditions, the ribbon will often appear flat, with the 1D EMA and 1D VWMA frequently crossing each other. This suggests that momentum is weak and that price action lacks a strong directional bias. ⚠️ A narrowing ribbon in a sideways market indicates reduced volatility and a potential breakout. If the EMA crosses above the VWMA during consolidation, it may signal a short-term upward move, especially if volume begins to increase. Conversely, if the EMA moves below the VWMA, it could indicate that selling pressure is increasing. However, in choppy conditions, crossovers alone are not enough to confirm a trade. Traders should wait for additional confirmation, such as a breakout from a defined range or a shift in volume. ♭ If the weekly EMA remains flat while the daily ribbon fluctuates, it confirms that the market lacks a strong trend. In such cases, traders may consider fading moves near the top and bottom of a range rather than expecting sustained breakouts. 💹 Trending Markets 🏗️ When the market is in a strong uptrend or downtrend, the ribbon takes on a more structured shape. A widening ribbon that slopes upward signals strong bullish momentum, with price consistently respecting the 1D EMA and VWMA as support. In a downtrend, the ribbon slopes downward, acting as dynamic resistance. 📈 In trending conditions, traders can use the ribbon to time pullback entries. In an uptrend, price often retraces to the VWMA before resuming its upward move. If price holds above both the EMA and VWMA, the trend remains strong. If price begins to close below the VWMA but remains above the EMA, it suggests weakening momentum but not necessarily a reversal. A clean break below both moving averages indicates a shift in trend structure. 📊 The one-week EMA serves as a higher timeframe guide. When price remains above the weekly EMA, it confirms that the broader trend is intact. If price pulls back to the weekly EMA and bounces, it can provide a high-confidence trade entry. Conversely, if price breaks below the weekly EMA and fails to reclaim it, it suggests that the trend may be reversing. ⏳ 5-Day and 7-Day Week Variants 🎚️ The setting for a five-day or seven-day trading week adjusts the calculation of the one-week EMA. This ensures that the indicator remains accurate across different asset classes. 5️⃣ A five-day trading week is appropriate for stocks, futures, and forex markets, where trading pauses on weekends. Using a seven-day week for these markets would create artificial distortions by including non-trading days. 7️⃣ In contrast, the seven-day week setting is ideal for crypto markets, which trade continuously. Without this adjustment, the weekly EMA would fail to reflect weekend price action, leading to misleading trend signals. 🧐 This indicator is expressly designed to complement its higher timeframe counterpart, the Triple Differential Moving Average Braid, optimized for the 1-Day chart.
Pine Script® göstergesi
TheLeadingIndicator tarafından
ZenAlgo - LevelsThis script combines multiple anchored Volume-Weighted Average Price (VWAP) calculations into a single tool, providing a continuous record of past VWAP levels and highlighting when price has tested them. Typically, VWAP indicators show only the current VWAP for a single anchor period, requiring you to either keep re-anchoring manually or juggle multiple instances of different VWAP tools for each timeframe. By contrast, this script automatically tracks both the ongoing VWAP and previously completed VWAP values, along with real-time detection of “tests” (when price crosses a particular VWAP level). It’s especially valuable for traders who want to see how price has interacted with VWAP over several sessions, weeks, or months—without switching between separate indicators or manually setting anchors. Below is a comprehensive explanation of each component, why multiple VWAP lines working together can be more informative than a single line, and how to adjust the script for various markets and trading styles: Primary VWAP vs. Historical VWAP Lines - Standard VWAP indicators typically focus on the current line only. This script also calculates a primary VWAP, but it “locks in” each completed VWAP value when a new time anchor is detected (e.g., new weekly bar, new monthly bar, new session). As a result, you retain an ongoing history of VWAP lines for every completed anchored period. This is more powerful than manually setting up multiple VWAP tools—one for each desired timeframe—because everything is handled in a single script. You avoid chart clutter and the risk of forgetting to reset your manual VWAP at the correct bar. Why Combine Multiple Anchored VWAP Lines in One Script? - Viewing several anchored VWAP lines together offers synergy . You see not only the current VWAP but also previous ones from different sessions or months, all within the same chart pane. This synergy becomes apparent if multiple historical VWAP lines cluster near the same price level, indicating a potentially significant zone of volume-based support or resistance. Handling this manually would involve repeatedly setting separate VWAP indicators, each reset at specific points, which is time-consuming and prone to error. In this script, the process is automated: as soon as the anchor changes, a completed VWAP line is stored so you can observe how price eventually reacts to it, repeatedly or not at all. Automated “Test” Detection - Once a historical VWAP line is set, the script tracks when price crosses it in subsequent bars. If the high and low of a bar span that line, the script marks it in red (both the line and its label). It also keeps a counter of how many times each line has been tested. This method goes beyond a simple visual approach by quantifying the retests. Because all these lines are created and managed in one place, you don’t have to manually label the lines or check them one by one. Advantages Over Manually Setting Multiple VWAPs You save screen space: Instead of layering several VWAP indicators, each with unique settings, this single script plots them all on one overlay. Automation: When a new anchor period begins, the script “closes out” the old VWAP and starts a new one. You never need to remember to reset it manually. Retest Visualization: The script not only draws each line but also changes color and updates the label automatically if a line gets tested. Doing this by hand would be labor-intensive. Unified Parameters: All settings (e.g., array size, max distance, test count limit) apply uniformly. You can manage them from one place, instead of configuring multiple separate tools. Extended Insight with Multiple VWAP Lines Since VWAP reflects the volume-weighted average price for each chosen period, historical lines can show zones where the market had a fair-value consensus in previous intervals. When the script preserves these lines, you see potential support/resistance areas more distinctly. If, for instance, price continually pivots around an old VWAP line, that may reveal a strong volume-based level. With several older VWAP lines on the chart, you gain an immediate sense of where these volume-derived averages have appeared and how price reacted over time. This wider perspective often proves more revealing than a single “current” VWAP line that does not reflect previous anchor sessions. Handling of Illiquid Markets and Volume Limitations VWAP is inherently tied to volume data, so its reliability decreases if volume reporting is missing or if the asset trades with very low liquidity. In such cases, a single large trade might momentarily skew the VWAP, resulting in “false” test signals when the high/low range intersects an abnormal price swing. If you suspect the data is incomplete or the market is unusually thin, it’s wise to confirm the validity of these VWAP lines before using them for any decision-making. Additionally, unusual market conditions—like after-hours trading or sudden high-volatility events—may cause VWAP to shift quickly, setting up multiple lines in a short time. Key User-Configurable Settings Hide VWAP on Day timeframe and above : Lets you disable the primary VWAP plot on daily or higher timeframes for a cleaner view. Anchor Period : Select from Session, Week, Month, Quarter, Year, Decade or Century. Controls how frequently the script resets and preserves the VWAP line. Offset : Moves the current VWAP line by a specified number of bars if you need a shifted perspective. Max Array Size : Caps how many past VWAP lines the script will remember. Prevents clutter if you’re charting very long histories. Max Distance : Defines how far back (in bar index units) a line is kept. If a line’s start bar is older than this threshold, it’s removed, keeping the chart uncluttered. Max Red Labels : Limits the number of tested (red) VWAP lines that appear. If price tests a large number of old lines, only the newest red labels remain once you hit the set limit. Workflow Overview As soon as a new anchor period begins (e.g., a new weekly candle if “Week” is chosen), the script ends the current VWAP and stores that final value in its internal arrays. It creates a dotted line and label representing the completed VWAP, and keeps track of whether it has been tested or not. Subsequent bars may then cross that line. If a bar’s high/low includes the line’s value, it’s flagged as tested, labeled red, and a test counter increases. As new anchored periods come, old lines remain visible—unless they fall outside your maxDistance or you exceed the maximum stored line count. Real-World Benefits Combining multiple VWAP lines—ranging, for example, from session-based lines for intraday perspectives to monthly or quarterly lines for broader context—provides a layered view of the volume-based fair price. This can help you quickly spot zones where price repeatedly intersects old VWAPs, potentially highlighting where bulls or bears took action historically. Because this script automates the management of all these lines and flags their retests, it removes a great deal of repetitive manual work that would typically accompany multiple, separate VWAP indicators set to different anchors. Limitations & Practical Use As with any volume-related tool, the script depends on reliable volume data. Assets trading on smaller venues or during illiquid periods may produce spurious signals. The script does not signal buy or sell decisions; rather, it helps visually map out where volume-weighted averages from previous periods might still be relevant to market behavior. Always combine the insight from these historical VWAP lines with your existing analytical approach or other technical and fundamental tools you use. Conclusion This script unifies past and present VWAP lines into one overlay, automatically detecting new anchor resets, storing the final VWAP values, and indicating whenever old lines are retested by price. It offers synergy through the simultaneous display of multiple historical VWAP lines, making it quicker and easier to detect potential support/resistance zones and better reflect changing market volumes over time. You no longer need to manually create, configure, or reset multiple VWAP indicators. Instead, the script handles all aspects of line creation, retest detection, and clutter management, giving you a robust framework to observe how historical VWAP data aligns with current price action. By understanding the significance of multiple anchored VWAP lines, you can assess market structure from multiple angles in a single view. As always, ensure you confirm the reliability of the volume data for your particular asset and use these lines in conjunction with other analyses to form a well-rounded perspective on current market behavior.
Pine Script® göstergesi
ZenAlgo_Official tarafından
1
imbalances bandsThis indicator is designed to identify imbalances based on the calculation of the average of the highest and lowest prices. It forms a kind of band indicating correction points. This indicator uses a total of 4 modified VWAPs, separated into 2 options that the user can activate or deactivate by checking or unchecking the options "Show imbalances bands VWAP 1" or "Show imbalances bands VWAP 2". Let's talk about the first option, "Show imbalances bands VWAP 1". This displays 2 modified VWAPs on the screen, one in green and one in red, forming a kind of band that indicates possible points of imbalance in the market, signaling increased volatility between buying and selling. When the price tests the bands, it can be useful as there is a probability of a correction in the movement. This can be particularly useful for those who trade using a scalping style, as it helps analyze when the price tests the bands. It can also be beneficial for trend traders because when the price tests one of the bands, there is a probability of a movement correction. Now let's talk about the option "Show imbalances bands VWAP 2". It contains two modified VWAPs, one in purple and one in blue, which also form a kind of band. These bands also indicate the probability of a movement correction. What is the difference between the Show imbalances bands VWAP 1 option and the Show imbalances bands VWAP 2 option? The option "Show imbalances bands VWAP 2" consists of 2 modified volume-weighted moving averages that have a calculation checking the increase in volatility between the highest and lowest prices. One modified moving average is in purple, and the other modified moving average is in blue, forming a kind of two modified VWAPs. The option "Show Imbalance Bands VWAP 1" consists of two modified moving averages using the absolute difference between the closing price and the moving average instead of the volume. This is particularly useful for assets where the volume is not a good indicator or is not available. The option "Show Imbalance Bands VWAP 1" also has a calculation that checks for increased volatility between the highest and lowest prices. It features two modified moving averages, one in green and one in red. This indicator can be adjusted according to the preferences and characteristics of the specific asset or market. It provides clear visual information and can be used as a complementary tool for technical analysis in trading strategies. and Interesting period 5,20,50,80,200 Interesting imbalance setting 2.4, 3.3 ,4.2 Analysis Ideas: If you are following a trend, you can use this indicator to analyze how the price behaves around the bands. Since the imbalance bands indicate a probability of correction, it can be useful for identifying protection points or moments to be cautious, as there might be a probability of increased volatility. Analysis Ideas2:For those trading using a scalping style, observe how the price behaves when it tests the imbalance band, as there may be a probability of increased volatility. Please note that this indicator is designed for educational and informational purposes. Always conduct your own analysis and consider risk management strategies before making trading decisions.
