Hybrid Adaptive Double Exponential Smoothing🙏🏻 This is HADES (Hybrid Adaptive Double Exponential Smoothing) : fully data-driven & adaptive exponential smoothing method, that gains all the necessary info directly from data in the most natural way and needs no subjective parameters & no optimizations. It gets applied to data itself -> to fit residuals & one-point forecast errors, all at O(1) algo complexity. I designed it for streaming high-frequency univariate time series data, such as medical sensor readings, orderbook data, tick charts, requests generated by a backend, etc.
The HADES method is:
fit & forecast = a + b * (1 / alpha + T - 1)
T = 0 provides in-sample fit for the current datum, and T + n provides forecast for n datapoints.
y = input time series
a = y, if no previous data exists
b = 0, if no previous data exists
otherwise:
a = alpha * y + (1 - alpha) * a
b = alpha * (a - a ) + (1 - alpha) * b
alpha = 1 / sqrt(len * 4)
len = min(ceil(exp(1 / sig)), available data)
sig = sqrt(Absolute net change in y / Sum of absolute changes in y)
For the start datapoint when both numerator and denominator are zeros, we define 0 / 0 = 1
...
The same set of operations gets applied to the data first, then to resulting fit absolute residuals to build prediction interval, and finally to absolute forecasting errors (from one-point ahead forecast) to build forecasting interval:
prediction interval = data fit +- resoduals fit * k
forecasting interval = data opf +- errors fit * k
where k = multiplier regulating intervals width, and opf = one-point forecasts calculated at each time t
...
How-to:
0) Apply to your data where it makes sense, eg. tick data;
1) Use power transform to compensate for multiplicative behavior in case it's there;
2) If you have complete data or only the data you need, like the full history of adjusted close prices: go to the next step; otherwise, guided by your goal & analysis, adjust the 'start index' setting so the calculations will start from this point;
3) Use prediction interval to detect significant deviations from the process core & make decisions according to your strategy;
4) Use one-point forecast for nowcasting;
5) Use forecasting intervals to ~ understand where the next datapoints will emerge, given the data-generating process will stay the same & lack structural breaks.
I advise k = 1 or 1.5 or 4 depending on your goal, but 1 is the most natural one.
...
Why exponential smoothing at all? Why the double one? Why adaptive? Why not Holt's method?
1) It's O(1) algo complexity & recursive nature allows it to be applied in an online fashion to high-frequency streaming data; otherwise, it makes more sense to use other methods;
2) Double exponential smoothing ensures we are taking trends into account; also, in order to model more complex time series patterns such as seasonality, we need detrended data, and this method can be used to do it;
3) The goal of adaptivity is to eliminate the window size question, in cases where it doesn't make sense to use cumulative moving typical value;
4) Holt's method creates a certain interaction between level and trend components, so its results lack symmetry and similarity with other non-recursive methods such as quantile regression or linear regression. Instead, I decided to base my work on the original double exponential smoothing method published by Rob Brown in 1956, here's the original source , it's really hard to find it online. This cool dude is considered the one who've dropped exponential smoothing to open access for the first time🤘🏻
R&D; log & explanations
If you wanna read this, you gotta know, you're taking a great responsability for this long journey, and it gonna be one hell of a trip hehe
Machine learning, apprentissage automatique, машинное обучение, digital signal processing, statistical learning, data mining, deep learning, etc., etc., etc.: all these are just artificial categories created by the local population of this wonderful world, but what really separates entities globally in the Universe is solution complexity / algorithmic complexity.
In order to get the game a lil better, it's gonna be useful to read the HTES script description first. Secondly, let me guide you through the whole R&D; process.
To discover (not to invent) the fundamental universal principle of what exponential smoothing really IS, it required the review of the whole concept, understanding that many things don't add up and don't make much sense in currently available mainstream info, and building it all from the beginning while avoiding these very basic logical & implementation flaws.
Given a complete time t, and yet, always growing time series population that can't be logically separated into subpopulations, the very first question is, 'What amount of data do we need to utilize at time t?'. Two answers: 1 and all. You can't really gain much info from 1 datum, so go for the second answer: we need the whole dataset.
So, given the sequential & incremental nature of time series, the very first and basic thing we can do on the whole dataset is to calculate a cumulative , such as cumulative moving mean or cumulative moving median.
Now we need to extend this logic to exponential smoothing, which doesn't use dataset length info directly, but all cool it can be done via a formula that quantifies the relationship between alpha (smoothing parameter) and length. The popular formulas used in mainstream are:
alpha = 1 / length
alpha = 2 / (length + 1)
The funny part starts when you realize that Cumulative Exponential Moving Averages with these 2 alpha formulas Exactly match Cumulative Moving Average and Cumulative (Linearly) Weighted Moving Average, and the same logic goes on:
alpha = 3 / (length + 1.5) , matches Cumulative Weighted Moving Average with quadratic weights, and
alpha = 4 / (length + 2) , matches Cumulative Weighted Moving Average with cubic weghts, and so on...
It all just cries in your shoulder that we need to discover another, native length->alpha formula that leverages the recursive nature of exponential smoothing, because otherwise, it doesn't make sense to use it at all, since the usual CMA and CMWA can be computed incrementally at O(1) algo complexity just as exponential smoothing.
From now on I will not mention 'cumulative' or 'linearly weighted / weighted' anymore, it's gonna be implied all the time unless stated otherwise.
What we can do is to approach the thing logically and model the response with a little help from synthetic data, a sine wave would suffice. Then we can think of relationships: Based on algo complexity from lower to higher, we have this sequence: exponential smoothing @ O(1) -> parametric statistics (mean) @ O(n) -> non-parametric statistics (50th percentile / median) @ O(n log n). Based on Initial response from slow to fast: mean -> median Based on convergence with the real expected value from slow to fast: mean (infinitely approaches it) -> median (gets it quite fast).
Based on these inputs, we need to discover such a length->alpha formula so the resulting fit will have the slowest initial response out of all 3, and have the slowest convergence with expected value out of all 3. In order to do it, we need to have some non-linear transformer in our formula (like a square root) and a couple of factors to modify the response the way we need. I ended up with this formula to meet all our requirements:
alpha = sqrt(1 / length * 2) / 2
which simplifies to:
alpha = 1 / sqrt(len * 8)
^^ as you can see on the screenshot; where the red line is median, the blue line is the mean, and the purple line is exponential smoothing with the formulas you've just seen, we've met all the requirements.
Now we just have to do the same procedure to discover the length->alpha formula but for double exponential smoothing, which models trends as well, not just level as in single exponential smoothing. For this comparison, we need to use linear regression and quantile regression instead of the mean and median.
Quantile regression requires a non-closed form solution to be solved that you can't really implement in Pine Script, but that's ok, so I made the tests using Python & sklearn:
paste.pics
^^ on this screenshot, you can see the same relationship as on the previous screenshot, but now between the responses of quantile regression & linear regression.
I followed the same logic as before for designing alpha for double exponential smoothing (also considered the initial overshoots, but that's a little detail), and ended up with this formula:
alpha = sqrt(1 / length) / 2
which simplifies to:
alpha = 1 / sqrt(len * 4)
Btw, given the pattern you see in the resulting formulas for single and double exponential smoothing, if you ever want to do triple (not Holt & Winters) exponential smoothing, you'll need len * 2 , and just len * 1 for quadruple exponential smoothing. I hope that based on this sequence, you see the hint that Maybe 4 rounds is enough.
Now since we've dealt with the length->alpha formula, we can deal with the adaptivity part.
Logically, it doesn't make sense to use a slower-than-O(1) method to generate input for an O(1) method, so it must be something universal and minimalistic: something that will help us measure consistency in our data, yet something far away from statistics and close enough to topology.
There's one perfect entity that can help us, this is fractal efficiency. The way I define fractal efficiency can be checked at the very beginning of the post, what matters is that I add a square root to the formula that is not typically added.
As explained in the description of my metric QSFS , one of the reasons for SQRT-transformed values of fractal efficiency applied in moving window mode is because they start to closely resemble normal distribution, yet with support of (0, 1). Data with this interesting property (normally distributed yet with finite support) can be modeled with the beta distribution.
Another reason is, in infinitely expanding window mode, fractal efficiency of every time series that exhibits randomness tends to infinitely approach zero, sqrt-transform kind of partially neutralizes this effect.
Yet another reason is, the square root might better reflect the dimensional inefficiency or degree of fractal complexity, since it could balance the influence of extreme deviations from the net paths.
And finally, fractals exhibit power-law scaling -> measures like length, area, or volume scale in a non-linear way. Adding a square root acknowledges this intrinsic property, while connecting our metric with the nature of fractals.
---
I suspect that, given analogies and connections with other topics in geometry, topology, fractals and most importantly positive test results of the metric, it might be that the sqrt transform is the fundamental part of fractal efficiency that should be applied by default.
Now the last part of the ballet is to convert our fractal efficiency to length value. The part about inverse proportionality is obvious: high fractal efficiency aka high consistency -> lower window size, to utilize only the last data that contain brand new information that seems to be highly reliable since we have consistency in the first place.
The non-obvious part is now we need to neutralize the side effect created by previous sqrt transform: our length values are too low, and exponentiation is the perfect candidate to fix it since translating fractal efficiency into window sizes requires something non-linear to reflect the fractal dynamics. More importantly, using exp() was the last piece that let the metric shine, any other transformations & formulas alike I've tried always had some weird results on certain data.
That exp() in the len formula was the last piece that made it all work both on synthetic and on real data.
^^ a standalone script calculating optimal dynamic window size
Omg, THAT took time to write. Comment and/or text me if you need
...
"Versace Pip-Boy, I'm a young gun coming up with no bankroll" 👻
∞
Komut dosyalarını "文华财经tick价格" için ara
Gold Friday Anomaly StrategyThis script implements the " Gold Friday Anomaly Strategy ," a well-known historical trading strategy that leverages the gold market's behavior from Thursday evening to Friday close. It is a backtesting-focused strategy designed to assess the historical performance of this pattern. Traders use this anomaly as it captures a recurring market tendency observed over the years.
What It Does:
Entry Condition: The strategy enters a long position at the beginning of the Friday trading session (Thursday evening close) within the defined backtesting period.
Exit Condition: Friday evening close.
Backtesting Controls: Allows users to set custom backtesting periods to evaluate strategy performance over specific date ranges.
Key Features:
Custom Backtest Periods: Easily configurable inputs to set the start and end date of the backtesting range.
Fixed Slippage and Commission Settings: Ensures realistic simulation of trading conditions.
Process Orders on Close: Backtesting is optimized by processing orders at the bar's close.
Important Notes:
Backtesting Only: This script is intended purely for backtesting purposes. Past performance is not indicative of future results.
Live Trading Recommendations: For live trading, it is highly recommended to use limit orders instead of market orders, especially during evening sessions, as market order slippage can be significant.
Default Settings:
Entry size: 10% of equity per trade.