Pine Script® göstergesi
rafaelfutuoso tarafından
Spot Symbols for CryptoLibrary "CryptoSpotSymbols" This Library has one purpose only. It generate Symbols for the Crypto Spot Market, like all the currencies pairs of most Crypto Exchanges available to TradingView. Have a look at .find() , which is an all in one function. Binance(basecurrency)   Generate 27 Symbols for the Spot Market of Binance.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: BinanceUS(basecurrency)   Generate seven Symbols for the Spot Market of BinanceUS.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: Bitfinex(basecurrency)   Generate 12 Symbols for the Spot Market of Bitfinex.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: bitFlyer(basecurrency)   Generate three Symbols for the Spot Market of bitFlyer.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: Bitget(basecurrency)   Generate seven Symbols for the Spot Market of Bitget.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: Bithumb(basecurrency)   Generate two Symbols for the Spot Market of Bithumb.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: bitkub(basecurrency)   Generate one Symbol for the Spot Market of bitkub.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: THB BitMEX(basecurrency)   Generate two Symbols for the Spot Market of BitMEX.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: bitpanda_pro(basecurrency)   Generate six Symbols for the Spot Market of bitpanda pro.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: bitrue(basecurrency)   Generate nine Symbols for the Spot Market of bitrue.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: Bitstamp(basecurrency)   Generate eight Symbols for the Spot Market of Bitstamp.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: BITTREX(basecurrency)   Generate six Symbols for the Spot Market of BITTREX.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: BTSE(basecurrency)   Generate 15 Symbols for the Spot Market of BTSE.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: BYBIT(basecurrency)   Generate five Symbols for the Spot Market of BYBIT.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: CapitalCom(basecurrency)   Generate five Symbols for the Spot Market of capital.com.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: coinbase(basecurrency)   Generate seven Symbols for the Spot Market of coinbase.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: CoinEx(basecurrency)   Generate three Symbols for the Spot Market of CoinEx.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: CurrencyCom(basecurrency)   Generate 30 Symbols for the Spot Market of currency.com.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: Delta(basecurrency)   Generate one Symbol for the Spot Market of Delta.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: USDT Deribit(basecurrency)   Generate two Symbols for the Spot Market of Deribit.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: easyMarkets(basecurrency)   Generate one Symbol for the Spot Market of easyMarkets.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: USD Eightcap(basecurrency)   Generate one Symbol for the Spot Market of Eightcap.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: USD ExMo(basecurrency)   Generate ten Symbols for the Spot Market of ExMo.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: FOREXcom(basecurrency)   Generate four Symbols for the Spot Market of FOREX.com.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: FXCM(basecurrency)   Generate three Symbols for the Spot Market of FXCM.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: GateIO(basecurrency)   Generate five Symbols for the Spot Market of Gate.io.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: Gemini(basecurrency)   Generate ten Symbols for the Spot Market of Gemini.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: Kraken(basecurrency)   Generate 14 Symbols for the Spot Market of Kraken.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: KuCoin(basecurrency)   Generate 13 Symbols for the Spot Market of KuCoin.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: MEXC(basecurrency)   Generate six Symbols for the Spot Market of MEXC.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: OANDA(basecurrency)   Generate one Symbol for the Spot Market of OANDA.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: USD OKX(basecurrency)   Generate six Symbols for the Spot Market of OKX.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: Pepperstone(basecurrency)   Generate one Symbol for the Spot Market of Pepperstone.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: USD phemex(basecurrency)   Generate four Symbols for the Spot Market of phemex.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: POLONIEX(basecurrency)   Generate nine Symbols for the Spot Market of POLONIEX.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: Pyth(basecurrency)   Generate three Symbols for the Spot Market of Pyth.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: Skilling(basecurrency)   Generate four Symbols for the Spot Market of Skilling.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: TimeX(basecurrency)   Generate six Symbols for the Spot Market of TimeX.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: TradeStation(basecurrency)   Generate four Symbols for the Spot Market of TradeStation.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: UpBit(basecurrency)   Generate four Symbols for the Spot Market of UpBit.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: whitebit(basecurrency)   Generate 13 Symbols for the Spot Market of whitebit.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: WOOX(basecurrency)   Generate two Symbols for the Spot Market of WOO.   Parameters:      basecurrency (simple string) : Its the Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`.   Returns: find(exchange, basecurrency)   Generate up to 30 Symbols for the Spot Market, depending on the market picked.   Parameters:      exchange (simple string) : The name of an Exchange. Case insensitivity. Optional. Default value is `syminfo.prefix`. If something else is put in here it will return `na` values.      basecurrency (simple string) : The Basecurrency to generate the Symbols with. Optional. Default value is `syminfo.basecurrency`   Returns: 30x string as tuple
Pine Script® kütüphanesi
weak_hand tarafından
Upper Candle Trends [theEccentricTrader]█   OVERVIEW This indicator simply plots upper candle trends and should be used in conjunction with my Lower Candle Trends indicator as a visual aid to my Upper and Lower Candle Trend Counter indicator. █   CONCEPTS Green and Red Candles • A green candle is one that closes with a close price equal to or above the price it opened. • A red candle is one that closes with a close price that is lower than the price it opened. Upper Candle Trends • A higher high candle is one that closes with a higher high price than the high price of the preceding candle. • A lower high candle is one that closes with a lower high price than the high price of the preceding candle. • A double-top candle is one that closes with a high price that is equal to the high price of the preceding candle. Lower Candle Trends • A higher low candle is one that closes with a higher low price than the low price of the preceding candle. • A lower low candle is one that closes with a lower low price than the low price of the preceding candle. • A double-bottom candle is one that closes with a low price that is equal to the low price of the preceding candle. Muti-Part Upper and Lower Candle Trends • A multi-part higher high trend begins with the formation of a new higher high and continues until a new lower high ends the trend. • A multi-part lower high trend begins with the formation of a new lower high and continues until a new higher high ends the trend. • A multi-part higher low trend begins with the formation of a new higher low and continues until a new lower low ends the trend. • A multi-part lower low trend begins with the formation of a new lower low and continues until a new higher low ends the trend. █   FEATURES Plots Green up-arrows, with the number of the trend part, denote higher high trends. Red down-arrows, with the number of the trend part, denote lower high trends. █   LIMITATIONS Some higher timeframe candles on tickers with larger lookbacks such as the DXY , do not actually contain all the open, high, low and close (OHLC) data at the beginning of the chart. Instead, they use the close price for open, high and low prices. So, while we can determine whether the close price is higher or lower than the preceding close price, there is no way of knowing what actually happened intra-bar for these candles. And by default candles that close at the same price as the open price, will be counted as green.
Pine Script® göstergesi
theEccentricTrader tarafından
Lower Candle Trends [theEccentricTrader]█   OVERVIEW This indicator simply plots lower candle trends and should be used in conjunction with my Upper Candle Trends indicator as a visual aid to my Upper and Lower Candle Trend Counter indicator. █   CONCEPTS Green and Red Candles • A green candle is one that closes with a close price equal to or above the price it opened. • A red candle is one that closes with a close price that is lower than the price it opened. Upper Candle Trends • A higher high candle is one that closes with a higher high price than the high price of the preceding candle. • A lower high candle is one that closes with a lower high price than the high price of the preceding candle. • A double-top candle is one that closes with a high price that is equal to the high price of the preceding candle. Lower Candle Trends • A higher low candle is one that closes with a higher low price than the low price of the preceding candle. • A lower low candle is one that closes with a lower low price than the low price of the preceding candle. • A double-bottom candle is one that closes with a low price that is equal to the low price of the preceding candle. Muti-Part Upper and Lower Candle Trends • A multi-part higher high trend begins with the formation of a new higher high and continues until a new lower high ends the trend. • A multi-part lower high trend begins with the formation of a new lower high and continues until a new higher high ends the trend. • A multi-part higher low trend begins with the formation of a new higher low and continues until a new lower low ends the trend. • A multi-part lower low trend begins with the formation of a new lower low and continues until a new higher low ends the trend. █   FEATURES Plots Green up-arrows, with the number of the trend part, denote higher low trends. Red down-arrows, with the number of the trend part, denote lower low trends. █   LIMITATIONS Some higher timeframe candles on tickers with larger lookbacks such as the DXY , do not actually contain all the open, high, low and close (OHLC) data at the beginning of the chart. Instead, they use the close price for open, high and low prices. So, while we can determine whether the close price is higher or lower than the preceding close price, there is no way of knowing what actually happened intra-bar for these candles. And by default candles that close at the same price as the open price, will be counted as green.
Pine Script® göstergesi
theEccentricTrader tarafından
Variety MA Cluster Filter Crosses [Loxx]What is a Cluster Filter? One of the approaches to determining a useful signal (trend) in stream data. Small filtering (smoothing) tests applied to market quotes demonstrate the potential for creating non-lagging digital filters (indicators) that are not redrawn on the last bars. Standard Approach This approach is based on classical time series smoothing methods. There are lots of articles devoted to this subject both on this and other websites. The results are also classical: 1. The changes in trends are displayed with latency; 2. Better indicator (digital filter) response achieved at the expense of smoothing quality decrease; 3. Attempts to implement non-lagging indicators lead to redrawing on the last samples (bars). And whereas traders have learned to cope with these things using persistence of economic processes and other tricks, this would be unacceptable in evaluating real-time experimental data, e.g. when testing aerostructures. The Main Problem It is a known fact that the majority of trading systems stop performing with the course of time, and that the indicators are only indicative over certain intervals. This can easily be explained: market quotes are not stationary. The definition of a stationary process is available in Wikipedia: A stationary process is a stochastic process whose joint probability distribution does not change when shifted in time. Judging by this definition, methods of analysis of stationary time series are not applicable in technical analysis. And this is understandable. A skillful market-maker entering the market will mess up all the calculations we may have made prior to that with regard to parameters of a known series of market quotes. Even though this seems obvious, a lot of indicators are based on the theory of stationary time series analysis. Examples of such indicators are moving averages and their modifications. However, there are some attempts to create adaptive indicators. They are supposed to take into account non-stationarity of market quotes to some extent, yet they do not seem to work wonders. The attempts to "punish" the market-maker using the currently known methods of analysis of non-stationary series (wavelets, empirical modes and others) are not successful either. It looks like a certain key factor is constantly being ignored or unidentified. The main reason for this is that the methods used are not designed for working with stream data. All (or almost all) of them were developed for analysis of the already known or, speaking in terms of technical analysis, historical data. These methods are convenient, e.g., in geophysics: you feel the earthquake, get a seismogram and then analyze it for few months. In other words, these methods are appropriate where uncertainties arising at the ends of a time series in the course of filtering affect the end result. When analyzing experimental stream data or market quotes, we are focused on the most recent data received, rather than history. These are data that cannot be dealt with using classical algorithms. Cluster Filter Cluster filter is a set of digital filters approximating the initial sequence. Cluster filters should not be confused with cluster indicators. Cluster filters are convenient when analyzing non-stationary time series in real time, in other words, stream data. It means that these filters are of principal interest not for smoothing the already known time series values, but for getting the most probable smoothed values of the new data received in real time. Unlike various decomposition methods or simply filters of desired frequency, cluster filters create a composition or a fan of probable values of initial series which are further analyzed for approximation of the initial sequence. The input sequence acts more as a reference than the target of the analysis. The main analysis concerns values calculated by a set of filters after processing the data received. In the general case, every filter included in the cluster has its own individual characteristics and is not related to others in any way. These filters are sometimes customized for the analysis of a stationary time series of their own which describes individual properties of the initial non-stationary time series. In the simplest case, if the initial non-stationary series changes its parameters, the filters "switch" over. Thus, a cluster filter tracks real time changes in characteristics. Cluster Filter Design Procedure Any cluster filter can be designed in three steps: 1. The first step is usually the most difficult one but this is where probabilistic models of stream data received are formed. The number of these models can be arbitrary large. They are not always related to physical processes that affect the approximable data. The more precisely models describe the approximable sequence, the higher the probability to get a non-lagging cluster filter. 2. At the second step, one or more digital filters are created for each model. The most general condition for joining filters together in a cluster is that they belong to the models describing the approximable sequence. 3. So, we can have one or more filters in a cluster. Consequently, with each new sample we have the sample value and one or more filter values. Thus, with each sample we have a vector or artificial noise made up of several (minimum two) values. All we need to do now is to select the most appropriate value. An Example of a Simple Cluster Filter For illustration, we will implement a simple cluster filter corresponding to the above diagram, using market quotes as input sequence. You can simply use closing prices of any time frame. 1. Model description. We will proceed on the assumption that: The aproximate sequence is non-stationary, i.e. its characteristics tend to change with the course of time. The closing price of a bar is not the actual bar price. In other words, the registered closing price of a bar is one of the noise movements, like other price movements on that bar. The actual price or the actual value of the approximable sequence is between the closing price of the current bar and the closing price of the previous bar. The approximable sequence tends to maintain its direction. That is, if it was growing on the previous bar, it will tend to keep on growing on the current bar. 2. Selecting digital filters. For the sake of simplicity, we take two filters: The first filter will be a variety filter calculated based on the last closing prices using the slow period. I believe this fits well in the third assumption we specified for our model. Since we have a non-stationary filter, we will try to also use an additional filter that will hopefully facilitate to identify changes in characteristics of the time series. I've chosen a variety filter using the fast period. 3. Selecting the appropriate value for the cluster filter. So, with each new sample we will have the sample value (closing price), as well as the value of MA and fast filter. The closing price will be ignored according to the second assumption specified for our model. Further, we select the МА or ЕМА value based on the last assumption, i.e. maintaining trend direction: For an uptrend, i.e. CF(i-1)>CF(i-2), we select one of the following four variants: if CF(i-1)fastfilter(i), then CF(i)=slowfilter(i); if CF(i-1)>slowfilter(i) and CF(i-1)slowfilter(i) and CF(i-1)>fastfilter(i), then CF(i)=MAX(slowfilter(i),fastfilter(i)). For a downtrend, i.e. CF(i-1)slowfilter(i) and CF(i-1)>fastfilter(i), then CF(i)=MAX(slowfilter(i),fastfilter(i)); if CF(i-1)>slowfilter(i) and CF(i-1)fastfilter(i), then CF(i)=fastfilter(i); if CF(i-1)