Slippage: 1 tick.
Commission: 0.05% per trade.
Triple CCI Strategy MFI Confirmed [Skyrexio]Overview
Triple CCI Strategy MFI Confirmed leverages 3 different periods Commodity Channel Index (CCI) indicator in conjunction Money Flow Index (MFI) and Exponential Moving Average (EMA) to obtain the high probability setups. Fast period CCI is used for having the high probability to enter in the direction of short term trend, middle and slow period CCI are used for confirmation, if market now likely in the mid and long-term uptrend. MFI is used to confirm trade with the money inflow/outflow with the high probability. EMA is used as an additional trend filter. Moreover, strategy uses exponential moving average (EMA) to trail the price when it reaches the specific level. More information in "Methodology" and "Justification of Methodology" paragraphs. The strategy opens only long trades.
Unique Features
Dynamic stop-loss system: Instead of fixed stop-loss level strategy utilizes average true range (ATR) multiplied by user given number subtracted from the position entry price as a dynamic stop loss level.
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Four layers trade filtering system: Strategy utilizes two different period CCI indicators, MFI and EMA indicators to confirm the signals produced by fast period CCI.
Trailing take profit level: After reaching the trailing profit activation level scrip activate the trailing of long trade using EMA. More information in methodology.
Methodology
The strategy opens long trade when the following price met the conditions:
Fast period CCI shall crossover the zero-line.
Slow and Middle period CCI shall be above zero-lines.
Price shall close above the EMA. Crossover is not obligatory
MFI shall be above 50
When long trade is executed, strategy set the stop-loss level at the price ATR multiplied by user-given value below the entry price. This level is recalculated on every next candle close, adjusting to the current market volatility.
At the same time strategy set up the trailing stop validation level. When the price crosses the level equals entry price plus ATR multiplied by user-given value script starts to trail the price with EMA. If price closes below EMA long trade is closed. When the trailing starts, script prints the label “Trailing Activated”.
Strategy settings
In the inputs window user can setup the following strategy settings:
ATR Stop Loss (by default = 1.75)
ATR Trailing Profit Activation Level (by default = 2.25)
CCI Fast Length (by default = 14, used for calculation short term period CCI)
CCI Middle Length (by default = 25, used for calculation short term period CCI)
CCI Slow Length (by default = 50, used for calculation long term period CCI)
MFI Length (by default = 14, used for calculation MFI
EMA Length (by default = 50, period of EMA, used for trend filtering EMA calculation)
Trailing EMA Length (by default = 20)
User can choose the optimal parameters during backtesting on certain price chart.
Justification of Methodology
Before understanding why this particular combination of indicator has been chosen let's briefly explain what is CCI, MFI and EMA.
The Commodity Channel Index (CCI) is a momentum-based technical indicator that measures the deviation of a security's price from its average price over a specific period. It helps traders identify overbought or oversold conditions and potential trend reversals.
The CCI formula is:
CCI = (Typical Price − SMA) / (0.015 × Mean Deviation)
Typical Price (TP): This is calculated as the average of the high, low, and closing prices for the period.
Simple Moving Average (SMA): This is the average of the Typical Prices over a specific number of periods.
Mean Deviation: This is the average of the absolute differences between the Typical Price and the SMA.
The result is a value that typically fluctuates between +100 and -100, though it is not bounded and can go higher or lower depending on the price movement.
The Money Flow Index (MFI) is a technical indicator that measures the strength of money flowing into and out of a security. It combines price and volume data to assess buying and selling pressure and is often used to identify overbought or oversold conditions. The formula for MFI involves several steps:
1. Calculate the Typical Price (TP):
TP = (high + low + close) / 3
2. Calculate the Raw Money Flow (RMF):
Raw Money Flow = TP × Volume
3. Determine Positive and Negative Money Flow:
If the current TP is greater than the previous TP, it's Positive Money Flow.
If the current TP is less than the previous TP, it's Negative Money Flow.
4. Calculate the Money Flow Ratio (MFR):
Money Flow Ratio = Sum of Positive Money Flow (over n periods) / Sum of Negative Money Flow (over n periods)
5. Calculate the Money Flow Index (MFI):
MFI = 100 − (100 / (1 + Money Flow Ratio))
MFI above 80 can be considered as overbought, below 20 - oversold.
The Exponential Moving Average (EMA) is a type of moving average that places greater weight and significance on the most recent data points. It is widely used in technical analysis to smooth price data and identify trends more quickly than the Simple Moving Average (SMA).
Formula:
1. Calculate the multiplier
Multiplier = 2 / (n + 1) , Where n is the number of periods.
2. EMA Calculation
EMA = (Current Price) × Multiplier + (Previous EMA) × (1 − Multiplier)
This strategy leverages Fast period CCI, which shall break the zero line to the upside to say that probability of short term trend change to the upside increased. This zero line crossover shall be confirmed by the Middle and Slow periods CCI Indicators. At the moment of breakout these two CCIs shall be above 0, indicating that there is a high probability that price is in middle and long term uptrend. This approach increases chances to have a long trade setup in the direction of mid-term and long-term trends when the short-term trend starts to reverse to the upside.
Additionally strategy uses MFI to have a greater probability that fast CCI breakout is confirmed by this indicator. We consider the values of MFI above 50 as a higher probability that trend change from downtrend to the uptrend is real. Script opens long trades only if MFI is above 50. As you already know from the MFI description, it incorporates volume in its calculation, therefore we have another one confirmation factor.
Finally, strategy uses EMA an additional trend filter. It allows to open long trades only if price close above EMA (by default 50 period). It increases the probability of taking long trades only in the direction of the trend.
ATR is used to adjust the strategy risk management to the current market volatility. If volatility is low, we don’t need the large stop loss to understand the there is a high probability that we made a mistake opening the trade. User can setup the settings ATR Stop Loss and ATR Trailing Profit Activation Level to realize his own risk to reward preferences, but the unique feature of a strategy is that after reaching trailing profit activation level strategy is trying to follow the trend until it is likely to be finished instead of using fixed risk management settings. It allows sometimes to be involved in the large movements. It’s also important to make a note, that script uses another one EMA (by default = 20 period) as a trailing profit level.
Backtest Results
Operating window: Date range of backtests is 2022.04.01 - 2024.11.25. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 50%
Maximum Single Position Loss: -4.13%
Maximum Single Profit: +19.66%
Net Profit: +5421.21 USDT (+54.21%)
Total Trades: 108 (44.44% win rate)
Profit Factor: 2.006
Maximum Accumulated Loss: 777.40 USDT (-7.77%)
Average Profit per Trade: 50.20 USDT (+0.85%)
Average Trade Duration: 44 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 2h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
US/JP Factor/Sector Performance RankingThis indicator is designed to help you easily understand the strengths and weaknesses of different factors and sectors in the U.S. stock market. It looks at various ETFs, ranks their performance over a specific period (20 days by default), and shows the results visually.
= How the Ranking Works
The best-performing rank is shown as -1, with lower ranks as -2, -3, -4, and so on. This setup makes it easy to see rank order in TradingView’s default view.
If you turn on the “Inverse” setting, ranks will be shown as positive numbers in order (e.g., 1, 2, 3…). In this case, it’s recommended to reverse the TradingView scale for better understanding.
= How the Indicator Reacts to Market Conditions
- Normal Market Conditions
Certain factors or sectors often stay at the top rank. For example, during the rallies at the start of 2024 and in May, the Momentum factor performed well, showing a risk-on market environment.
On the other hand, sectors at the bottom rank also tend to stay in specific positions.
- Market Tops
Capital flows within sectors slow down, and top ranks begin to change frequently. This may suggest a market turning point.
- Bear Markets or High Volatility
Rankings become more chaotic in these conditions. These large changes can help you understand market sentiment and the level of volatility.
= Way of using the Indicator
You can use this indicator in the following ways:
- To apply sector rotation strategies.
- To build positions after volatile markets calm down.
- To take long positions on strong elements (higher ranks) and short positions on weaker ones (lower ranks).
= Things to Keep in Mind
It’s a Lagging Indicator
This indicator calculates rankings using the past 20 days of data. It doesn’t provide signals for the future but is a tool for analyzing past performance. To predict the market, you should combine this with other tools or leading indicators.
However, since trends in capital flows often continue, this indicator can help you spot those trends.
= Customization
This indicator is set up for U.S. and Japanese stock markets. However, you can customize it for other markets by changing the ticker and label description in the script.
==Japanese Description==
このインジケーターは、米国株市場におけるファクターやセクターの強弱を直感的に把握するために設計されています。
各ETFを参照し、特定期間（デフォルトでは20日間）のパフォーマンスを順位付けし、それを視覚的に表示します。
= インジケーターの特徴
- ランク付けの仕様
ランク1位は-1で表され、順位が下がるごとに-2、-3、-4…と減少します。この仕様により、TradingViewの標準状態でランクの高低を直感的に把握できるようにしました。
さらに、Inverse設定をONにすると、1位から順に正の値（例: 1, 2, 3…）で表示されるようになります。この場合、TradingViewのスケールを反転させることを推奨します。
= 市況とインジケーターの動き
- 平常時の市況
特定のファクターやセクターがランク1位を維持することが多いです。
例えば、2024年の年初や同年5月の上昇相場では、Momentumファクターが効果を発揮し、リスクオンの市場環境であったことを示しています。
一方、最下位に位置するセクターも特定の順位を維持する傾向があります。
- 天井圏の市況
セクター内の資金流入や流出が停滞し、上位ランクの変動が起こり始めます。これが市場の転換点を示唆する場合があります。
- 下落相場や荒れた市況
ランク順位が大きく乱れることが特徴です。この変動の大きさは、市況の雰囲気やボラティリティの高さを感じ取る材料として活用できます。
= 活用方法
このインジケーターは以下のような投資戦略に役立てることができます：
- セクターローテーションを活用した投資戦略
- 荒れた相場が落ち着いたタイミングでのポジション構築
- 強い要素（ランク上位）のロング、弱い要素（ランク下位）のショート
= 注意点
- 遅行指標であること
本インジケーターは、過去20日間のデータを基にランクを算出します。そのため、先行的なシグナルを提供するものではなく、過去のパフォーマンスに基づいた分析ツールです。市場を先回りするには、別途先行指標や分析を組み合わせる必要があります。
ただし、特定のファクターやセクターへの資金流入・流出が継続する傾向があるため、これを見極める手助けにはなります。
= カスタマイズについて
このインジケーターは米国・日本株市場に特化しています。ただし、他国のファクターやセクターのETFや指数が利用可能であれば、スクリプト内のtickerとlabel descriptionを変更することでカスタマイズが可能です。
Supertrend StrategyThe Supertrend Strategy was created based on the Supertrend and Relative Strength Index (RSI) indicators, widely respected tools in technical analysis. This strategy combines these two indicators to capture market trends with precision and reliability, looking for optimizing exit levels at oversold or overbought price levels.