Pine Script® göstergesi
loxx tarafından
1
Swing Counter [theEccentricTrader]█   OVERVIEW This indicator counts the number of confirmed swing high and swing low scenarios on any given candlestick chart and displays the statistics in a table, which can be repositioned and resized at the user's discretion. █   CONCEPTS Green and Red Candles • A green candle is one that closes with a high price equal to or above the price it opened. • A red candle is one that closes with a low price that is lower than the price it opened. Swing Highs and Swing Lows • A swing high is a green candle or series of consecutive green candles followed by a single red candle to complete the swing and form the peak. • A swing low is a red candle or series of consecutive red candles followed by a single green candle to complete the swing and form the trough. Peak and Trough Prices (Basic) • The peak price of a complete swing high is the high price of either the red candle that completes the swing high or the high price of the preceding green candle, depending on which is higher. • The trough price of a complete swing low is the low price of either the green candle that completes the swing low or the low price of the preceding red candle, depending on which is lower. Peak and Trough Prices (Advanced) • The advanced peak price of a complete swing high is the high price of either the red candle that completes the swing high or the high price of the highest preceding green candle high price, depending on which is higher. • The advanced trough price of a complete swing low is the low price of either the green candle that completes the swing low or the low price of the lowest preceding red candle low price, depending on which is lower. Green and Red Peaks and Troughs • A green peak is one that derives its price from the green candle/s that constitute the swing high. • A red peak is one that derives its price from the red candle that completes the swing high. • A green trough is one that derives its price from the green candle that completes the swing low. • A red trough is one that derives its price from the red candle/s that constitute the swing low. Historic Peaks and Troughs The current, or most recent, peak and trough occurrences are referred to as occurrence zero. Previous peak and trough occurrences are referred to as historic and ordered numerically from right to left, with the most recent historic peak and trough occurrences being occurrence one. Upper Trends • A return line uptrend is formed when the current peak price is higher than the preceding peak price. • A downtrend is formed when the current peak price is lower than the preceding peak price. • A double-top is formed when the current peak price is equal to the preceding peak price. Lower Trends • An uptrend is formed when the current trough price is higher than the preceding trough price. • A return line downtrend is formed when the current trough price is lower than the preceding trough price. • A double-bottom is formed when the current trough price is equal to the preceding trough price. █   FEATURES Inputs • Start Date • End Date • Position • Text Size • Show Sample Period • Show Plots • Show Lines Table The table is colour coded, consists of three columns and nine rows. Blue cells denote neutral scenarios, green cells denote return line uptrend and uptrend scenarios, and red cells denote downtrend and return line downtrend scenarios. The swing scenarios are listed in the first column with their corresponding total counts to the right, in the second column. The last row in column one, row nine, displays the sample period which can be adjusted or hidden via indicator settings. Rows three and four in the third column of the table display the total higher peaks and higher troughs as percentages of total peaks and troughs, respectively. Rows five and six in the third column display the total lower peaks and lower troughs as percentages of total peaks and troughs, respectively. And rows seven and eight display the total double-top peaks and double-bottom troughs as percentages of total peaks and troughs, respectively. Plots I have added plots as a visual aid to the swing scenarios listed in the table. Green up-arrows with ‘HP’ denote higher peaks, while green up-arrows with ‘HT’ denote higher troughs. Red down-arrows with ‘LP’ denote higher peaks, while red down-arrows with ‘LT’ denote lower troughs. Similarly, blue diamonds with ‘DT’ denote double-top peaks and blue diamonds with ‘DB’ denote double-bottom troughs. These plots can be hidden via indicator settings. Lines I have also added green and red trendlines as a further visual aid to the swing scenarios listed in the table. Green lines denote return line uptrends (higher peaks) and uptrends (higher troughs), while red lines denote downtrends (lower peaks) and return line downtrends (lower troughs). These lines can be hidden via indicator settings. █   HOW TO USE This indicator is intended for research purposes and strategy development. I hope it will be useful in helping to gain a better understanding of the underlying dynamics at play on any given market and timeframe. It can, for example, give you an idea of any inherent biases such as a greater proportion of higher peaks to lower peaks. Or a greater proportion of higher troughs to lower troughs. Such information can be very useful when conducting top down analysis across multiple timeframes, or considering entry and exit methods. What I find most fascinating about this logic, is that the number of swing highs and swing lows will always find equilibrium on each new complete wave cycle. If for example the chart begins with a swing high and ends with a swing low there will be an equal number of swing highs to swing lows. If the chart starts with a swing high and ends with a swing high there will be a difference of one between the two total values until another swing low is formed to complete the wave cycle sequence that began at start of the chart. Almost as if it was a fundamental truth of price action, although quite common sensical in many respects. As they say, what goes up must come down. The objective logic for swing highs and swing lows I hope will form somewhat of a foundational building block for traders, researchers and developers alike. Not only does it facilitate the objective study of swing highs and swing lows it also facilitates that of ranges, trends, double trends, multi-part trends and patterns. The logic can also be used for objective anchor points. Concepts I will introduce and develop further in future publications. █   LIMITATIONS Some higher timeframe candles on tickers with larger lookbacks such as the DXY , do not actually contain all the open, high, low and close (OHLC) data at the beginning of the chart. Instead, they use the close price for open, high and low prices. So, while we can determine whether the close price is higher or lower than the preceding close price, there is no way of knowing what actually happened intra-bar for these candles. And by default candles that close at the same price as the open price, will be counted as green. You can avoid this problem by utilising the sample period filter. The green and red candle calculations are based solely on differences between open and close prices, as such I have made no attempt to account for green candles that gap lower and close below the close price of the preceding candle, or red candles that gap higher and close above the close price of the preceding candle. I can only recommend using 24-hour markets, if and where possible, as there are far fewer gaps and, generally, more data to work with. Alternatively, you can replace the scenarios with your own logic to account for the gap anomalies, if you are feeling up to the challenge. The sample size will be limited to your Trading View subscription plan. Premium users get 20,000 candles worth of data, pro+ and pro users get 10,000, and basic users get 5,000. If upgrading is currently not an option, you can always keep a rolling tally of the statistics in an excel spreadsheet or something of the like. █   NOTES I feel it important to address the mention of advanced peak and trough price logic. While I have introduced the concept, I have not included the logic in my script for a number of reasons. The most pertinent of which being the amount of extra work I would have to do to include it in a public release versus the actual difference it would make to the statistics. Based on my experience, there are actually only a small number of cases where the advanced peak and trough prices are different from the basic peak and trough prices. And with adequate multi-timeframe analysis any high or low prices that are not captured using basic peak and trough price logic on any given time frame, will no doubt be captured on a higher timeframe. See the example below on the 1H FOREXCOM:USDJPY chart (Figure 1), where the basic peak price logic denoted by the indicator plot does not capture what would be the advanced peak price, but on the 2H FOREXCOM:USDJPY chart (Figure 2), the basic peak logic does capture the advanced peak price from the 1H timeframe. Figure 1. Figure 2. █   RAMBLINGS “Never was there an age that placed economic interests higher than does our own. Never was the need of a scientific foundation for economic affairs felt more generally or more acutely. And never was the ability of practical men to utilize the achievements of science, in all fields of human activity, greater than in our day. If practical men, therefore, rely wholly on their own experience, and disregard our science in its present state of development, it cannot be due to a lack of serious interest or ability on their part. Nor can their disregard be the result of a haughty rejection of the deeper insight a true science would give into the circumstances and relationships determining the outcome of their activity. The cause of such remarkable indifference must not be sought elsewhere than in the present state of our science itself, in the sterility of all past endeavours to find its empirical foundations.” (Menger, 1871, p.45). █   BIBLIOGRAPHY Menger, C. (1871) Principles of Economics. Reprint, Auburn, Alabama: Ludwig Von Mises Institute: 2007.
Pine Script® göstergesi
theEccentricTrader tarafından
[ChasinAlts] A New Beginning[MO]Hello Tradeurs, firstly let me say this… Please do not think that this dump is over (so I want to gift you one of the best gifts I CAN gift you at the PERFECT TIME...which is now) but I believe it to be the final one before a New Beginning is upon us. I hope that anybody that sees this within the next day or so listens to me when I tell you this… Follow the instructions below, IF ANYTHING, just to set the alert to be notify you so you can see why I’m about to tell you everything that I’m about to tell you. That being that this indicator is pure magic…..BUT you must stay in your lane when using it (ie. ultimately, understand its use case) and most importantly, how many people you expose it to. The good thing about it is it produces very few alerts. In fact, it was built SOLELY to find the very tips of MAJOR dumps/pumps (with its current default settings). I honestly cannot remember where I acquired the code so if anyone recognizes it please direct me to the source so I can give a shoutout. In the past it has been so astonishingly accurate that I didn’t want to publish it but I've just been...in the mood I suppose recently. Now…it is SPECIFICALLY meant for the 1min TF. I’ll say it again… It is meant for ONE MINUTE CHARTS…it was built for 1min charts, it will only work as well as I’m describing to you on the…you guessed it…ONE MINUTE CHART (again, with the default settings how they are, that is). If any of you use it for this present dump (November 8, 2022) and want to thank me for it or speak very highly about it or give it a bunch of likes… DO NOT!!! I will reword this so you fully comprehend my urgency on this matter. I do not want this indicator getting out for every Joe Schmoe (or stupid YouTuber) to use and spread because the manipulators will see to it that it will no longer work. Things that will happen that will cause it to gain the popularity that I do not want it to have are the following: 1) You "like" the indicator in TradingView to show appreciation/that your using it so that it will show up in your indicators list (to get past this you need to select all of the text of the script on the indicator's page and copy and paste it into the “Pine Editor”. Then select "save" and name it as you wish. Now, it is in your indicator list under the name that you saved it as. 2) You *favorite* the indicator in TradingView 3) You leave comments in the comments section on the indicators page in TradingView (I really do love hearing comments about anything regarding my indicators(positive or negative..though I haven't gotten any negative yet SO BRING IT ON), even though I don’t get too many of them, so if you are grateful (or hateful) PLEASE message me privately (and really I truly truly do appreciate getting comments/messages so if it has benefited you make sure to message me as I might have more for those that do express their gratitude) and tell me anything that you want to tell me or ask me anything that you wanna ask me there). One major thing that will help to suppress its popularity will be that if anybody goes back on historical charts to see its accuracy they most likely will not be able to go far back enough on the 1min TF to be able to Witness its efficacy so I'm banking on that helping to keep a lid on things. The settings used (as well as the TF used) really should not be changed if using it for its intended purpose. On little dumps that last for a few hours os so will produce points somewhere in the 40 to 60 range at the dumps/pumps peak. Each coin is worth one point and there are 40 coins per set and 2 sets (that you will have to link together) and when the under the hood indicator is triggered for that coin it will add a point to the score. With the settings how they are and on the 1min TF(if I hadn't mentioned it yet. lol) a good point alert threshold to use to catch the apex of heavy pumps/dumps would be between 70 to 80 points(80 is max). Ultimately is the users choice to input the alert threshold of points in the indicators settings(default is 72). If you’re trying to nail the very bottom of a hard pump/dump, DO NOT fall for times where it peaks at 50 to 60. You’re looking for 70 or above. *** This is the most important thing to do as you will not receive an alert if you do not do this correctly. You have to add the indicator two times to the chart. One of the indicators needs to be under “Coin Set 1“ and the other under “Coin Set 2“. Now, in “Set 1“ you need to go to the setting entitled “Select New Beginning Count Plot from drop-down“ and you need to open the drop-down and select the plot entitled “A New Beginning Count Plot”. This will link both the indicators and since there are 40 coins per iteration of the script, when you link them it could give you a max of 80 points total at the very peak of a very strong dump...which will obviously be rare. You CAN use only one copy of the script (but need to change the alert setting to a MAX of 40) but in my experience it's best to use both of them and to link them. It gives you a more well-rounded outcome. Good luck my people and always remember...Much love...Much Love. May the force be with your trades. -ChasinAlts out.
Pine Script® göstergesi
A_Traders_Edge tarafından
7
Oscillator Workbench — Chart [LucF]█  OVERVIEW This indicator uses an on-chart visual framework to help traders with the interpretation of any oscillator's behavior. The advantage of using this tool is that you do not need to know all the ins and outs of a particular oscillator such as RSI, CCI, Stochastic, etc. Your choice of oscillator and settings in this indicator will change its visuals, which allows you to evaluate different configurations in the context of how the workbench models oscillator behavior. My hope is that by using the workbench, you may come up with an oscillator selection and settings that produce visual cues you find useful in your trading. The workbench works on any symbol and timeframe. It uses the same presentation engine as my Delta ​Volume Channels indicator; those already familiar with it will feel right at home here. █  CONCEPTS Oscillators An oscillator is any signal that moves up and down a centerline. The centerline value is often zero or 50. Because the range of oscillator values is different than that of the symbol prices we look at on our charts, it is usually impossible to display an oscillator on the chart, so we typically put oscillators in a separate pane where they live in their own space. Each oscillator has its own profile and properties that dictate its behavior and interpretation. Oscillators can be bounded , meaning their values oscillate between fixed values such as 0 to 100 or +1 to -1, or unbounded when their maximum and minimum values are undefined. Oscillator weight How do you display an oscillator's value on a chart showing prices when both values are not on the same scale? The method I use here converts the oscillator's value into a percentage that is used to weigh a reference line. The weight of the oscillator is calculated by maintaining its highest and lowest value above and below its centerline since the beginning of the chart's history. The oscillator's relative position in either of those spaces is then converted to a percentage, yielding a positive or negative value depending on whether the oscillator is above or below its centerline. This method works equally well with bounded and unbounded oscillators. Oscillator Channel The ​​oscillator channel is the space between two moving averages: the reference line and a weighted version of that line. The reference line is a moving average of a type, source and length which you select. The weighted line uses the same settings, but it averages the oscillator-weighted price source. The weight applied to the source of the reference line can also include the relative size of the bar's ​volume in relation to previous bars. The effect of this is that the oscillator's weight on bars with higher total ​volume will carry greater weight than those with lesser ​volume. The ​oscillator channel can be in one of four states, each having its corresponding color:  • Bull (teal): The weighted line is above the reference line.  • Strong bull (lime): The bull condition is fulfilled and the bar's close is above the reference line and both the reference and the weighted lines are rising.  • Bear (maroon): The weighted line is below the reference line.  • Strong bear (pink): The bear condition is fulfilled and the bar's close is below the reference line and both the reference and the weighted lines are falling. Divergences In the context of this indicator, a divergence is any bar where the slope of the reference line does not match that of the weighted line. No directional bias is assigned to divergences when they occur. You can also choose to define divergences as differences in polarity between the oscillator's slope and the polarity of close-to-close values. This indicator's divergences are designed to identify transition levels. They have no polarity; their ​bullish/​bearish bias is determined by the behavior of price relative to the divergence channel after the divergence channel is built. Divergence Channel The divergence channel is the space between two levels (by default, the bar's low and high ) saved when divergences occur. When price has breached a channel and a new divergence occurs, a new channel is created. Until that new channel is breached, bars where additional divergences occur will expand the channel's levels if the bar's price points are outside the channel. Price breaches of the divergence channel will change its state. Divergence channels can be in one of five different states:  • Bull (teal): Price has breached the channel to the upside.  • Strong bull (lime): The bull condition is fulfilled and the ​oscillator channel is in the strong bull state.  • Bear (maroon): Price has breached the channel to the downside.  • Strong bear (pink): The bear condition is fulfilled and the ​oscillator channel is in the strong bear state.  • Neutral (gray): The channel has not been breached. █  HOW TO USE THE INDICATOR Load the indicator on an active chart (see here if you don't know how). The default configuration displays:  • The Divergence channel's levels.  • Bar colors using the state of the ​oscillator channel. The default settings use:  • RSI as the oscillator, using the close source and a length of 20 bars.  • An Arnaud-Legoux moving average on the close and a length of 20 bars as the reference line.  • The weighted version of the reference line uses only the oscillator's weight, i.e., without the relative ​volume's weight.   The weighted line is capped to three standard deviations of the reference.  • The divergence channel's levels are determined using the high and low of the bars where divergences occur.   Breaches of the channel require a bar's low to move above the top of the channel, and the bar's high to move below the channel's bottom. No markers appear on the chart; if you want to create alerts from this script, you will need first to define the conditions that will trigger the markers, then create the alert, which will trigger on those same conditions. To learn more about how to use this indicator, you must understand the concepts it uses and the information it displays, which requires reading this description. There are no videos to explain it. █  FEATURES The script's inputs are divided in five sections: "Oscillator", "Oscillator channel", "Divergence channel", "Bar Coloring" and "Marker/Alert Conditions". Oscillator This is where you configure the oscillator you want to study. Thirty oscillators are available to choose from, but you can also use an oscillator from another indicator that is on your chart, if you want. When you select an external indicator's plot as the oscillator, you must also specify the value of its centerline. Oscillator Channel Here, you control the visibility and colors of the reference line, its weighted version, and the oscillator channel between them. You also specify what type of moving average you want to use as a reference line, its source and its length. This acts as the ​oscillator channel's baseline. The weighted line is also a moving average of the same type and length as the reference line, except that it will be calculated from the weighted version of the source used in the reference line. By default, the weighted line is capped to three standard deviations of the reference line. You can change that value, and also elect to cap using a multiple of ATR instead. The cap provides a mechanism to control how far the weighted line swings from the reference line. This section is also where you can enable the relative ​volume component of the weight. Divergence Channel This is where you control the appearance of the divergence channel and the key price values used in determining the channel's levels and breaching conditions. These choices have an impact on the behavior of the channel. More generous level prices like the default low and high selection will produce more conservative channels, as will the default choice for breach prices. In this section, you can also enable a mode where an attempt is made to estimate the channel's bias before price breaches the channel. When it is enabled, successive increases/decreases of the channel's top and bottom levels are counted as new divergences occur. When one count is greater than the other, a bull/bear bias is inferred from it. You can also change the detection mode of divergences, and choose to display a mark above or below bars where divergences occur. Bar Coloring You specify here:  • The method used to color chart bars, if you choose to do so.  • If you want to hollow out the bodies of bars where ​volume has not increased since the last bar. Marker/Alert Conditions Here, you specify the conditions that will trigger up or down markers. The trigger conditions can include a combination of state transitions of the ​oscillator and the divergence channels. The triggering conditions can be filtered using a variety of conditions. Configuring the marker conditions is necessary before creating an alert from this script, as the alert will use the marker conditions to trigger. Realtime values will repaint, as is usually the case with oscillators, but markers only appear on bar closes, so they will not repaint. Keep in mind, when looking at markers on historical bars, that they are positioned on the bar when it closes — NOT when it opens. Raw values The raw values calculated by this script can be inspected using the Data Window, including the oscillator's value and the weights. █  INTERPRETATION Except when mentioned otherwise, this section's charts use the indicator's default settings, with different visual components turned on or off. The aim of the ​ oscillator channel is to provide a visual representation of an oscillator's general behavior. The simplest characteristic of the channel is its bull/bear state, determined by whether the weighted line is above or below the reference line. One can then distinguish between its bull and strong bull states, as transitions from strong bull to bull states will generally happen when trends are losing steam. While one should not infer a reversal from such transitions, they can be a good place to tighten stops. Only time will tell if a reversal will occur. One or more divergences will often occur before reversals. This shows the oscillator channel, with the reference line and the thicker, weighted line: The nature of the divergence channel 's design makes it particularly adept at identifying consolidation areas if its settings are kept on the conservative side. The divergence channel will also reveal transition areas. A gray divergence channel should usually be considered a no-trade zone. More adventurous traders can use the oscillator ​ channel to orient their trade entries if they accept the risk of trading in a neutral divergence channel, which by definition will not have been breached by price. This show only the divergence channels: This chart shows divergence channels and their levels, and colors bars on divergences and on the state of the oscillator channel, which is not visible on the chart: If your charts are already busy with other stuff you want to hold on to, you could consider using only the chart bar coloring component of this indicator. Here we only color bars using the combined state of the oscillator and divergence channel, and we do not color the bodies of bars where ​volume has not increased. Note that my chart's settings do not color the candle bodies: At its simplest, one way to use this indicator would be to look for overlaps of the strong bull/bear colors in both the ​oscillator channel and a divergence channel, as these identify points where price is breaching the divergence channel when the oscillator's state is consistent with the direction of the breach. Tip One way to use the Workbench is to combine it with my Delta ​Volume Channels indicator. If both indicators use the same MA as a reference line, you can display its delta ​volume channel instead of the oscillator channel. This chart shows such a setup. The Workbench displays its divergence levels, the weighted reference line using the default ​RSI oscillator, and colors bars on divergences. The ​DV Channels indicator only displays its delta ​volume channel, which uses the same MA as the workbench for its baseline. This way you can ascertain the ​volume delta situation in contrast with the visuals of the Workbench: █  LIMITATIONS  • For some of the oscillators, assumptions are made concerning their different parameters when they are more complex than just a source and length.   See the `oscCalc()` function in this indicator's code for all the details, and ask me in a comment if you can't find the information you need.  • When an oscillator using ​volume is selected and no ​volume information is available for the chart's symbol, an error will occur.  • The method I use to convert an oscillator's value into a percentage is fragile in the early history of datasets   because of the nascent expression of the oscillator's range during those early bars. █  NOTES Working with this workbench This indicator is called a workbench for a reason; it is designed for traders interested in exploring its behavior with different oscillators and settings, in the hope they can come up with a setup that suits their trading methodology. I cannot tell you which setup is the best because its setup should be compatible with your trading methodology, which may require faster or slower transitions, thus different configurations of the settings affecting the calculations of the divergence channels. For Pine Script™ Coders  • This script uses the new overload of the fill() function which now makes it possible to do vertical gradients in Pine. I use it for both channels displayed by this script.  • I use the new arguments for plot() 's `display` parameter to control where the script plots some of its values,   namely those I only want to appear in the script's status line and in the Data Window.  • I used my ta library for some of the oscillator calculations and helper functions.  • I also used TradingView's ta library for other oscillator calculations.  • I wrote my script using the revised recommendations in the Style Guide from the Pine v5 User Manual.