The Supertrend indicator identifies trend direction based on price and volatility by using the Average True Range (ATR). The ATR measures market volatility by calculating the average range between an asset’s high and low prices over a set period. It provides insight into price fluctuations, with higher ATR values indicating increased volatility and lower values suggesting stability. The Supertrend Indicator plots a line above or below the price, signaling potential buy or sell opportunities: when the price closes above the Supertrend line, an uptrend is indicated, while a close below the line suggests a downtrend. This line shifts as price movements and volatility levels change, acting as both a trailing stop loss and trend confirmation.
To enhance the Supertrend strategy, the Relative Strength Index (RSI) has been added as an exit criterion. As a momentum oscillator, the RSI indicates overbought (usually above 70) or oversold (usually below 30) conditions. This integration allows trades to close when the asset is overbought or oversold, capturing gains before a possible reversal, even if the percentage take profit level has not been reached. This mechanism aims to prevent losses due to market reversals before the Supertrend signal changes.
### Key Features
1. **Entry criteria**:
- The strategy uses the Supertrend indicator calculated by adding or subtracting a multiple of the ATR from the closing price, depending on the trend direction.
- When the price crosses above the Supertrend line, the strategy signals a long (buy) entry. Conversely, when the price crosses below, it signals a short (sell) entry.
- The strategy performs a reversal if there is an open position and a change in the direction of the supertrend occurs
2. **Exit criteria**:
- Take profit of 30% (default) on the average position price.
- Oversold (≤ 5) or overbought (≥ 95) RSI
- Reversal when there is a change in direction of the Supertrend
3. **No Repainting**:
- This strategy is not subject to repainting, as long as the timeframe configured on your chart is the same as the supertrend timeframe .
4. **Position Sizing by Equity and risk management**:
- This strategy has a default configuration to operate with 35% of the equity. At the time of opening the position, the supertrend line is typically positioned at about 12 to 16% of the entry price. This way, the strategy is putting at risk about 16% of 35% of equity, that is, around 5.6% of equity for each trade. The percentage of equity can be adjusted by the user according to their risk management.
5. **Backtest results**:
- This strategy was subjected to deep backtesting and operations in replay mode, including transaction fees of 0.12%, and slippage of 5 ticks.
- The past results in deep backtest and replay mode were compatible and profitable (Variable results depending on the take profit used, supertrend and RSI parameters). However, it should be noted that few operations were evaluated, since the currency in question has been created for a short time and the frequency of operations is relatively small.
- Past results are no guarantee of future results. The strategy's backtest results may even be due to overfitting with past data.
Default Settings
Chart timeframe: 2h
Supertrend Factor: 3.42
ATR period: 14
Supertrend timeframe: 2 h
RSI timeframe: 15 min
RSI Lenght: 5 min
RSI Upper limit: 95
RSI Lower Limit: 5
Take Profit: 30%
BYBIT:1000000MOGUSDT.P
(Early Test) Weekly Seasonality with Dynamic Kelly Criterion# Enhancing Trading Strategies with the Weekly Seasonality Dynamic Kelly Criterion Indicator
Amidst this pursuit to chase price, a common pitfall emerges: an overemphasis on price movements without adequate attention to risk management, probabilistic analysis, and strategic position sizing. To address these challenges, I developed the **Weekly Seasonality with Dynamic Kelly Criterion Indicator**. It is designed to refocus traders on essential aspects of trading, such as risk management and probabilistic returns, thereby catering to both short-term swing traders and long-term investors aiming for tax-efficient positions.
## The Motivation Behind the Indicator
### Overemphasis on Price: A Common Trading Pitfall
Many traders concentrate heavily on price charts and technical indicators, often neglecting the underlying principles of risk management and probabilistic analysis. This overemphasis on price can lead to:
- **Overtrading:** Making frequent trades based solely on price movements without considering the associated risks.
- **Poor Risk Management:** Failing to set appropriate stop-loss levels or position sizes, increasing the potential for significant losses.
- **Emotional Trading:** Letting emotions drive trading decisions rather than objective analysis, which can result in impulsive and irrational trades.
### The Need for Balanced Focus
To achieve sustained trading success, it is crucial to balance price analysis with robust risk management and probabilistic strategies. Key areas of focus include:
1. **Risk Management:** Implementing strategies to protect capital, such as setting stop-loss orders and determining appropriate position sizes based on risk tolerance.
2. **Probabilistic Analysis:** Assessing the likelihood of various market outcomes to make informed trading decisions.
3. **Swing Trading Percent Returns:** Capitalizing on short- to medium-term price movements by buying assets below their average return and selling them above.
## Introducing the Weekly Seasonality with Dynamic Kelly Criterion Indicator
The **Weekly Seasonality with Dynamic Kelly Criterion Indicator** is designed to integrate these essential elements into a comprehensive tool that aids traders in making informed, risk-aware decisions. Below, we explore the key components and functionalities of this indicator.
### Key Components of the Indicator
1. **Average Return (%)**
- **Definition:** The mean percentage return for each week across multiple years.
- **Purpose:** Serves as a benchmark to identify weeks with above or below-average performance, guiding buy and sell decisions.
2. **Positive Percentage (%)**
- **Definition:** The proportion of weeks that yielded positive returns.
- **Purpose:** Indicates the consistency of positive returns, helping traders gauge the reliability of certain weeks for trading.
3. **Volatility (%)**
- **Definition:** The standard deviation of weekly returns.
- **Purpose:** Measures the variability of returns, providing insights into the risk associated with trading during specific weeks.
4. **Kelly Ratio**
- **Definition:** A mathematical formula used to determine the optimal size of a series of bets to maximize the logarithmic growth of capital.
- **Purpose:** Balances potential returns against risks, guiding traders on the appropriate position size to take.
5. **Adjusted Kelly Fraction**
- **Definition:** The Kelly Ratio adjusted based on user-defined risk tolerance and external factors like Federal Reserve (Fed) stance.
- **Purpose:** Personalizes the Kelly Criterion to align with individual risk preferences and market conditions, enhancing risk management.
6. **Position Size ($)**
- **Definition:** The calculated amount to invest based on the Adjusted Kelly Fraction.
- **Purpose:** Ensures that position sizes are aligned with risk management strategies, preventing overexposure to any single trade.
7. **Max Drawdown (%)**
- **Definition:** The maximum observed loss from a peak to a trough of a portfolio, before a new peak is attained.
- **Purpose:** Assesses the worst-case scenario for losses, crucial for understanding potential capital erosion.
### Functionality and Benefits
- **Weekly Data Aggregation:** Aggregates weekly returns across multiple years to provide a robust statistical foundation for decision-making.
- **Quarterly Filtering:** Allows users to filter weeks based on quarters, enabling seasonality analysis and tailored strategies aligned with specific timeframes.
- **Dynamic Risk Adjustment:** Incorporates the Dynamic Kelly Criterion to adjust position sizes in real-time based on changing risk profiles and market conditions.
- **User-Friendly Visualization:** Presents all essential metrics in an organized Summary Table, facilitating quick and informed decision-making.
## The Origin of the Kelly Criterion and Addressing Its Limitations
### Understanding the Kelly Criterion
The Kelly Criterion, developed by John L. Kelly Jr. in 1956, is a formula used to determine the optimal size of a series of bets to maximize the long-term growth of capital. The formula considers both the probability of winning and the payout ratio, balancing potential returns against the risk of loss.
**Kelly Formula:**
\
Where:
- \( b \) = the net odds received on the wager ("b to 1")
- \( p \) = probability of winning
- \( q \) = probability of losing ( \( q = 1 - p \) )
### The Risk of Ruin
While the Kelly Criterion is effective in optimizing growth, it carries inherent risks:
- **Overbetting:** If the input probabilities or payout ratios are misestimated, the Kelly Criterion can suggest overly aggressive position sizes, leading to significant losses.
- **Assumption of Constant Probabilities:** The criterion assumes that probabilities remain constant, which is rarely the case in dynamic markets.
- **Ignoring External Factors:** Traditional Kelly implementations do not account for external factors such as Federal Reserve rates, margin requirements, or market volatility, which can impact risk and returns.
### Addressing Traditional Limitations
Recognizing these limitations, the **Weekly Seasonality with Dynamic Kelly Criterion Indicator** introduces enhancements to the traditional Kelly approach:
- **Incorporation of Fed Stance:** Adjusts the Kelly Fraction based on the current stance of the Federal Reserve (neutral, dovish, or hawkish), reflecting broader economic conditions that influence market behavior.
- **Margin and Leverage Considerations:** Accounts for margin rates and leverage, ensuring that position sizes remain within manageable risk parameters.
- **Dynamic Adjustments:** Continuously updates position sizes based on real-time risk assessments and probabilistic analyses, mitigating the risk of ruin associated with static Kelly implementations.
## How the Indicator Aids Traders
### For Short-Term Swing Traders
Short-term swing traders thrive on capitalizing over weekly price movements. The indicator aids them by:
- **Identifying Favorable Weeks:** Highlights weeks with above-average returns and favorable volatility, guiding entry and exit points.
- **Optimal Position Sizing:** Utilizes the Adjusted Kelly Fraction to determine the optimal amount to invest, balancing potential returns with risk exposure.
- **Probabilistic Insights:** Provides metrics like Positive Percentage (%) and Kelly Ratio to assess the likelihood of favorable outcomes, enhancing decision-making.
### For Long-Term Tax-Free Investors
This is effectively a drop-in replacement for DCA which uses fixed position size that doesn't change based on market conditions, as a result, it's like catching multiple falling knifes by the blade and smiling with blood on your hand... I don't know about you, but I'd rather juggle by the hilt and look like an actual professional...
Long-term investors, especially those seeking tax-free positions (e.g., through retirement accounts), benefit from:
- **Consistent Risk Management:** Ensures that position sizes are aligned with long-term capital preservation strategies.
- **Seasonality Analysis:** Allows for strategic positioning based on historical performance trends across different weeks and quarters.
- **Dynamic Adjustments:** Adapts to changing market conditions, maintaining optimal risk profiles over extended investment horizons.
### Developers
Please double check the logic and functionality because I think there are a few issue and I need to crowd source solutions and be responsible about the code I publish. If you have corrections, please DM me or leave a respectful comment.
I want to publish this by the end of the year and include other things like highlighting triple witching weeks, adding columns for volume % stats, VaR and CVaR, alpha, beta (to see the seasonal alpha and beta based off a benchmark ticker and risk free rate ticker and other little goodies.
TASC 2024.10 Adaptive Oscillator Threshold█ OVERVIEW
This script introduces a more dynamic approach to generating trading signals using the RSI indicator and a threshold that adapts to price trends and dispersion. This methodology comes from Francesco Bufi's article "Overbought/Oversold Oscillators: Useless Or Just Misused" from the October 2024 edition of TASC's Traders' Tips .