Pine Script® göstergesi
LucF tarafından
86
Fourier Extrapolator of 'Caterpillar' SSA of Price [Loxx]Fourier Extrapolator of 'Caterpillar' SSA of Price is a forecasting indicator that applies Singular Spectrum Analysis to input price and then injects that transformed value into the Quinn-Fernandes Fourier Transform algorithm to generate a price forecast. The indicator plots two curves: the green/red curve indicates modeled past values and the yellow/fuchsia dotted curve indicates the future extrapolated values. What is the Fourier Transform Extrapolator of price? Fourier Extrapolator of Price is a multi-harmonic (or multi-tone) trigonometric model of a price series xi, i=1..n, is given by: xi = m + Sum( a*Cos(w*i) + b*Sin(w*i), h=1..H ) Where: xi - past price at i-th bar, total n past prices; m - bias; a and b - scaling coefficients of harmonics; w - frequency of a harmonic ; h - harmonic number; H - total number of fitted harmonics. Fitting this model means finding m, a, b, and w that make the modeled values to be close to real values. Finding the harmonic frequencies w is the most difficult part of fitting a trigonometric model. In the case of a Fourier series, these frequencies are set at 2*pi*h/n. But, the Fourier series extrapolation means simply repeating the n past prices into the future. Quinn-Fernandes algorithm find sthe harmonic frequencies. It fits harmonics of the trigonometric series one by one until the specified total number of harmonics H is reached. After fitting a new harmonic , the coded algorithm computes the residue between the updated model and the real values and fits a new harmonic to the residue. see here: A Fast Efficient Technique for the Estimation of Frequency , B. G. Quinn and J. M. Fernandes, Biometrika, Vol. 78, No. 3 (Sep., 1991), pp . 489-497 (9 pages) Published By: Oxford University Press Fourier Transform Extrapolator of Price inputs are as follows: npast - number of past bars, to which trigonometric series is fitted; nharm - total number of harmonics in model; frqtol - tolerance of frequency calculations. What is Singular Spectrum Analysis ( SSA )? Singular spectrum analysis ( SSA ) is a technique of time series analysis and forecasting. It combines elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing. SSA aims at decomposing the original series into a sum of a small number of interpretable components such as a slowly varying trend, oscillatory components and a ‘structureless’ noise. It is based on the singular value decomposition ( SVD ) of a specific matrix constructed upon the time series. Neither a parametric model nor stationarity-type conditions have to be assumed for the time series. This makes SSA a model-free method and hence enables SSA to have a very wide range of applicability. For our purposes here, we are only concerned with the "Caterpillar" SSA . This methodology was developed in the former Soviet Union independently (the ‘iron curtain effect’) of the mainstream SSA . The main difference between the main-stream SSA and the "Caterpillar" SSA is not in the algorithmic details but rather in the assumptions and in the emphasis in the study of SSA properties. To apply the mainstream SSA , one often needs to assume some kind of stationarity of the time series and think in terms of the "signal plus noise" model (where the noise is often assumed to be ‘red’). In the "Caterpillar" SSA , the main methodological stress is on separability (of one component of the series from another one) and neither the assumption of stationarity nor the model in the form "signal plus noise" are required. "Caterpillar" SSA The basic "Caterpillar" SSA algorithm for analyzing one-dimensional time series consists of: Transformation of the one-dimensional time series to the trajectory matrix by means of a delay procedure (this gives the name to the whole technique); Singular Value Decomposition of the trajectory matrix; Reconstruction of the original time series based on a number of selected eigenvectors. This decomposition initializes forecasting procedures for both the original time series and its components. The method can be naturally extended to multidimensional time series and to image processing. The method is a powerful and useful tool of time series analysis in meteorology, hydrology, geophysics, climatology and, according to our experience, in economics, biology, physics, medicine and other sciences; that is, where short and long, one-dimensional and multidimensional, stationary and non-stationary, almost deterministic and noisy time series are to be analyzed. "Caterpillar" SSA inputs are as follows: lag - How much lag to introduce into the SSA algorithm, the higher this number the slower the process and smoother the signal ncomp - Number of Computations or cycles of of the SSA algorithm; the higher the slower ssapernorm - SSA Period Normalization numbars =- number of past bars, to which SSA is fitted Included: Bar coloring Alerts Signals Loxx's Expanded Source Types Related Fourier Transform Indicators Real-Fast Fourier Transform of Price w/ Linear Regression Fourier Extrapolator of Variety RSI w/ Bollinger Bands Fourier Extrapolator of Price w/ Projection Forecast Related Projection Forecast Indicators Itakura-Saito Autoregressive Extrapolation of Price Helme-Nikias Weighted Burg AR-SE Extra. of Price Related SSA Indicators End-pointed SSA of FDASMA End-pointed SSA of Williams %R
Pine Script® göstergesi
loxx tarafından
2
VHF-Adaptive T3 iTrend [Loxx]VHF-Adaptive T3 iTrend is an iTrend indicator with T3 smoothing and Vertical Horizontal Filter Adaptive period input. iTrend is used to determine where the trend starts and ends. You'll notice that the noise filter on this one is extreme. Adjust the period inputs accordingly to suit your take and your backtest requirements. This is also useful for scalping lower timeframes. Enjoy! What is VHF Adaptive Period? Vertical Horizontal Filter (VHF) was created by Adam White to identify trending and ranging markets. VHF measures the level of trend activity, similar to ADX DI. Vertical Horizontal Filter does not, itself, generate trading signals, but determines whether signals are taken from trend or momentum indicators. Using this trend information, one is then able to derive an average cycle length. What is the T3 moving average? Better Moving Averages Tim Tillson November 1, 1998 Tim Tillson is a software project manager at Hewlett-Packard, with degrees in Mathematics and Computer Science. He has privately traded options and equities for 15 years. Introduction "Digital filtering includes the process of smoothing, predicting, differentiating, integrating, separation of signals, and removal of noise from a signal. Thus many people who do such things are actually using digital filters without realizing that they are; being unacquainted with the theory, they neither understand what they have done nor the possibilities of what they might have done." This quote from R. W. Hamming applies to the vast majority of indicators in technical analysis . Moving averages, be they simple, weighted, or exponential, are lowpass filters; low frequency components in the signal pass through with little attenuation, while high frequencies are severely reduced. "Oscillator" type indicators (such as MACD , Momentum, Relative Strength Index ) are another type of digital filter called a differentiator. Tushar Chande has observed that many popular oscillators are highly correlated, which is sensible because they are trying to measure the rate of change of the underlying time series, i.e., are trying to be the first and second derivatives we all learned about in Calculus. We use moving averages (lowpass filters) in technical analysis to remove the random noise from a time series, to discern the underlying trend or to determine prices at which we will take action. A perfect moving average would have two attributes: It would be smooth, not sensitive to random noise in the underlying time series. Another way of saying this is that its derivative would not spuriously alternate between positive and negative values. It would not lag behind the time series it is computed from. Lag, of course, produces late buy or sell signals that kill profits. The only way one can compute a perfect moving average is to have knowledge of the future, and if we had that, we would buy one lottery ticket a week rather than trade! Having said this, we can still improve on the conventional simple, weighted, or exponential moving averages. Here's how: Two Interesting Moving Averages We will examine two benchmark moving averages based on Linear Regression analysis. In both cases, a Linear Regression line of length n is fitted to price data. I call the first moving average ILRS, which stands for Integral of Linear Regression Slope. One simply integrates the slope of a linear regression line as it is successively fitted in a moving window of length n across the data, with the constant of integration being a simple moving average of the first n points. Put another way, the derivative of ILRS is the linear regression slope. Note that ILRS is not the same as a SMA ( simple moving average ) of length n, which is actually the midpoint of the linear regression line as it moves across the data. We can measure the lag of moving averages with respect to a linear trend by computing how they behave when the input is a line with unit slope. Both SMA (n) and ILRS(n) have lag of n/2, but ILRS is much smoother than SMA . Our second benchmark moving average is well known, called EPMA or End Point Moving Average. It is the endpoint of the linear regression line of length n as it is fitted across the data. EPMA hugs the data more closely than a simple or exponential moving average of the same length. The price we pay for this is that it is much noisier (less smooth) than ILRS, and it also has the annoying property that it overshoots the data when linear trends are present. However, EPMA has a lag of 0 with respect to linear input! This makes sense because a linear regression line will fit linear input perfectly, and the endpoint of the LR line will be on the input line. These two moving averages frame the tradeoffs that we are facing. On one extreme we have ILRS, which is very smooth and has considerable phase lag. EPMA has 0 phase lag, but is too noisy and overshoots. We would like to construct a better moving average which is as smooth as ILRS, but runs closer to where EPMA lies, without the overshoot. A easy way to attempt this is to split the difference, i.e. use (ILRS(n)+EPMA(n))/2. This will give us a moving average (call it IE /2) which runs in between the two, has phase lag of n/4 but still inherits considerable noise from EPMA. IE /2 is inspirational, however. Can we build something that is comparable, but smoother? Figure 1 shows ILRS, EPMA, and IE /2. Filter Techniques Any thoughtful student of filter theory (or resolute experimenter) will have noticed that you can improve the smoothness of a filter by running it through itself multiple times, at the cost of increasing phase lag. There is a complementary technique (called twicing by J.W. Tukey) which can be used to improve phase lag. If L stands for the operation of running data through a low pass filter, then twicing can be described by: L' = L(time series) + L(time series - L(time series)) That is, we add a moving average of the difference between the input and the moving average to the moving average. This is algebraically equivalent to: 2L-L(L) This is the Double Exponential Moving Average or DEMA , popularized by Patrick Mulloy in TASAC (January/February 1994). In our taxonomy, DEMA has some phase lag (although it exponentially approaches 0) and is somewhat noisy, comparable to IE /2 indicator. We will use these two techniques to construct our better moving average, after we explore the first one a little more closely. Fixing Overshoot An n-day EMA has smoothing constant alpha=2/(n+1) and a lag of (n-1)/2. Thus EMA (3) has lag 1, and EMA (11) has lag 5. Figure 2 shows that, if I am willing to incur 5 days of lag, I get a smoother moving average if I run EMA (3) through itself 5 times than if I just take EMA (11) once. This suggests that if EPMA and DEMA have 0 or low lag, why not run fast versions (eg DEMA (3)) through themselves many times to achieve a smooth result? The problem is that multiple runs though these filters increase their tendency to overshoot the data, giving an unusable result. This is because the amplitude response of DEMA and EPMA is greater than 1 at certain frequencies, giving a gain of much greater than 1 at these frequencies when run though themselves multiple times. Figure 3 shows DEMA (7) and EPMA(7) run through themselves 3 times. DEMA^3 has serious overshoot, and EPMA^3 is terrible. The solution to the overshoot problem is to recall what we are doing with twicing: DEMA (n) = EMA (n) + EMA (time series - EMA (n)) The second term is adding, in effect, a smooth version of the derivative to the EMA to achieve DEMA . The derivative term determines how hot the moving average's response to linear trends will be. We need to simply turn down the volume to achieve our basic building block: EMA (n) + EMA (time series - EMA (n))*.7; This is algebraically the same as: EMA (n)*1.7-EMA( EMA (n))*.7; I have chosen .7 as my volume factor, but the general formula (which I call "Generalized Dema") is: GD (n,v) = EMA (n)*(1+v)-EMA( EMA (n))*v, Where v ranges between 0 and 1. When v=0, GD is just an EMA , and when v=1, GD is DEMA . In between, GD is a cooler DEMA . By using a value for v less than 1 (I like .7), we cure the multiple DEMA overshoot problem, at the cost of accepting some additional phase delay. Now we can run GD through itself multiple times to define a new, smoother moving average T3 that does not overshoot the data: T3(n) = GD ( GD ( GD (n))) In filter theory parlance, T3 is a six-pole non-linear Kalman filter. Kalman filters are ones which use the error (in this case (time series - EMA (n)) to correct themselves. In Technical Analysis , these are called Adaptive Moving Averages; they track the time series more aggressively when it is making large moves. Included Bar coloring Alerts Signals Loxx's Expanded Source Types
Pine Script® göstergesi
loxx tarafından
2