█ CONCEPTS
According to Francesco Bufi's observations, an oscillator-based buy signal should have a threshold that varies with the trend direction: higher during uptrends and lower during downtrends. Additionally, the level should decrease as the distance from the price to its mean increases to reduce signals in volatile conditions. Accordingly, Bufi proposes a formula for an adaptive buy level whose value is proportional to the trend (linear regression slope) and inversely proportional to the typical distance between price and its mean (standard deviation). Traders can apply this method to any oscillator to add adaptivity without modifying the oscillator's calculations, as it's simply an adaptive technique for interpreting the calculated values.
This script demonstrates the application of Bufi's Adaptive Threshold (BAT) in a simple RSI-based strategy and allows users to compare its performance to the traditional fixed-threshold approach. Bufi's observations suggest that using the BAT instead of a static threshold can help improve the backtest performance of oscillator-based systems.
█ DISCLAIMER
This strategy script educates users on the trading systems outlined by the TASC article. By default, it uses 10% of equity as the order size and a slippage amount of 5 ticks. Traders should adjust these settings and the commission amount when using this script.
Futures Risk CalculatorFutures Risk Calculator Script - Description
The Futures Risk Calculator (FRC) is a comprehensive tool designed to help traders effectively manage risk when trading futures contracts. This script allows users to calculate risk/reward ratios directly on the chart by specifying their entry price and stop loss. It's an ideal tool for futures traders who want to quantify their potential losses and gains with precision, based on their trading account size and the number of contracts they trade.
What the Script Does:
1. Risk and Reward Calculation:
The script calculates your total risk in dollars and as a percentage of your account size based on the entry and stop-loss prices you input.
It also calculates two key levels where potential reward (Take Profit 1 and Take Profit 2) can be expected, helping you assess the reward-to-risk ratio for any trade.
2. Customizable Settings:
You can specify the size of your trading account (available $ for Futures trading) and the number of futures contracts you're trading. This allows for tailored risk management that reflects your exact trading conditions.
3. Live Chart Integration:
You add the script to your chart after opening a futures chart in TradingView. Simply click on the chart to set your Entry Price and Stop Loss. The script will instantly calculate and display the risk and reward levels based on the points you set.
Adjusting the entry and stop-loss points later is just as easy: drag and drop the levels directly on the chart, and the risk and reward calculations update automatically.
4. Futures Contract Support:
The script is pre-configured with a list of popular futures symbols (like ES, NQ, CL, GC, and more). If your preferred futures contract isn’t in the list, you can easily add it by modifying the script.
The script uses each symbol’s point value to ensure precise risk calculations, providing you with an accurate dollar risk and potential reward based on the specific contract you're trading.
How to Use the Script:
1. Apply the Script to a Futures Chart:
Open a futures contract chart in TradingView.
Add the Futures Risk Calculator (FRC) script as an indicator.
2. Set Entry and Stop Loss:
Upon applying the script, it will prompt you to select your entry price by clicking the chart where you plan to enter the market.
Next, click on the chart to set your stop-loss level.
The script will then calculate your total risk in dollars and as a percentage of your account size.
3. View Risk, Reward, and (Take Profit):
You can immediately see visual lines representing your entry, stop loss, and the calculated reward-to-risk ratio levels (Take Profit 1 and Take Profit 2).
If you want to adjust the entry or stop loss after plotting them, simply move the points on
the chart, and the script will recalculate everything for you.
4. Configure Account and Contracts:
In the script settings, you can enter your account size and adjust the number of contracts you are trading. These inputs allow the script to calculate risk in monetary terms and as a percentage, making it easier to manage your risk effectively.
5. Understand the Information in the Table:
Once you apply the script, a table will appear in the top-right corner of your chart, providing you with key information about your futures contract and the trade setup. Here's what each field represents:
Account Size: Displays your total account value, which you can set in the script's settings.
Future: Shows the selected futures symbol, along with key details such as its tick size and point value. This gives you a clear understanding of how much one point or tick is worth in dollar terms.
Entry Price: The exact price at which you plan to enter the trade, displayed in green.
Stop Loss Price: The price level where you plan to exit the trade if the market moves against you, shown in red.
Contracts: The number of futures contracts you are trading, which you can adjust in the settings.
Risk: Highlighted in orange, this field shows your total risk in dollars, as well as the percentage risk based on your account size. This is a crucial value to help you stay within your risk tolerance and manage your trades effectively.
RSI Trend Following StrategyOverview
The RSI Trend Following Strategy utilizes Relative Strength Index (RSI) to enter the trade for the potential trend continuation. It uses Stochastic indicator to check is the price is not in overbought territory and the MACD to measure the current price momentum. Moreover, it uses the 200-period EMA to filter the counter trend trades with the higher probability. The strategy opens only long trades.
Unique Features
Dynamic stop-loss system: Instead of fixed stop-loss level strategy utilizes average true range (ATR) multiplied by user given number subtracted from the position entry price as a dynamic stop loss level.
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Two layers trade filtering system: Strategy utilizes MACD and Stochastic indicators measure the current momentum and overbought condition and use 200-period EMA to filter trades against major trend.
Trailing take profit level: After reaching the trailing profit activation level script activates the trailing of long trade using EMA. More information in methodology.
Wide opportunities for strategy optimization: Flexible strategy settings allows users to optimize the strategy entries and exits for chosen trading pair and time frame.
Methodology
The strategy opens long trade when the following price met the conditions:
RSI is above 50 level.
MACD line shall be above the signal line
Both lines of Stochastic shall be not higher than 80 (overbought territory)
Candle’s low shall be above the 200 period EMA
When long trade is executed, strategy set the stop-loss level at the price ATR multiplied by user-given value below the entry price. This level is recalculated on every next candle close, adjusting to the current market volatility.
At the same time strategy set up the trailing stop validation level. When the price crosses the level equals entry price plus ATR multiplied by user-given value script starts to trail the price with trailing EMA(by default = 20 period). If price closes below EMA long trade is closed. When the trailing starts, script prints the label “Trailing Activated”.
Strategy settings
In the inputs window user can setup the following strategy settings:
ATR Stop Loss (by default = 1.75)
ATR Trailing Profit Activation Level (by default = 2.25)
MACD Fast Length (by default = 12, period of averaging fast MACD line)
MACD Fast Length (by default = 26, period of averaging slow MACD line)
MACD Signal Smoothing (by default = 9, period of smoothing MACD signal line)
Oscillator MA Type (by default = EMA, available options: SMA, EMA)
Signal Line MA Type (by default = EMA, available options: SMA, EMA)
RSI Length (by default = 14, period for RSI calculation)
Trailing EMA Length (by default = 20, period for EMA, which shall be broken close the trade after trailing profit activation)
Justification of Methodology
This trading strategy is designed to leverage a combination of technical indicators—Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), Stochastic Oscillator, and the 200-period Exponential Moving Average (EMA)—to determine optimal entry points for long trades. Additionally, the strategy uses the Average True Range (ATR) for dynamic risk management to adapt to varying market conditions. Let's look in details for which purpose each indicator is used for and why it is used in this combination.
Relative Strength Index (RSI) is a momentum indicator used in technical analysis to measure the speed and change of price movements in a financial market. It helps traders identify whether an asset is potentially overbought (overvalued) or oversold (undervalued), which can indicate a potential reversal or continuation of the current trend.
How RSI Works? RSI tracks the strength of recent price changes. It compares the average gains and losses over a specific period (usually 14 periods) to assess the momentum of an asset. Average gain is the average of all positive price changes over the chosen period. It reflects how much the price has typically increased during upward movements. Average loss is the average of all negative price changes over the same period. It reflects how much the price has typically decreased during downward movements.
RSI calculates these average gains and losses and compares them to create a value between 0 and 100. If the RSI value is above 70, the asset is generally considered overbought, meaning it might be due for a price correction or reversal downward. Conversely, if the RSI value is below 30, the asset is considered oversold, suggesting it could be poised for an upward reversal or recovery. RSI is a useful tool for traders to determine market conditions and make informed decisions about entering or exiting trades based on the perceived strength or weakness of an asset's price movements.
This strategy uses RSI as a short-term trend approximation. If RSI crosses over 50 it means that there is a high probability of short-term trend change from downtrend to uptrend. Therefore RSI above 50 is our first trend filter to look for a long position.
The MACD (Moving Average Convergence Divergence) is a popular momentum and trend-following indicator used in technical analysis. It helps traders identify changes in the strength, direction, momentum, and duration of a trend in an asset's price.
The MACD consists of three components:
MACD Line: This is the difference between a short-term Exponential Moving Average (EMA) and a long-term EMA, typically calculated as: MACD Line = 12 period EMA − 26 period EMA
Signal Line: This is a 9-period EMA of the MACD Line, which helps to identify buy or sell signals. When the MACD Line crosses above the Signal Line, it can be a bullish signal (suggesting a buy); when it crosses below, it can be a bearish signal (suggesting a sell).
Histogram: The histogram shows the difference between the MACD Line and the Signal Line, visually representing the momentum of the trend. Positive histogram values indicate increasing bullish momentum, while negative values indicate increasing bearish momentum.
This strategy uses MACD as a second short-term trend filter. When MACD line crossed over the signal line there is a high probability that uptrend has been started. Therefore MACD line above signal line is our additional short-term trend filter. In conjunction with RSI it decreases probability of following false trend change signals.
The Stochastic Indicator is a momentum oscillator that compares a security's closing price to its price range over a specific period. It's used to identify overbought and oversold conditions. The indicator ranges from 0 to 100, with readings above 80 indicating overbought conditions and readings below 20 indicating oversold conditions.
It consists of two lines:
%K: The main line, calculated using the formula (CurrentClose−LowestLow)/(HighestHigh−LowestLow)×100 . Highest and lowest price taken for 14 periods.
%D: A smoothed moving average of %K, often used as a signal line.
This strategy uses stochastic to define the overbought conditions. The logic here is the following: we want to avoid long trades in the overbought territory, because when indicator reaches it there is a high probability that the potential move is gonna be restricted.
The 200-period EMA is a widely recognized indicator for identifying the long-term trend direction. The strategy only trades in the direction of this primary trend to increase the probability of successful trades. For instance, when the price is above the 200 EMA, only long trades are considered, aligning with the overarching trend direction.
Therefore, strategy uses combination of RSI and MACD to increase the probability that price now is in short-term uptrend, Stochastic helps to avoid the trades in the overbought (>80) territory. To increase the probability of opening long trades in the direction of a main trend and avoid local bounces we use 200 period EMA.
ATR is used to adjust the strategy risk management to the current market volatility. If volatility is low, we don’t need the large stop loss to understand the there is a high probability that we made a mistake opening the trade. User can setup the settings ATR Stop Loss and ATR Trailing Profit Activation Level to realize his own risk to reward preferences, but the unique feature of a strategy is that after reaching trailing profit activation level strategy is trying to follow the trend until it is likely to be finished instead of using fixed risk management settings. It allows sometimes to be involved in the large movements.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2024.08.01. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 30%
Maximum Single Position Loss: -3.94%
Maximum Single Profit: +15.78%
Net Profit: +1359.21 USDT (+13.59%)
Total Trades: 111 (36.04% win rate)
Profit Factor: 1.413
Maximum Accumulated Loss: 625.02 USDT (-5.85%)
Average Profit per Trade: 12.25 USDT (+0.40%)
Average Trade Duration: 40 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 2h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
VRS (Vegas Reversal Strategy)It is based on the reversal of the price after an accentuated volatility of the previous day. It is tested only on BTC, TF Day, and has an activation value equal to a spike of minimum 2.4% amplitude, a value that I have left in the settings free to be modified if it is found valid for other assets.
In the settings you can change how many of the latest longs or shorts I want to view in the past, colors and various aesthetics.
When the system detects a spike at the end of the day from 2.4% onwards it will signal the direction of Reversal, generating the 3 TP, dotted lines.
Entry into the market must be done at the close of the candle day, unfortunately at night time if you want to enter on the tick.
Stop above/below the spike that generated the condition.
If the Day2 candle closes FULL inside the spike, immediate and early closing of the operation.
There cannot be two consecutive Day events: if you are Long or Short and have taken a stop on the next candle, even if the latter generates another entry, this must not be activated.
TP 1 and 2 are both mandatory at 33% of the position, TP3, based on the current movement, can be considered to be left to run to the bitter end or in any case to structuring confirmations of a slowdown in the price.
Upon reaching TP1 it is mandatory to move the STOP to even.
In the event of the presence of extremely strong directional movements, for example Long direction, an opposite activation, Short, must be done but with reduced capital, on the contrary an activation in the same direction as the trend movement can be done with a surcharge. Always pay attention to Money Management and Risk Management.
Always manage Risk and Money Management in an adequate, technical and sustainable manner in relation to your capital. A fair exposure per transaction is between 1% and 2% of the capital.
4C Data Table SuiteOverview
The 4C Data Table Suite is a versatile TradingView indicator, designed for traders who focus on the critical role of prior bar levels in their strategy. By highlighting the high and low points of previous bars, this tool aids in pinpointing crucial support and resistance zones, which often act as psychological triggers for market participants. The unique feature of this indicator is its dual-color coding: it colors bars green for bullish closes and red for bearish closes in the Prior Bar Table, and for the current timeframe, it highlights whether the current price is above (green) or below (red) these levels.
Concepts
1 — This sophisticated indicator is not just about visual cues; it provides a rich tableau of data including:
• Current timeframe countdown to bar close
• GMT-adjusted clock for precise trade timing
• Real-time updates on market internals and volatility measures such as the NYSE TICK and ATR
2 — The configuration is highly customizable, allowing users to:
• Adjust the table's positioning and text size
• Choose color settings for text, background, and borders to suit their visual preference
• Toggle the display of various data components based on their trading needs
3 — The Prior Bar Table is particularly valuable for:
• Traders using prior bar levels as triggers for trade entries and exits
• Quick visual assessment of market sentiment
• Real-time decision-making supported by dynamic color coding based on current price movements relative to prior highs and lows
Features
• Prior Bar Levels Table: Visualizes high and low levels of prior bars with intuitive color coding, aiding traders in assessing market trends.
• Dynamic Color Coding: Updates colors based on the closure of the previous bar and current price positions relative to the past bar's highs and lows.
• Comprehensive Market Data: Includes a suite of essential data such as market internals, the prior bar’s range, and the latest price information.
• Customizable Visuals: Offers extensive options for customizing the appearance and data presented, ensuring the tool fits seamlessly into any trader's strategy.
How to Use
1. To add the indicator, search for "4C Data Table Suite" under indicators on TradingView and apply it to your chart.
2. Navigate to the indicator settings to customize the display properties, including table position, color schemes, and which data points to display.
3. Utilize the toggles within the "Prior Bar Levels Table" settings to adjust the visibility and behavior of the table, depending on your trading approach.
Limitations
• This indicator is optimized for use on time-based charts and may not perform as intended on tick charts or non-standard timeframes.
• The color coding is based on the closure of bars, which may not always reflect intrabar movements, potentially affecting real-time decision-making in highly volatile markets.
Notes
• Ensure your TradingView interface is set to the correct timezone settings to align the GMT clock accurately.
• The dynamic color updates are designed to provide at-a-glance insights but should be used in conjunction with other analysis tools for best results.
*If you find that the indicator is blocking some of the candles at the bottom of the screen, go to (Chart) Settings, Canvas, and then adjust the Bottom/Top margin by increasing the % Amount.
Thanks
Special thanks to the TradingView community and developers whose feedback and insights have helped refine the functionalities of the 4C Data Table Suite. Your collaborative spirit is what makes continuous improvement possible.
MACD with 1D Stochastic Confirmation Reversal StrategyOverview
The MACD with 1D Stochastic Confirmation Reversal Strategy utilizes MACD indicator in conjunction with 1 day timeframe Stochastic indicators to obtain the high probability short-term trend reversal signals. The main idea is to wait until MACD line crosses up it’s signal line, at the same time Stochastic indicator on 1D time frame shall show the uptrend (will be discussed in methodology) and not to be in the oversold territory. Strategy works on time frames from 30 min to 4 hours and opens only long trades.
Unique Features
Dynamic stop-loss system: Instead of fixed stop-loss level strategy utilizes average true range (ATR) multiplied by user given number subtracted from the position entry price as a dynamic stop loss level.
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Higher time frame confirmation: Strategy utilizes 1D Stochastic to establish the major trend and confirm the local reversals with the higher probability.
Trailing take profit level: After reaching the trailing profit activation level scrip activate the trailing of long trade using EMA. More information in methodology.
Methodology
The strategy opens long trade when the following price met the conditions:
MACD line of MACD indicator shall cross over the signal line of MACD indicator.
1D time frame Stochastic’s K line shall be above the D line.
1D time frame Stochastic’s K line value shall be below 80 (not overbought)
When long trade is executed, strategy set the stop-loss level at the price ATR multiplied by user-given value below the entry price. This level is recalculated on every next candle close, adjusting to the current market volatility.
At the same time strategy set up the trailing stop validation level. When the price crosses the level equals entry price plus ATR multiplied by user-given value script starts to trail the price with EMA. If price closes below EMA long trade is closed. When the trailing starts, script prints the label “Trailing Activated”.
Strategy settings
In the inputs window user can setup the following strategy settings:
ATR Stop Loss (by default = 3.25, value multiplied by ATR to be subtracted from position entry price to setup stop loss)
ATR Trailing Profit Activation Level (by default = 4.25, value multiplied by ATR to be added to position entry price to setup trailing profit activation level)
Trailing EMA Length (by default = 20, period for EMA, when price reached trailing profit activation level EMA will stop out of position if price closes below it)
User can choose the optimal parameters during backtesting on certain price chart, in our example we use default settings.
Justification of Methodology
This strategy leverages 2 time frames analysis to have the high probability reversal setups on lower time frame in the direction of the 1D time frame trend. That’s why it’s recommended to use this strategy on 30 min – 4 hours time frames.
To have an approximation of 1D time frame trend strategy utilizes classical Stochastic indicator. The Stochastic Indicator is a momentum oscillator that compares a security's closing price to its price range over a specific period. It's used to identify overbought and oversold conditions. The indicator ranges from 0 to 100, with readings above 80 indicating overbought conditions and readings below 20 indicating oversold conditions.
It consists of two lines:
%K: The main line, calculated using the formula (CurrentClose−LowestLow)/(HighestHigh−LowestLow)×100 . Highest and lowest price taken for 14 periods.
%D: A smoothed moving average of %K, often used as a signal line.
Strategy logic assumes that on 1D time frame it’s uptrend in %K line is above the %D line. Moreover, we can consider long trade only in %K line is below 80. It means that in overbought state the long trade will not be opened due to higher probability of pullback or even major trend reversal. If these conditions are met we are going to our working (lower) time frame.
On the chosen time frame, we remind you that for correct work of this strategy you shall use 30min – 4h time frames, MACD line shall cross over it’s signal line. The MACD (Moving Average Convergence Divergence) is a popular momentum and trend-following indicator used in technical analysis. It helps traders identify changes in the strength, direction, momentum, and duration of a trend in a stock's price.
The MACD consists of three components:
MACD Line: This is the difference between a short-term Exponential Moving Average (EMA) and a long-term EMA, typically calculated as: MACD Line=12-period EMA−26-period
Signal Line: This is a 9-period EMA of the MACD Line, which helps to identify buy or sell signals. When the MACD Line crosses above the Signal Line, it can be a bullish signal (suggesting a buy); when it crosses below, it can be a bearish signal (suggesting a sell).
Histogram: The histogram shows the difference between the MACD Line and the Signal Line, visually representing the momentum of the trend. Positive histogram values indicate increasing bullish momentum, while negative values indicate increasing bearish momentum.
In our script we are interested in only MACD and signal lines. When MACD line crosses signal line there is a high chance that short-term trend reversed to the upside. We use this strategy on 45 min time frame.
ATR is used to adjust the strategy risk management to the current market volatility. If volatility is low, we don’t need the large stop loss to understand the there is a high probability that we made a mistake opening the trade. User can setup the settings ATR Stop Loss and ATR Trailing Profit Activation Level to realize his own risk to reward preferences, but the unique feature of a strategy is that after reaching trailing profit activation level strategy is trying to follow the trend until it is likely to be finished instead of using fixed risk management settings. It allows sometimes to be involved in the large movements.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2024.08.01. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 30%
Maximum Single Position Loss: -4.79%
Maximum Single Profit: +20.14%
Net Profit: +2361.33 USDT (+44.72%)
Total Trades: 123 (44.72% win rate)
Profit Factor: 1.623
Maximum Accumulated Loss: 695.80 USDT (-5.48%)
Average Profit per Trade: 19.20 USDT (+0.59%)
Average Trade Duration: 30 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe between 30 min and 4 hours and chart (optimal performance observed on 45 min BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
MACD Trail | Flux Charts💎 GENERAL OVERVIEW
Introducing our new MACD Trail indicator! Moving average convergence/divergence (MACD) is a well-known indicator among traders. It's a trend-following indicator that uses the relationship between two exponential moving averages (EMAs). This indicator aims to use MACD to generate a trail that follows the current price of the ticker, which can act as a support / resistance zone. More info about the process in the "How Does It Work" section.
Features of the new MACD Trail Indicator :
A Trail Generated Using MACD Calculation
Customizable Algorithm
Customizable Styling
📌 HOW DOES IT WORK ?
First of all, this indicator calculates the current MACD of the ticker using the user's input as settings. Let X = MACD Length setting ;
MACD ~= X Period EMA - (X * 2) Period EMA
Then, two MACD Trails are generated, one being bullish and other being bearish. Let ATR = 30 period ATR (Average True Range)
Bullish MACD Trail = Current Price + MACD - (ATR * 1.75)
Bearish MACD Trail = Current Price + MACD + (ATR * 1.75)
The indicator starts by rendering only the Bullish MACD Trail. Then if it's invalidated (candlestick closes below the trail) it switches to Bearish MACD Trail. The MACD trail switches between bullish & bearish as they get invalidated.