CFB-Adaptive CCI w/ T3 Smoothing [Loxx]CFB-Adaptive CCI w/ T3 Smoothing is a CCI indicator with adaptive period inputs and T3 smoothing. Jurik's Composite Fractal Behavior is used to created dynamic period input. What is Composite Fractal Behavior ( CFB )? All around you mechanisms adjust themselves to their environment. From simple thermostats that react to air temperature to computer chips in modern cars that respond to changes in engine temperature, r.p.m.'s, torque, and throttle position. It was only a matter of time before fast desktop computers applied the mathematics of self-adjustment to systems that trade the financial markets. Unlike basic systems with fixed formulas, an adaptive system adjusts its own equations. For example, start with a basic channel breakout system that uses the highest closing price of the last N bars as a threshold for detecting breakouts on the up side. An adaptive and improved version of this system would adjust N according to market conditions, such as momentum, price volatility or acceleration. Since many systems are based directly or indirectly on cycles, another useful measure of market condition is the periodic length of a price chart's dominant cycle, (DC), that cycle with the greatest influence on price action. The utility of this new DC measure was noted by author Murray Ruggiero in the January '96 issue of Futures Magazine. In it. Mr. Ruggiero used it to adaptive adjust the value of N in a channel breakout system. He then simulated trading 15 years of D-Mark futures in order to compare its performance to a similar system that had a fixed optimal value of N. The adaptive version produced 20% more profit! This DC index utilized the popular MESA algorithm (a formulation by John Ehlers adapted from Burg's maximum entropy algorithm, MEM). Unfortunately, the DC approach is problematic when the market has no real dominant cycle momentum, because the mathematics will produce a value whether or not one actually exists! Therefore, we developed a proprietary indicator that does not presuppose the presence of market cycles. It's called CFB (Composite Fractal Behavior) and it works well whether or not the market is cyclic. CFB examines price action for a particular fractal pattern, categorizes them by size, and then outputs a composite fractal size index. This index is smooth, timely and accurate Essentially, CFB reveals the length of the market's trending action time frame. Long trending activity produces a large CFB index and short choppy action produces a small index value. Investors have found many applications for CFB which involve scaling other existing technical indicators adaptively, on a bar-to-bar basis. What is Jurik Volty used in the Juirk Filter? One of the lesser known qualities of Juirk smoothing is that the Jurik smoothing process is adaptive. "Jurik Volty" (a sort of market volatility ) is what makes Jurik smoothing adaptive. The Jurik Volty calculation can be used as both a standalone indicator and to smooth other indicators that you wish to make adaptive. What is the Jurik Moving Average? Have you noticed how moving averages add some lag (delay) to your signals? ... especially when price gaps up or down in a big move, and you are waiting for your moving average to catch up? Wait no more! JMA eliminates this problem forever and gives you the best of both worlds: low lag and smooth lines. Ideally, you would like a filtered signal to be both smooth and lag-free. Lag causes delays in your trades, and increasing lag in your indicators typically result in lower profits. In other words, late comers get what's left on the table after the feast has already begun. What is the T3 moving average? Better Moving Averages Tim Tillson November 1, 1998 Tim Tillson is a software project manager at Hewlett-Packard, with degrees in Mathematics and Computer Science. He has privately traded options and equities for 15 years. Introduction "Digital filtering includes the process of smoothing, predicting, differentiating, integrating, separation of signals, and removal of noise from a signal. Thus many people who do such things are actually using digital filters without realizing that they are; being unacquainted with the theory, they neither understand what they have done nor the possibilities of what they might have done." This quote from R. W. Hamming applies to the vast majority of indicators in technical analysis . Moving averages, be they simple, weighted, or exponential, are lowpass filters; low frequency components in the signal pass through with little attenuation, while high frequencies are severely reduced. "Oscillator" type indicators (such as MACD , Momentum, Relative Strength Index ) are another type of digital filter called a differentiator. Tushar Chande has observed that many popular oscillators are highly correlated, which is sensible because they are trying to measure the rate of change of the underlying time series, i.e., are trying to be the first and second derivatives we all learned about in Calculus. We use moving averages (lowpass filters) in technical analysis to remove the random noise from a time series, to discern the underlying trend or to determine prices at which we will take action. A perfect moving average would have two attributes: It would be smooth, not sensitive to random noise in the underlying time series. Another way of saying this is that its derivative would not spuriously alternate between positive and negative values. It would not lag behind the time series it is computed from. Lag, of course, produces late buy or sell signals that kill profits. The only way one can compute a perfect moving average is to have knowledge of the future, and if we had that, we would buy one lottery ticket a week rather than trade! Having said this, we can still improve on the conventional simple, weighted, or exponential moving averages. Here's how: Two Interesting Moving Averages We will examine two benchmark moving averages based on Linear Regression analysis. In both cases, a Linear Regression line of length n is fitted to price data. I call the first moving average ILRS, which stands for Integral of Linear Regression Slope. One simply integrates the slope of a linear regression line as it is successively fitted in a moving window of length n across the data, with the constant of integration being a simple moving average of the first n points. Put another way, the derivative of ILRS is the linear regression slope. Note that ILRS is not the same as a SMA ( simple moving average ) of length n, which is actually the midpoint of the linear regression line as it moves across the data. We can measure the lag of moving averages with respect to a linear trend by computing how they behave when the input is a line with unit slope. Both SMA (n) and ILRS(n) have lag of n/2, but ILRS is much smoother than SMA . Our second benchmark moving average is well known, called EPMA or End Point Moving Average. It is the endpoint of the linear regression line of length n as it is fitted across the data. EPMA hugs the data more closely than a simple or exponential moving average of the same length. The price we pay for this is that it is much noisier (less smooth) than ILRS, and it also has the annoying property that it overshoots the data when linear trends are present. However, EPMA has a lag of 0 with respect to linear input! This makes sense because a linear regression line will fit linear input perfectly, and the endpoint of the LR line will be on the input line. These two moving averages frame the tradeoffs that we are facing. On one extreme we have ILRS, which is very smooth and has considerable phase lag. EPMA has 0 phase lag, but is too noisy and overshoots. We would like to construct a better moving average which is as smooth as ILRS, but runs closer to where EPMA lies, without the overshoot. A easy way to attempt this is to split the difference, i.e. use (ILRS(n)+EPMA(n))/2. This will give us a moving average (call it IE /2) which runs in between the two, has phase lag of n/4 but still inherits considerable noise from EPMA. IE /2 is inspirational, however. Can we build something that is comparable, but smoother? Figure 1 shows ILRS, EPMA, and IE /2. Filter Techniques Any thoughtful student of filter theory (or resolute experimenter) will have noticed that you can improve the smoothness of a filter by running it through itself multiple times, at the cost of increasing phase lag. There is a complementary technique (called twicing by J.W. Tukey) which can be used to improve phase lag. If L stands for the operation of running data through a low pass filter, then twicing can be described by: L' = L(time series) + L(time series - L(time series)) That is, we add a moving average of the difference between the input and the moving average to the moving average. This is algebraically equivalent to: 2L-L(L) This is the Double Exponential Moving Average or DEMA , popularized by Patrick Mulloy in TASAC (January/February 1994). In our taxonomy, DEMA has some phase lag (although it exponentially approaches 0) and is somewhat noisy, comparable to IE /2 indicator. We will use these two techniques to construct our better moving average, after we explore the first one a little more closely. Fixing Overshoot An n-day EMA has smoothing constant alpha=2/(n+1) and a lag of (n-1)/2. Thus EMA (3) has lag 1, and EMA (11) has lag 5. Figure 2 shows that, if I am willing to incur 5 days of lag, I get a smoother moving average if I run EMA (3) through itself 5 times than if I just take EMA (11) once. This suggests that if EPMA and DEMA have 0 or low lag, why not run fast versions (eg DEMA (3)) through themselves many times to achieve a smooth result? The problem is that multiple runs though these filters increase their tendency to overshoot the data, giving an unusable result. This is because the amplitude response of DEMA and EPMA is greater than 1 at certain frequencies, giving a gain of much greater than 1 at these frequencies when run though themselves multiple times. Figure 3 shows DEMA (7) and EPMA(7) run through themselves 3 times. DEMA^3 has serious overshoot, and EPMA^3 is terrible. The solution to the overshoot problem is to recall what we are doing with twicing: DEMA (n) = EMA (n) + EMA (time series - EMA (n)) The second term is adding, in effect, a smooth version of the derivative to the EMA to achieve DEMA . The derivative term determines how hot the moving average's response to linear trends will be. We need to simply turn down the volume to achieve our basic building block: EMA (n) + EMA (time series - EMA (n))*.7; This is algebraically the same as: EMA (n)*1.7-EMA( EMA (n))*.7; I have chosen .7 as my volume factor, but the general formula (which I call "Generalized Dema") is: GD (n,v) = EMA (n)*(1+v)-EMA( EMA (n))*v, Where v ranges between 0 and 1. When v=0, GD is just an EMA , and when v=1, GD is DEMA . In between, GD is a cooler DEMA . By using a value for v less than 1 (I like .7), we cure the multiple DEMA overshoot problem, at the cost of accepting some additional phase delay. Now we can run GD through itself multiple times to define a new, smoother moving average T3 that does not overshoot the data: T3(n) = GD ( GD ( GD (n))) In filter theory parlance, T3 is a six-pole non-linear Kalman filter. Kalman filters are ones which use the error (in this case (time series - EMA (n)) to correct themselves. In Technical Analysis , these are called Adaptive Moving Averages; they track the time series more aggressively when it is making large moves. Included: Bar coloring Signals Alerts
Pine Script® göstergesi
loxx tarafından
Pips-Stepped, OMA-Filtered, Ocean NMA [Loxx]Pips-Stepped, OMA-Filtered, Ocean NMA is an Ocean Natural Moving Average Filter that is pre-filtered using One More Moving Average (OMA) and then post-filtered using stepping by pips. This indicator is quadruple adaptive depending on the settings used: OMA adaptive Hiekin-Ashi Better Source Input Adaptive (w/ AMA of Kaufman smoothing) Ocean NMA adaptive Pips adaptive What is the One More Moving Average (OMA)? The usual story goes something like this : which is the best moving average? Everyone that ever started to do any kind of technical analysis was pulled into this "game". Comparing, testing, looking for new ones, testing ... The idea of this one is simple: it should not be itself, but it should be a kind of a chameleon - it should "imitate" as much other moving averages as it can. So the need for zillion different moving averages would diminish. And it should have some extra, of course: The extras: it has to be smooth it has to be able to "change speed" without length change it has to be able to adapt or not (since it has to "imitate" the non-adaptive as well as the adaptive ones) The steps: Smoothing - compared are the simple moving average (that is the basis and the first step of this indicator - a smoothed simple moving average with as little lag added as it is possible and as close to the original as it is possible) Speed 1 and non-adaptive are the reference for this basic setup. Speed changing - same chart only added one more average with "speeds" 2 and 3 (for comparison purposes only here) Finally - adapting : same chart with SMA compared to one more average with speed 1 but adaptive (so this parameters would make it a "smoothed adaptive simple average") Adapting part is a modified Kaufman adapting way and this part (the adapting part) may be a subject for changes in the future (it is giving satisfactory results, but if or when I find a better way, it will be implemented here) Some comparisons for different speed settings (all the comparisons are without adaptive turned on, and are approximate. Approximation comes from a fact that it is impossible to get exactly the same values from only one way of calculation, and frankly, I even did not try to get those same values). speed 0.5 - T3 (0.618 Tilson) speed 2.5 - T3 (0.618 Fulks/Matulich) speed 1 - SMA , harmonic mean speed 2 - LWMA speed 7 - very similar to Hull and TEMA speed 8 - very similar to LSMA and Linear regression value Parameters: Length - length (period) for averaging Source - price to use for averaging Speed - desired speed (i limited to -1.5 on the lower side but it even does not need that limit - some interesting results with speeds that are less than 0 can be achieved) Adaptive - does it adapt or not What is the Ocean Natural Moving Average? Created by Jim Sloman, the NMA is a moving average that automatically adjusts to volatility without being programed to do so. For more info, read his guide "Ocean Theory, an Introduction" What's the difference between this indicator and Sloan's original NMA? Sloman's original calculation uses the natural log of price as input into the NMA , here we use moving averages of price as the input for NMA . As such, this indicator applies a certain level of Ocean theory adaptivity to moving average filter used. Included: Bar coloring Alerts Expanded source types Signals Flat-level coloring for scalping
Pine Script® göstergesi
loxx tarafından
1
(JS) Checklist SignalsWhat if I told you that you could use over 10 indicators at once without having a single one of them on you chart? Enter the Checklist Signals. This is probably the most complex yet simple indicator I've ever done. What you get is 6 rows (if you want them all) of labels that hover at the top of your screen with a ton of extremely useful information. I will go down the list of options in the indicator settings and explain how it all works. So the label placement is based on ATR. You choose your X Axis and Y Axis starting point then adjust the lookback period. Default lookback is 600 bars. What that means is, the indicator finds the highest high in the last 600 bars, then begins to place the labels above that zone based on the ATR of the chart. Different timeframes require very different combinations so it's all customizable. Sometimes if labels overlap you need to adjust the X Axis starting point, or the spread on either axis. The next set of options allows you to decide what you'd prefer to be set on or off. Let's start with ATR and VWAP. I have added bands for both of these. When price is below the mean (which is the 21 ema by default), then the labels show you the next 5 standard deviations of ATR going down. When under one of these levels the label turns red. The opposite is true when above the mean and in those instances the labels will be green. It is the same with the VWAP, though instead of using the mean we use the daily VWAP as the starting point. If you choose to have levels switched on then you can see the actual values of each standard deviation level. Down lower in the options you can change the resolution and source used for VWAP. The next option is "Trending". This creates a moving average using the length of the Trending Lookback Period (default is 5) and then tells you using arrows in the label which direction the trend of the indicator is going. The next area let's you specify the information you receive in the Squeeze labels. By default all options are one - and this tells you if there's a Squeeze, what type of Squeeze there is, and how many bars the Squeeze has been building up or since it fired. These labels are color coded to correspond with the Squeeze type as well. Then we get to another one of my indicators, the Ballista. One of the main signals is the "Inverted Squeeze" where the short term momentum inverts against the long term momentum. Here I have the distance between the two oscillators in the first label, and then the second label tells you if there's an Inverted Squeeze