The trail type may give a hint about the current trend of the price action. The trail itself also can act as a support / resistance zone, here is an example :
🚩 UNIQUENESS
While MACD is one of the most used indicators among traders, this indicator aims to add another functionality to it by rendering a trail based on it. This trail may act as a support / resistance zone as described above, and gives a glimpse about the current trend. The indicator also has custom MACD Length and smoothing options, as well as various style options.
⚙️ SETTINGS
1. General Configuration
MACD Length -> This setting adjusts the EMA periods used in MACD calculation. Increasing this setting will make MACD more responseive to longer trends, while decreasing it may help with detection of shorter trends.
Smoothing -> The smoothing of the MACD Trail. Increasing this setting will help smoothen out the MACD Trail line, but it can also make it less responsive to the latest changes.
Double CCI Confirmed Hull Moving Average Reversal StrategyOverview
The Double CCI Confirmed Hull Moving Average Strategy utilizes hull moving average (HMA) in conjunction with two commodity channel index (CCI) indicators: the slow and fast to increase the probability of entering when the short and mid-term uptrend confirmed. The main idea is to wait until the price breaks the HMA while both CCI are showing that the uptrend has likely been already started. Moreover, strategy uses exponential moving average (EMA) to trail the price when it reaches the specific level. The strategy opens only long trades.
Unique Features
Dynamic stop-loss system: Instead of fixed stop-loss level strategy utilizes average true range (ATR) multiplied by user given number subtracted from the position entry price as a dynamic stop loss level.
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Double trade setup confirmation: Strategy utilizes two different period CCI indicators to confirm the breakouts of HMA.
Trailing take profit level: After reaching the trailing profit activation level scrip activate the trailing of long trade using EMA. More information in methodology.
Methodology
The strategy opens long trade when the following price met the conditions:
Short-term period CCI indicator shall be above 0.
Long-term period CCI indicator shall be above 0.
Price shall cross the HMA and candle close above it with the same candle
When long trade is executed, strategy set the stop-loss level at the price ATR multiplied by user-given value below the entry price. This level is recalculated on every next candle close, adjusting to the current market volatility.
At the same time strategy set up the trailing stop validation level. When the price crosses the level equals entry price plus ATR multiplied by user-given value script starts to trail the price with EMA. If price closes below EMA long trade is closed. When the trailing starts, script prints the label “Trailing Activated”.
Strategy settings
In the inputs window user can setup the following strategy settings:
ATR Stop Loss (by default = 1.75)
ATR Trailing Profit Activation Level (by default = 2.25)
CCI Fast Length (by default = 25, used for calculation short term period CCI
CCI Slow Length (by default = 50, used for calculation long term period CCI)
Hull MA Length (by default = 34, period of HMA, which shall be broken to open trade)
Trailing EMA Length (by default = 20)
User can choose the optimal parameters during backtesting on certain price chart.
Justification of Methodology
Before understanding why this particular combination of indicator has been chosen let's briefly explain what is CCI and HMA.
The Commodity Channel Index (CCI) is a momentum-based technical indicator used in trading to measure a security's price relative to its average price over a given period. Developed by Donald Lambert in 1980, the CCI is primarily used to identify cyclical trends in a security, helping traders to spot potential buying or selling opportunities.
The CCI formula is:
CCI = (Typical Price − SMA) / (0.015 × Mean Deviation)
Typical Price (TP): This is calculated as the average of the high, low, and closing prices for the period.
Simple Moving Average (SMA): This is the average of the Typical Prices over a specific number of periods.
Mean Deviation: This is the average of the absolute differences between the Typical Price and the SMA.
The result is a value that typically fluctuates between +100 and -100, though it is not bounded and can go higher or lower depending on the price movement.
The Hull Moving Average (HMA) is a type of moving average that was developed by Alan Hull to improve upon the traditional moving averages by reducing lag while maintaining smoothness. The goal of the HMA is to create an indicator that is both quick to respond to price changes and less prone to whipsaws (false signals).
How the Hull Moving Average is Calculated?
The Hull Moving Average is calculated using the following steps:
Weighted Moving Average (WMA): The HMA starts by calculating the Weighted Moving Average (WMA) of the price data over a period square root of n (sqrt(n))
Speed Adjustment: A WMA is then calculated for half of the period n/2, and this is multiplied by 2 to give more weight to recent prices.
Lag Reduction: The WMA of the full period n is subtracted from the doubled n/2 WMA.
Final Smoothing: To smooth the result and reduce noise, a WMA is calculated for the square root of the period n.
The formula can be represented as:
HMA(n) = WMA(WMA(n/2) × 2 − WMA(n), sqrt(n))
The Weighted Moving Average (WMA) is a type of moving average that gives more weight to recent data points, making it more responsive to recent price changes than a Simple Moving Average (SMA). In a WMA, each data point within the selected period is multiplied by a weight, with the most recent data receiving the highest weight. The sum of these weighted values is then divided by the sum of the weights to produce the WMA.
This strategy leverages HMA of user given period as a critical level which shall be broken to say that probability of trend change to the upside increased. HMA reacts faster than EMA or SMA to the price change, that’s why it increases chances to enter new trade earlier. Long-term period CCI helps to have an approximation of mid-term trend. If it’s above 0 the probability of uptrend increases. Short-period CCI allows to have an approximation of short-term trend reversal from down to uptrend. This approach increases chances to have a long trade setup in the direction of mid-term trend when the short-term trend starts to reverse.
ATR is used to adjust the strategy risk management to the current market volatility. If volatility is low, we don’t need the large stop loss to understand the there is a high probability that we made a mistake opening the trade. User can setup the settings ATR Stop Loss and ATR Trailing Profit Activation Level to realize his own risk to reward preferences, but the unique feature of a strategy is that after reaching trailing profit activation level strategy is trying to follow the trend until it is likely to be finished instead of using fixed risk management settings. It allows sometimes to be involved in the large movements. It’s also important to make a note, that script uses HMA to enter the trade, but for trailing it leverages EMA. It’s used because EMA has no such fast reaction to price move which increases probability not to be stopped out from any significant uptrend move.
Backtest Results
Operating window: Date range of backtests is 2022.07.01 - 2024.08.01. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 100%
Maximum Single Position Loss: -4.67%
Maximum Single Profit: +19.66%
Net Profit: +14897.94 USDT (+148.98%)
Total Trades: 104 (36.54% win rate)
Profit Factor: 2.312
Maximum Accumulated Loss: 1302.66 USDT (-9.58%)
Average Profit per Trade: 143.25 USDT (+0.96%)
Average Trade Duration: 34 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 2h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
Three Anchored Moving Averages (VWAP / SMA / EMA)
This indicator allows users to anchor three types of moving averages (Simple Moving Average (SMA), Exponential Moving Average (EMA), and Volume Weighted Average Price (VWAP)) to specific points in time (anchor points)
Key Features:
Select from three Moving Average Types:
Simple Moving Average (SMA): Averages the closing prices over a specified period.
Exponential Moving Average (EMA): Gives more weight to recent prices, making it more responsive to new information.
Volume Weighted Average Price (VWAP): Averages the price weighted by volume, useful for understanding the average price at which the asset has traded over a period.
Up to Three Anchor Points:
Users can set up to three different anchor points to calculate the moving averages from specific dates and times. This allows for analysis of price action starting from significant points or specific events. For example, you can anchor to the low and high of a move to identify key levels or to points where the price takes off from a previous anchored MA.
Customisable Sentiment Options:
Each anchor point can be associated with a sentiment input (Auto, Bull, Bear, None), which influences if the MAs are displayed as lines or zones/bands:
Auto: Automatically determines the sentiment based on whether anchor points are on pivot highs and lows. If anchored to a pivot high, the system will assume a bearish sentiment and display a red band or zone between the MA OHLC4 and High. Anchoring to a pivot low will display a green band (OHLC4 - Low).
Bull: Forces a bullish sentiment (Green Band - OHLC4 to Low)
Bear: Forces a bearish sentiment (Red Band - OHLC4 to High)
None: Ignores sentiment and displays a single line (OHLC4)
Chart Matching:
The indicator includes an option to display the moving averages only if the chart symbol matches a specified ticker. This feature ensures that the indicator is relevant to the specific asset being analysed.
How to Use the Indicator:
1. Set Anchor Points: When added to your chart, select three anchor points by point and click. If you only wish to anchor to a single point, click on that point three times and disable the other two in settings once the indicator is applied.
2. Select Moving Average Type: Choose between SMA, EMA, or VWAP using the dropdown menu. EMAs are the most responsive.
3. Enable/Disable Anchor Points: Use the checkboxes to enable or disable each anchor point.
4. Select Sentiment Type: Choose between Auto, Bull, Bear, or None.
5. Chart Matching: Optionally, specify a chart symbol to restrict the indicator's display to that particular asset.
6. Interpret the Plots: The indicator plots the high, mid, and low values of the selected moving average type from each anchor point. The fills between these plots help identify potential support and resistance zones. These should be used as points of interest for pullback reversals or potential continuation if the price breaks through.
Practical Applications:
Trend Analysis: Identify the overall trend direction from specific historical points.
Support and Resistance: Determine key dynamic support and resistance levels based on anchored moving averages.
Event-Based Analysis: Anchor the moving averages to significant events (e.g., earnings releases, economic data) to study their impact on price trends.
Multi Timeframe Analysis: Higher Timeframe Anchors can be used to identify longer term trend analysis. Switching to a lower timeframe for execution triggers at these points wont distort the MA levels as they are anchored to a specific point in time
Intraday or Swing Trading: trend analysis using anchor points can be used for any style of trading (Intraday / Swing / Invest). Use anchored levels as points of interest and wait for hints in price action to try and catch the next move.
Bollinger Bands Enhanced StrategyOverview
The common practice of using Bollinger bands is to use it for building mean reversion or squeeze momentum strategies. In the current script Bollinger Bands Enhanced Strategy we are trying to combine the strengths of both strategies types. It utilizes Bollinger Bands indicator to buy the local dip and activates trailing profit system after reaching the user given number of Average True Ranges (ATR). Also it uses 200 period EMA to filter trades only in the direction of a trend. Strategy can execute only long trades.
Unique Features
Trailing Profit System: Strategy uses user given number of ATR to activate trailing take profit. If price has already reached the trailing profit activation level, scrip will close long trade if price closes below Bollinger Bands middle line.
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Major Trend Filter: Strategy utilizes 100 period EMA to take trades only in the direction of a trend.
Flexible Risk Management: Users can choose number of ATR as a stop loss (by default = 1.75) for trades. This is flexible approach because ATR is recalculated on every candle, therefore stop-loss readjusted to the current volatility.
Methodology
First of all, script checks if currently price is above the 200-period exponential moving average EMA. EMA is used to establish the current trend. Script will take long trades on if this filtering system showing us the uptrend. Then the strategy executes the long trade if candle’s low below the lower Bollinger band. To calculate the middle Bollinger line, we use the standard 20-period simple moving average (SMA), lower band is calculated by the substruction from middle line the standard deviation multiplied by user given value (by default = 2).
When long trade executed, script places stop-loss at the price level below the entry price by user defined number of ATR (by default = 1.75). This stop-loss level recalculates at every candle while trade is open according to the current candle ATR value. Also strategy set the trailing profit activation level at the price above the position average price by user given number of ATR (by default = 2.25). It is also recalculated every candle according to ATR value. When price hit this level script plotted the triangle with the label “Strong Uptrend” and start trail the price at the middle Bollinger line. It also started to be plotted as a green line.
When price close below this trailing level script closes the long trade and search for the next trade opportunity.
Risk Management
The strategy employs a combined and flexible approach to risk management:
It allows positions to ride the trend as long as the price continues to move favorably, aiming to capture significant price movements. It features a user-defined ATR stop loss parameter to mitigate risks based on individual risk tolerance. By default, this stop-loss is set to a 1.75*ATR drop from the entry point, but it can be adjusted according to the trader's preferences.
There is no fixed take profit, but strategy allows user to define user the ATR trailing profit activation parameter. By default, this stop-loss is set to a 2.25*ATR growth from the entry point, but it can be adjusted according to the trader's preferences.
Justification of Methodology
This strategy leverages Bollinger bangs indicator to open long trades in the local dips. If price reached the lower band there is a high probability of bounce. Here is an issue: during the strong downtrend price can constantly goes down without any significant correction. That’s why we decided to use 200-period EMA as a trend filter to increase the probability of opening long trades during major uptrend only.
Usually, Bollinger Bands indicator is using for mean reversion or breakout strategies. Both of them have the disadvantages. The mean reversion buys the dip, but closes on the return to some mean value. Therefore, it usually misses the major trend moves. The breakout strategies usually have the issue with too high buy price because to have the breakout confirmation price shall break some price level. Therefore, in such strategies traders need to set the large stop-loss, which decreases potential reward to risk ratio.
In this strategy we are trying to combine the best features of both types of strategies. Script utilizes ate ATR to setup the stop-loss and trailing profit activation levels. ATR takes into account the current volatility. Therefore, when we setup stop-loss with the user-given number of ATR we increase the probability to decrease the number of false stop outs. The trailing profit concept is trying to add the beat feature from breakout strategies and increase probability to stay in trade while uptrend is developing. When price hit the trailing profit activation level, script started to trail the price with middle line if Bollinger bands indicator. Only when candle closes below the middle line script closes the long trade.
Backtest Results
Operating window: Date range of backtests is 2020.10.01 - 2024.07.01. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 30%
Maximum Single Position Loss: -9.78%
Maximum Single Profit: +25.62%
Net Profit: +6778.11 USDT (+67.78%)
Total Trades: 111 (48.65% win rate)
Profit Factor: 2.065
Maximum Accumulated Loss: 853.56 USDT (-6.60%)
Average Profit per Trade: 61.06 USDT (+1.62%)
Average Trade Duration: 76 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 4h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
Ethereum ETF Tracker (EET)Get all the information you need about all the different Ethereum ETF.
With the Ethereum ETF Tracker, you can observe all possible Ethereum ETF data:
ETF name.
Ticker.
Price.
Volume.
Share of total ETF volume.
Fees.
Exchange.
Custodian.
At the bottom of the table, you'll find the ETHE Premium (and ETH per Share), and day's total volume.
In addition, you can see the volume for the different Exchanges, as well as for the different Custodians.
If you don't want to display these lines to save space, you can uncheck "Show Additional Data" in the indicator settings.
The Idea
The goal is to provide the community with a tool for tracking all Ethereum ETF data in a synthesized way, directly in your TradingView chart.
How to Use
Simply read the information in the table. You can hover above the Fees and Exchanges cells for more details.
The table takes space on the chart, you can remove the extra lines by unchecking "Show Additional Data" in the indicator settings or reduce text size by changing the "Table Text Size" parameter.
Aggregate volume can be displayed directly on the graph (this volume can be displayed on any asset, such as Ethereum itself). The display can be disabled in the settings.
TASC 2024.08 Volume Confirmation For A Trend System█ OVERVIEW
This script demonstrates the use of volume data to validate price movements based on the techniques Buff Pelz Dormeier discusses in his "Volume Confirmation For A Trend System" article from the August 2024 edition of TASC's Traders' Tips . It presents a trend-following system implementation that utilizes a combination of three indicators: the Average Directional Index (ADX), the Trend Thrust Indicator (TTI), and the Volume Price Confirmation Indicator (VPCI).
█ CONCEPTS
In his article, Buff Pelz Dormeier recounts his search for an optimal trend-following strategy enhanced with volume data, starting with a simple system combining the ADX , MACD , and OBV indicators. Even in these early tests, the author observed that the volume confirmation from OBV notably improved trading performance. Subsequently, the author replaced OBV with his VPCI, which considers the proportional weights of volume and price, to enhance the validation of trend momentum. Lastly, the author explored the inclusion of his TTI, a modified MACD that features volume-based enhancements, as a strategy component for improved trend-following performance.
According to the author's research, the ADX+TTI+VPCI system outperformed similar strategies he tested in the article, yielding significantly higher returns and enhanced perceived reliability. Because the system's design revolves around catching pronounced trends, it performs best with a portfolio of individual stocks. The author applies the system in the article by allocating 5% of the equity to long positions in S&P 500 components that meet the ADX+TTI+VPCI entry criteria (see the Calculations section below for details). He uses the proceeds from closing positions to enter new positions in other stocks meeting the screening criteria, holding any excess proceeds in cash.
█ CALCULATIONS
The TTI is similar to the MACD. Its calculation entails the following steps:
Calculate fast (short-term) and slow (long-term) volume-weighted moving averages (VWMAs).
Compute the volume multiple (VM) as the square of the ratio of the fast VWMA to the slow VWMA.
Adjust these averages by multiplying the fast VWMA by the VM and dividing the slow VWMA by the VM.
Calculate the difference between the adjusted VWMAs to determine the TTI value, and take the average of that series to determine the signal line value.
The VPCI utilizes differences and ratios between VWMAs and corresponding simple moving averages (SMAs) to provide an alternative volume-price confirmation tool. Its calculation is as follows:
Subtract the slow SMA from the VWMA of the same length to calculate the volume-price confirmation/contradiction (VPC) value.
Divide the fast VWMA by the corresponding fast SMA to determine the volume-price ratio (VPR).
Divide the short-term VWMA by the long-term VWMA to calculate the VM.
Compute the VPCI as the product of the VPC, VPR, and VM values.
The long entry criteria of the ADX+TTI+VPCI system are as follows:
The ADX is above 30.
The TTI crosses above its signal line.
The VPCI is above 0, confirming the trend.
Signals to close positions occur when the VPCI is below 0, indicating a contradiction .
NOTE: Unlike in the article, this script applies the ADX+TTI+VPCI system to one stock at a time , not a portfolio of S&P 500 constituents.
█ DISCLAIMER
This strategy script educates users on the trading system outlined by the TASC article. By default, it uses 10% of equity as the order size and a slippage amount of 5 ticks. Traders should adjust these settings and the commission amount when using this script.
Equal Highs and Lows {Reh's and Rel's }# Equal Highs and Lows {Reh's and Rel's} Indicator
## Overview
The "Equal Highs and Lows {Reh's and Rel's}" indicator is designed to identify and mark equal highs and lows on a price chart. It detects both exact and relative equal levels, draws lines connecting these levels, and optionally labels them. This tool can help traders identify potential support and resistance zones based on historical price levels.
## Key Features
1. **Exact and Relative Equality**: Detects both precise price matches and relative equality within a specified threshold.
2. **Customizable Appearance**: Allows users to adjust colors, line styles, and widths.
3. **Dynamic Line Management**: Automatically extends or removes lines based on ongoing price action.
4. **Labeling System**: Optional labels to identify types of equal levels (e.g., "Equal High", "REH/Equal High").
5. **Flexible Settings**: Adjustable parameters for lookback periods, maximum bars apart, and relative equality thresholds.
## User Inputs
### Appearance
- `lineColorHigh`: Color for lines marking equal highs (default: red)
- `lineColorLow`: Color for lines marking equal lows (default: green)
- `lineWidth`: Thickness of the lines (range: 1-5, default: 1)
- `lineStyle`: Style of the lines (options: Solid, Dash, Dotted)
- `showLabels`: Toggle to show or hide labels for equal highs and lows
### Settings
- `lookbackLength`: Number of bars to look back for finding equal highs and lows (default: 200)
- `maxBarsApart`: Maximum number of bars apart for equal highs/lows to be considered (range: 2-10, default: 5)
### Relative Equality
- `considerRelativeEquals`: Enable detection of relative equal highs and lows
- `thresholdIndex`: Maximum tick difference for relative equality in index instruments (range: 1-10, default: 2)
- `thresholdStocks`: Maximum tick difference for relative equality in stock instruments (range: 5-200, step: 5, default: 10)
## How It Works
The indicator scans historical price data to identify equal or relatively equal highs and lows. It draws lines connecting these levels and updates them as new price data comes in. Lines are extended if the level holds and removed if the price breaks through. The tool adapts to different market conditions by allowing adjustments to the equality thresholds for various instrument types.
## Practical Use
Traders can use this indicator to:
- Identify potential support and resistance levels
- Spot areas where price might react based on historical turning points
- Enhance their understanding of price structure and repetitive patterns
## Disclaimer
This indicator is provided as a tool to assist in identifying potential price levels of interest. It is not financial advice. Users should not rely solely on this or any single indicator for trading decisions. Always conduct thorough analysis, consider multiple factors, and be aware that past price behavior does not guarantee future results. All trading involves risk.
FVG & IFVG ICT [TradingFinder] Inversion Fair Value Gap Signal🔵 Introduction
🟣 Fair Value Gap (FVG)
To spot a Fair Value Gap (FVG) on a chart, you need to perform a detailed candle-by-candle analysis.
Here’s the process :
Focus on Candles with Large Bodies : Identify a candle with a substantial body and examine it alongside the preceding candle.
Check Surrounding Candles : The candles immediately before and after the central candle should have long shadows.
Ensure No Overlap : The bodies of the candles before and after the central candle should not overlap with the body of the central candle.
Determine the FVG Range : The gap between the shadows of the first and third candles forms the FVG range.
🟣 ICT Inversion Fair Value Gap (IFVG)
An ICT Inversion Fair Value Gap, also known as a reverse FVG, is a failed fair value gap where the price does not respect the gap. An IFVG forms when a fair value gap fails to hold the price and the price moves beyond it, breaking the fair value gap.