signal, if there's potential entry, confirmed entry, or how many bars its been since the last entry signal. The next feature is off by default, but it will add arrows to your chart based on a simple lower highs and higher lows signals. Turning arrows on will place them right on your chart above or below each bar. The rest of it is customizable settings of all the other indicators that are shown. Now looking at the labels themselves, starting in the top left corner: First Row- ADX + DMI: These labels show the ADX, DI+, & DI- values in each label. Whenever the DI+ or DI- is above the other then their respective label will light up. Also, when the ADX is above 20 (confirming the trend) it lights up in the same color as well. Squeeze: I described how this worked above, the labels tell you if there's a Squeeze, how long there's been one, and how long since it fired, all while also changing to color of the associated Squeeze type. Second Row - Stacked EMAs: The top label looks at the EMA values using the numbers of the Fibonacci sequence. It looks at the EMA 8, 21, 34, 55, 89, & 233 and tells you if they're all stacked in the same direction (Stacked Bear meaning they're all crossed down in order, Stacked Bull meaning they're all crossed up in order). If the EMAs are all stacked but 1 or 2 it will say Stacked -1 or Stacked -2. When they're all over the place it will say they aren't stacked at all. BB%: This tells you the value of the Bollinger Band %. If this is negative then you know that price is currently below the lower Bollinger Band, and if it is above 100% it is above the upper Bollinger Band. RSI: This tells you the value of the RSI and the label changes colors based on the value. Stoch: This tells you the Stochastic value and changes colors based on the value, same as the RSI. Third Row - The Mean: This tells you the numerical value of whatever you have the mean set as (21 ema by default). The label changes colors based on price being above or below the mean. One ATR: This was something I added for those looking to plan their trades out. This tells you the value of one ATR so you can have a better idea of how to plan your trades based on this distance. VIX: This tells you the current value of the VIX, and color changes based on being green or red on the day. Ballista: I explained this above, it tells you the distance between the two oscillators and changes colors based on the trend being above or below 0. When there's an Inverted Squeeze this label is gray. Inverted Squeeze: This label tells you if there's an inverted squeeze as well as if it is showing an entry or how many bars since the last entry signal. This label turns fuchsia on a bear signal and lime on a bull signal. Fourth Row - ATR Bands: As I explained above, this plots each standard deviation using ATR and changes colors based on price's relationship to each one. Fifth Row - VWAP: The three labels here show the daily, weekly, and monthly VWAP values, and color changes based on price's relationship to each one. Sixth Row - VWAP Bands: These are the standard deviation levels of the VWAP resolution of your choosing (as explained above), and just as the others, colors change based on price's relationship to each one. I thought this was a really cool indicator that could be used for people like me who like knowing the right information, but HATE having their charts clustered with a ton of stuff. Hope you all like it, enjoy!
Pine Script® göstergesi
J-Streak tarafından
4
CVD - Cumulative Volume Delta Candles█   OVERVIEW This indicator displays cumulative ​volume delta in candle form. It uses intrabar information to obtain more precise ​volume delta information than methods using only the chart's timeframe. █   CONCEPTS Bar polarity By bar polarity , we mean the direction of a bar, which is determined by looking at the bar's close vs its open . Intrabars Intrabars are chart bars at a lower timeframe than the chart's. Each 1H chart bar of a 24x7 market will, for example, usually contain 60 bars at the lower timeframe of 1min, provided there was market activity during each minute of the hour. Mining information from intrabars can be useful in that it offers traders visibility on the activity inside a chart bar. Lower timeframes (LTFs) A lower timeframe is a timeframe that is smaller than the chart's timeframe. This script uses a ​LTF to access intrabars. The lower the ​LTF, the more intrabars are analyzed, but the less chart bars can display ​CVD information because there is a limit to the total number of intrabars that can be analyzed. Volume delta The ​volume delta concept divides a bar's ​volume in "up" and "down" ​volumes. The delta is calculated by subtracting down ​volume from up ​volume. Many calculation techniques exist to isolate up and down ​volume within a bar. The simplest techniques use the polarity of interbar price changes to assign their ​volume to up or down slots, e.g., On Balance Volume or the Klinger Oscillator . Others such as Chaikin Money Flow use assumptions based on a bar's OHLC values. The most precise calculation method uses tick data and assigns the ​volume of each tick to the up or down slot depending on whether the transaction occurs at the bid or ask price. While this technique is ideal, it requires huge amounts of data on historical bars, which usually limits the historical depth of charts and the number of symbols for which tick data is available. This indicator uses intrabar analysis to achieve a compromise between the simplest and most precise methods of calculating ​volume delta. In the context where historical tick data is not yet available on TradingView, intrabar analysis is the most precise technique to calculate ​volume delta on historical bars on our charts. Our Volume Profile indicators use it. Other ​volume delta indicators in our Community Scripts such as the Realtime 5D Profile use realtime chart updates to achieve more precise ​volume delta calculations, but that method cannot be used on historical bars, so those indicators only work in real time. This is the logic we use to assign intrabar ​volume to up or down slots:  • If the intrabar's open and close values are different, their relative position is used.  • If the intrabar's open and close values are the same, the difference between the intrabar's close and the previous intrabar's close is used.  • As a last resort, when there is no movement during an intrabar and it closes at the same price as the previous intrabar, the last known polarity is used. Once all intrabars making up a chart bar have been analyzed and the up or down property of each intrabar's ​volume determined, the up volumes are added and the down volumes subtracted. The resulting value is ​volume delta for that chart bar. █   FEATURES CVD Candles Cumulative ​Volume Delta Candles present ​volume delta information as it evolves during a period of time. This is how each candle's levels are calculated:  • open : Each candle's' open level is the cumulative ​volume delta for the current period at the start of the bar.   This value becomes zero on the first candle following a ​CVD reset.   The candles after the first one always open where the previous candle closed.   The candle's high, low and close levels are then calculated by adding or subtracting a ​volume value to the open.  • high : The highest ​​volume delta value found in intrabars. If it is not higher than the ​volume delta for the bar, then that candle will have no upper wick.  • low : The lowest ​​volume delta value found in intrabars. If it is not lower than the ​volume delta for the bar, then that candle will have no lower wick.  • close : The aggregated ​volume delta for all intrabars. If ​volume delta is positive for the chart bar, then the candle's close will be higher than its open, and vice versa. The candles are plotted in one of two configurable colors, depending on the polarity of ​volume delta for the bar. CVD resets The "cumulative" part of the indicator's name stems from the fact that calculations accumulate during a period of time. This allows you to analyze the progression of ​volume delta across manageable chunks, which is often more useful than looking at ​volume delta cumulated from the beginning of a chart's history. You can configure the reset period using the "CVD Resets" input, which offers the following selections:  • None : Calculations do not reset.  • On a fixed higher timeframe : Calculations reset on the higher timeframe you select in the "Fixed higher timeframe" field.  • At a fixed time that you specify.  • At the beginning of the regular session .  • On a stepped higher timeframe : Calculations reset on a higher timeframe automatically stepped using the chart's timeframe and following these rules: Chart TF ​HTF < 1min 1H < 3H 1D <= 12H 1W < 1W 1M >= 1W 1Y The indicator's background shows where resets occur. Intrabar precision The precision of calculations increases with the number of intrabars analyzed for each chart bar. It is controlled through the script's "Intrabar precision" input, which offers the following selections:  • Least precise, covering many chart bars  • Less precise, covering some chart bars  • More precise, covering less chart bars  • Most precise, 1min intrabars As there is a limit to the number of intrabars that can be analyzed by a script, a tradeoff occurs between the number of intrabars analyzed per chart bar and the chart bars for which calculations are possible. Total ​volume candles You can choose to display candles showing the total intrabar ​volume for the chart bar. This provides you with more context to evaluate a bar's ​volume delta by showing it relative to the sum of intrabar ​volume. Note that because of the reasons explained in the "NOTES" section further down, the total ​volume is the sum of all intrabar ​​volume rather than the ​volume of the bar at the chart's timeframe. Total ​volume candles can be configured with their own up and down colors. You can also control the opacity of their bodies to make them more or less prominent. This publication's chart shows the indicator with total ​volume candles. They are turned off by default, so you will need to choose to display them in the script's inputs for them to plot. Divergences Divergences occur when the polarity of ​volume delta does not match that of the chart bar. You can identify divergences by coloring the ​CVD candles differently for them, or by coloring the indicator's background. Information box An information box in the lower-left corner of the indicator displays the HTF used for resets, the ​LTF used for intrabars, and the average quantity of intrabars per chart bar. You can hide the box using the script's inputs. █   INTERPRETATION The first thing to look at when analyzing ​CVD candles is the side of the zero line they are on, as this tells you if ​CVD is generally ​bullish or ​bearish. Next, one should consider the relative position of successive candles, just as you would with a price chart. Are successive candles trending up, down, or stagnating? Keep in mind that whatever trend you identify must be considered in the context of where it appears with regards to the zero line; an uptrend in a negative ​CVD (below the zero line) may not be as powerful as one taking place in positive ​CVD values, but it may also predate a movement into positive ​CVD territory. The same goes with stagnation; a trader in a long position will find stagnation in positive ​CVD territory less worrisome than stagnation under the zero line. After consideration of the bigger picture, one can drill down into the details. Exactly what you are looking for in markets will, of course, depend on your trading methodology, but you may find it useful to:   • Evaluate ​volume delta for the bar in relation to price movement for that bar.   • Evaluate the proportion that ​​volume delta represents of total ​volume.   • Notice divergences and if the chart's candle shape confirms a hesitation point, as a Doji would.   • Evaluate if the progress of ​CVD candles correlates with that of chart bars.   • Analyze the wicks. As with price candles, long wicks tend to indicate weakness. Always keep in mind that unless you have chosen not to reset it, your ​CVD resets for each period, whether it is fixed or automatically stepped. Consequently, any trend from the preceding period must re-establish itself in the next. █   NOTES Know your volume Traders using ​volume information should understand the ​volume data they are using: where it originates and what transactions it includes, as this can vary with instruments, sectors, exchanges, timeframes, and between historical and realtime bars. The information used to build a chart's bars and display ​volume comes from data providers (exchanges, brokers, etc.) who often maintain distinct feeds for intraday and ​end-of-day (​EOD) timeframes. How ​volume data is assembled for the two feeds depends on how instruments are traded in that sector and/or the ​volume reporting policy for each feed. Instruments from crypto and forex markets, for example, will often display similar ​volume on both feeds. Stocks will often display variations because block trades or other types of trades may not be included in their intraday ​volume data. ​Futures will also typically display variations. Note that as intraday vs ​EOD variations exist for historical bars on some instruments, differences may also exist between the realtime feeds used on intraday vs 1D or greater timeframes for those same assets. Realtime reporting rules will often be different from historical feed reporting rules, so variations between realtime feeds will often be different from the variations between historical feeds for the same instrument. The Volume X-ray indicator can help you analyze differences between intraday and ​EOD ​volumes for the instruments you trade. If every unit of ​volume is both bought by a buyer and sold by a seller, how can ​volume delta make sense? Traders who do not understand the mechanics of matching engines (the exchange software that matches orders from buyers and sellers) sometimes argue that the concept of ​volume delta is flawed, as every unit of ​volume is both bought and sold. While they are rigorously correct in stating that every unit of ​volume is both bought and sold, they overlook the fact that information can be mined by analyzing variations in the price of successive ticks, or in our case, intrabars. Our calculations model the situation where, in fully automated order handling, market orders are generally matched to limit orders sitting in the order book. Buy market orders are matched to quotes at the ask level and sell market orders are matched to quotes at the bid level. As explained earlier, we use the same logic when comparing intrabar prices. While using intrabar analysis does not produce results as precise as when individual transactions — or ticks — are analyzed, results are much more precise than those of methods using only chart prices. Not only does the concept underlying ​volume delta make sense, it provides a window on an oft-overlooked variable which, with price and time, is the only basic information representing market activity. Furthermore, because the calculation of ​volume delta also uses price and time variations, one could conceivably surmise that it can provide a more complete model than ones using price and time only. Whether or not ​volume delta can be useful in your trading practice, as usual, is for you to decide, as each trader's methodology is different. For Pine Script™ coders As our latest Polarity Divergences publication, this script uses the recently released request.security_lower_tf() Pine Script™ function discussed in this blog post . It works differently from the usual request.security() in that it can only be used at LTFs, and it returns an array containing one value per intrabar. This makes it much easier for programmers to access intrabar information. Look first. Then leap.
Pine Script® göstergesi
TradingView tarafından
225
Joint Conditions Strategy Suite + TradingConnector alerts bot"Please give us combined alerts with the possibility of having several conditions in place to trigger the alert." - was the top voted request from users under one of the recent blogposts by TradingView. Ask and you shall receive ;) TradingView is a great platform, with unmatched set of functionalities, yet this particular combo of features indeed seems not to be in place. Fortunately, TradingView is also very open platform, thanks to PineScript coding language, which enables developing combos like the requried one and plenty of other magic. I have already published numerous "educational" scripts, showing how to code indicators and alerts with PineScript, but... this is not one of them. This one is for real. READY FOR USE on real markets, also by the non-coding traders. Just take my script, set parameters with dropdowns, backtest the strategy, fire the alerts and execute them. HOW TO USE IT In "Settings" popup I tried to mimic the CreateAlert popup dropdowns for selecting logic. Let's say you want to enter Long position at Stochastic KxD crossover. In first line of Long Entry conditions set "StochK" + "Crossing Up" + "StochD". Last field doesn't matter because in 3rd dropdown something else than "value" was selected. In second line you could set "maB" + "Greater Than" + "maC" to filter out those entries which are in direction of the uptrend. And yeah, add ADX>25 to make sure the market is actually moving: "ADX" + "Greater Than" + "value" + "25". All condition lines must be TRUE (or skipped) for the entry to be triggered. Toghether with an alert. The same for Short entries. Combinations are limitless. INDICATORS AND MTF (MULTI-TIMEFRAME) In those dropdowns you can select candle values like open/close/high/low/ohlc4, but also some most popular indicators, which I have pre-built into this script: RSI, various Moving Averages, ADX-DMI, Stochastic and Bollinger Bands for start. You can configure parameters of those indicators also in "Settings" popup, in "Indicator Definitions" section. What's important, you can use any of these indicators from higher timeframe, setting MTF multiplier. So if you applied this indicator to 1h chart, but want to use rsi(close,14) from 4h chart, set MTF to 4. If you want to use current timeframe indicators, keep MTF at 1, which is a default setting here. Note for coders: to keep focus of this script on joining conditions, entire logic for those indicators has been moved to external library, also open source. I encourage you to dig into the code and see how it's done. I love the addition of libraries concept in PineScript. CUSTOM INDICATOR Following the "openness" spirit of my master - which is TradingView itself - my work is also open, in 2 ways: 1. This script is open source. So you can grab it, modify or add any functionalities you want. I cannot and don't want to stop you from doing that. I'm asking for only one favor - please mention this source script in your credits. 2. You can import the plot (series) from any other indicator on TradingView. In Settings popup of my script, scroll down to "Indicator Definitions" section, and select the series of your choice in the first dropdown. Now it is ready to use in conditions dropdowns on top of the Settings popup. Let me give you an example of that last scenario. Take another script of mine, "Pivot Points on SR lines DEMO". You can find it in "Indicators & Strategies" library or here: (). Attach it to your chart. Now come back to THIS script, open Settings popup and in "Custom Indicator aka Imported Source" select "Pivot Points on SR lines: ...". The way it works - it detects if a pivot point happened on Support/Resistance line from the past and returns 1 for PivotLow and -1 for Pivot High. Now in first Long Entry condition set: "custom indicator" + "Greater Than" + "value" + "0" and long entries will be marked on every pivot low noticed on Support/Resistance line. ALERTS Last but not least - the alerts. This script produces alerts on the entries calculated by strategy logic, as marked on the chart by the backtester. Moreover, syntax of those alerts is already prepared and fully compatible with TradingConnector - alerts executing tool (bot), if you want to auto-execute those trades. Apart from installing the tool, you need to set up the alerts in TradingView, here is how: open CreateAlert popup in first dropdown select "Joint Conditions Strategy Template" in second dropdown select "alert() function calls only" And that's all. You only need to set one alert for the whole script, not one for Longs and one for Shorts as it was in the past. Also, you don't need to setup closing alerts, because stop-loss/take-profit/trailing-stop information is embedded in the entry alert so your broker receives it as early as possible. Alerts sent will look like this: "long sl=40 tp=80", which is exactly what TradingConnector expects. Phew, that's all folks. If you think I should add something to this template (maybe other indicators?) please let me know in comments or via DM. Happy trading! P.S. Pyramiding is not supported in this script. Disclaimer : I'm not saying above combination of conditions will make you money. Actually none of this can be considered financial advice. It is only a software tool. Use it wisely, be aware of the risk and do your own research!
Pine Script® stratejisi
Peter_O tarafından
15
40+ Coin Screener (workaround to 40 Security Limit Per Script) This is a far inferior method for a screener/scanner (compared to my first publication) but after looking at that script from a noobs eyes again, I could see how this form would be a lot easier to take in/understand so wanted to publish it. Everything that I could think of to mention about this is in my 1st pub so ill leave it to you to check it out...though I did include some comments in the script. It is pretty straight forward but if you have any questions don't hold them in. I'll answer them if I can. The only thing that is not in this one is setting up the alert feature so that you only have to create 1 alert per iteration of the script and it takes care of all of the coins for that iteration/set that is chosen in the settings (so please see previous script if would like to do this for your screener/scanner). To be PERFECTLY CLEAR, the workaround is to the issue of not being able to scan but only 40 coins per script. You can scan more than 40 per script but only if you create "batches" or "sets" that the user can select within the settings which set to use for each iteration of the script on the chart. That being, you have to the script multiple times to the chart and merge them into 1 window and merge the scales (instructions in first publications). Here in this script I am scanning 72 different coins that are the Margin Coins on KUCOIN. I have split them up into 3 sets (24 coins per set). I could have made 2 sets but the script will be slower to load and to respond (like, when it comes to receiving alerts), thus I split them up the way I did. If you want to change any of this there are slightly more details in the previous script. One great use-case that I LOVE about this particular version (and the way I use it) is right at the end of when I see a whole market dump/pump coming to an end and want to know which horse to bet on. Used to think whichever coin come out the fastest from the dump was the one to bet on but quickly learned that 1-2 (or even a few) hrs needs to go by first bc the ones that look the strongest in the beginning are NOT the ones to have performed the best when viewing the results 12 hrs later. IN FACT, many instances of using this exact script for reasons as such has taught me that the manipulators (I believe this to be the case as least) WANT everyone to bet on these that come out the gate the hardest and thus they make them move REALLY hard in the beginning then they QUICKLY become stagnant (moreso, they become WORSE than stagnant, they actually quickly retrace to put you into the negative so that you get out to get into the others now moving (to provide the market with more liquidity. They WANT you to get into a coin thats moving crazy hard so that they can then cease that movement once many fall for the trick just to then make that once strong looking coin now stagnant and make others move crazy hard. They wait for you to get out of the 1st and into the next set of movers just to do this time and time again bc hey, what are we sheep good for other than to provide the big guns with liquidity, am I right? Thats rhetorical, which you would know if you've ever had this happen to you (without a doubt MANY of you have). Let this script (above all other things) provide good evidence to back up this cynical way of viewing the markets to anyone that is questioning it. This prolonged time between when the dump is over and when the ACTUAL movers REALLY start moving can actually be of great benefit to us sheep if used correctly, Firstly, it gives us some time to determine if when we thought was the bottom, ACTUALLY was the bottom. That bottom is easily determined if there are no (or very few) coins that went any lower than the point in time that the script began calculating on. Secondly, it allows us time to wait for the REAL movers and shakers to start moving and shaking. One new feature that I LOVE that TV has implemented is the ability (once the script is added to the chart) to be able to click a point in time on the chart where you want the script to begin its calculations. If this point needs to be changed at any point in time then you can either go into the setting and input the time you wish or simply remove the script and add it again so that you are prompted to select another point in time. Ok, I think that everything I wanted to say. The next version that I will add will be probably my favorite and most used by yours truly...not to mention unique in a way that I have yet to see an implementation anything like it in all of TV's public library. Not to say its not there, but I have yet to come across it and I have DEFINITELY done my fair share of searching for it when I couldn't figure out how to code it for the longest time (though, I was and still am a noob so might get some great feedback on better ways to approach it, but we'll save that jabbering for the next of the publications. I hope each and every one of ya'll (yes, Im from the South) have the GREATEST of Thanksgivings (if in the US that is...I graced my parents with the best gift anyone could have given them 35 years ago on Thanksgiving....MEEEE ;) So I will sure as hell be having a great holiday. Thanks for checking out my script...you can "like" and leave a comment if you so feel the urge to...or not. Im not doing this for me, but rather to stretch my arms out as far as possible to benefit the most people as possible and more people would see the script if it has more likes/comments/traffic pointing towards it...not to mention as other publishers have...it IS gratifying to see a few likes in my side window, which btw, I have MANY more variations and completely diff types of scanners/screeners Ill be publishing in the future and to know that they've become of use....I"VE become of use to the community is very....pleasing to me and does (as I've also seen many publishers mention as well) drive me to want to publish ones that I originally thought I would keep for myself. Peace out people.
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