This marks the initial shift in price momentum. Typically, when the price moves in one direction, it respects the fair value gaps and continues its trend.
However, if a fair value gap is violated, it acts as an inversion fair value gap, indicating the first change in price momentum, potentially leading to a short-term reversal or a subsequent change in direction.
🟣 Bullish Inversion Fair Value Gap (Bullish IFVG)
🟣 Bearish Inversion Fair Value Gap (Bearish IFVG)
🔵 How to Use
🟣 Identify an Inversion Fair Value Gap
To identify an IFVG, you first need to recognize a fair value gap. Just as fair value gaps come in two types, inversion fair value gaps also fall into two categories:
🟣 Bullish Inversion Fair Value Gap
A bullish IFVG is essentially a bearish fair value gap that is invalidated by the price closing above it.
Here’s how to identify it :
Identify a bearish fair value gap.
When the price closes above this bearish fair value gap, it transforms into a bullish inversion fair value gap.
This gap acts as support for the price and drives it upwards, indicating a reduction in sellers' strength and an initial shift in momentum towards buyers.
🟣 Bearish Inversion Fair Value Gap
A bearish IFVG is primarily a bullish fair value gap that fails to hold the price, with the price closing below it.
Here’s how to identify it :
Identify a bullish fair value gap.
When the price closes below this gap, it becomes a bearish inversion fair value gap.
This gap acts as resistance for the price, pushing it downwards. A bearish inversion fair value gap signifies a decrease in buyers' momentum and an increase in sellers' strength.
🔵 Setting
🟣 Global Setting
Show All FVG : If it is turned off, only the last FVG will be displayed.
S how All Inversion FVG : If it is turned off, only the last FVG will be displayed.
FVG and IFVG Validity Period (Bar) : You can specify the maximum time the FVG and the IFVG remains valid based on the number of candles from the origin.
Switching Colors Theme Mode : Three modes "Off", "Light" and "Dark" are included in this parameter. "Light" mode is for color adjustment for use in "Light Mode".
"Dark" mode is for color adjustment for use in "Dark Mode" and "Off" mode turns off the color adjustment function and the input color to the function is the same as the output color.
🟣 Logic Setting
FVG Filter
When utilizing FVG filtering, the number of identified FVG areas undergoes refinement based on a specified algorithm. This process helps to focus on higher quality signals and eliminate noise.
Here are the types of FVG filters available :
Very Aggressive Filter : Introduces an additional condition to the initial criteria. For an upward FVG, the highest price of the last candle must exceed the highest price of the middle candle. Similarly, for a downward FVG, the lowest price of the last candle should be lower than the lowest price of the middle candle. This mode minimally filters out FVGs.
Aggressive Filter : Builds upon the Very Aggressive mode by considering the size of the middle candle. It ensures the middle candle is not too small, thereby eliminating more FVGs compared to the Very Aggressive mode.
Defensive Filter : In addition to the conditions of the Very Aggressive mode, the Defensive mode incorporates criteria regarding the size and structure of the middle candle. It requires the middle candle to have a substantial body, with specific polarity conditions for the second and third candles relative to the first candle's direction. This mode filters out a significant number of FVGs, focusing on higher-quality signals.
Very Defensive Filter : Further refines filtering by adding conditions that the first and third candles should not be small-bodied doji candles. This stringent mode eliminates the majority of FVGs, retaining only the highest quality signals.
Mitigation Level FVG and IFVG : Its inputs are one of "Proximal", "Distal" or "50 % OB" modes, which you can enter according to your needs. The "50 % OB" line is the middle line between distal and proximal.
🟣 Display Setting
Show Bullish FVG : Enables the display of demand-related boxes, which can be toggled on or off.
Show Bearish FVG : Enables the display of supply-related boxes along the path, which can also be toggled on or off.
Show Bullish IFVG : Enables the display of demand-related boxes, which can be toggled on or off.
Show Bearish IFVG : Enables the display of supply-related boxes along the path, which can also be toggled on or off.
🟣 Alert Setting
Alert FVG Mitigation : If you want to receive the alert about FVG's mitigation after setting the alerts, leave this tick on. Otherwise, turn it off.
Alert Inversion FVG Mitigation : If you want to receive the alert about Inversion FVG's mitigation after setting the alerts, leave this tick on. Otherwise, turn it off.
Message Frequency : This parameter, represented as a string, determines the frequency of announcements. Options include: 'All' (triggers the alert every time the function is called), 'Once Per Bar' (triggers the alert only on the first call within the bar), and 'Once Per Bar Close' (activates the alert only during the final script execution of the real-time bar upon closure). The default setting is 'Once per Bar'.
Show Alert time by Time Zone : The date, hour, and minute displayed in alert messages can be configured to reflect any chosen time zone. For instance, if you prefer London time, you should input 'UTC+1'. By default, this input is configured to the 'UTC' time zone.
Display More Info : The 'Display More Info' option provides details regarding the price range of the order blocks (Zone Price), along with the date, hour, and minute. If you prefer not to include this information in the alert message, you should set it to 'Off'.
Groupings [SS]Hey everyone,
Releasing this indicator called groupings.
If you watch/read my analyses on Tradingview, you will have heard me talk about groups. Groups is something I invented. What it is, is just taking the Euclidean Distance (ED) of the previous 5 candles in a specified period (i.e. daily timeframe, weekly, 1 minute, 5 minute, etc.) and rounding the ED up to a whole number.
I have had great success in this approach because the information provided is broad enough to give leniency in interpretation but narrow enough to hone in on potential moves and target prices.
This indicator is a simplified version of how I do groupings in other software, however it is no less powerful!
What do groups tell us?
A "group" takes into account the previous 5 candles, using the ED. This gives Pinescript a general idea of what the short term trend looks like mathematically. From there, Pinescript can look for other groups that looked similar to how this current trend looks. From there, it can offer us insights into what tends to happen in candles subsequent to this group. For example, the ATR range, the close range and whether it is bearish or bullish.
And that is precisely how this indicator operates, Pinescript will calculate the group of the previous 5 canndles in the timeframe period you are looking at. It will then lookback over the designated "train" length and identify previous groups, and what happened in those groups. It looks specifically at:
- What is that average High ATR associated with that group,
- What is the average Low ATR associated with that group,
- What is the average close range associated with that group,
- What is the sentiment associated with that group.
How to use the indicator?
In terms of use, the indicator is relatively simple to use. It will plot three lines, a red for the anticipated low range, a green for the anticipated high range and purple for the opening range (where the current candle opened at).
In addition, it will plot a dot for the anticipated close area. When the dot is green, it expects a bullish close. When the dot is red, it expects a bearish close.
The indicator is going to give you a heads up as to whether we are in a bullish group, what you can anticipate the high and low range to be and where you can anticipate the close.
Of course, its not always exact, as in the image above you can see it underestimated the high range and over-estimated the low range; however, we did close within the anticipate range.
The indicator is meant to help you with your bias. I will reference this indicator on the daily timeframe at open to see what the expectations are for the day.
However, you can use it on any timeframe you wish.
Other functions:
The indicator can plot the EMA 9, 21 and 5. These are the 3 indicators I like and I find them helpful for both intraday and swing trading. However, they can be toggled off if you do not wish to view them.
In addition, the EMAs will be green if the ticker is trending above the EMA 21 (which is a critical EMA for me to determine the immediate sentiment). If the ticker is below, they will turn red.
There is also the ability to adjust the train time. The default is 1,000 candles back, but I usually have it on 1500. If you have a lot of indicators and a lot going on, on your chart, you may find that 1500 is too much and it will lag/error. That’s okay, 500 candles is sufficient and will not put a lot of stress on Pinescript.
Concluding remarks
Its overall a fairly simple concept and indicator, but it has been a neat and helpful / insightful invention. I originally developed this using R and happy to have now brought it into Pinescript.
I hope you enjoy!
Safe trades everyone!
Fractal Breakout Trend Following StrategyOverview
The Fractal Breakout Trend Following Strategy is a trend-following system which utilizes the Willams Fractals and Alligator to execute the long trades on the fractal's breakouts which have a high probability to be the new uptrend phase beginning. This system also uses the normalized Average True Range indicator to filter trades after a large moves, because it's more likely to see the trend continuation after a consolidation period. Strategy can execute only long trades.
Unique Features
Trend and volatility filtering system: Strategy uses Williams Alligator to filter the counter-trend fractals breakouts and normalized Average True Range to avoid the trades after large moves, when volatility is high
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Flexible Risk Management: Users can choose the stop-loss percent (by default = 3%) for trades, but strategy also has the dynamic stop-loss level using down fractals.
Methodology
The strategy places stop order at the last valid fractal breakout level. Validity of this fractal is defined by the Williams Alligator indicator. If at the moment of time when price breaking the last fractal price is higher than Alligator's teeth line (8 period SMA shifted 5 bars in the future) this is a valid breakout. Moreover strategy has the additional volatility filtering system using normalized ATR. It calculates the average normalized ATR for last user-defined number of bars and if this value lower than the user-defined threshold value the long trade is executed.
When trade is opened, script places the stop loss at the price higher of two levels: user defined stop-loss from the position entry price or down fractal validation level. The down fractal is valid with the rule, opposite as the up fractal validation. Price shall break to the downside the last down fractal below the Willians Alligator's teeth line.
Strategy has no fixed take profit. Exit level changes with the down fractal validation level. If price is in strong uptrend trade is going to be active until last down fractal is not valid. Strategy closes trade when price hits the down fractal validation level.
Risk Management
The strategy employs a combined approach to risk management:
It allows positions to ride the trend as long as the price continues to move favorably, aiming to capture significant price movements. It features a user-defined stop-loss parameter to mitigate risks based on individual risk tolerance. By default, this stop-loss is set to a 3% drop from the entry point, but it can be adjusted according to the trader's preferences.
Justification of Methodology
This strategy leverages Williams Fractals to open long trade when price has broken the key resistance level to the upside. This resistance level is the last up fractal and is shall be broken above the Williams Alligator's teeth line to be qualified as the valid breakout according to this strategy. The Alligator filtering increases the probability to avoid the false breakouts against the current trend.
Moreover strategy has an additional filter using Average True Range(ATR) indicator. If average value of ATR for the last user-defined number of bars is lower than user-defined threshold strategy can open the long trade according to open trade condition above. The logic here is following: we want to open trades after period of price consolidation inside the range because before and after a big move price is more likely to be in sideways, but we need a trend move to have a profit.
Another one important feature is how the exit condition is defined. On the one hand, strategy has the user-defined stop-loss (3% below the entry price by default). It's made to give users the opportunity to restrict their losses according to their risk-tolerance. On the other hand, strategy utilizes the dynamic exit level which is defined by down fractal activation. If we assume the breaking up fractal is the beginning of the uptrend, breaking down fractal can be the start of downtrend phase. We don't want to be in long trade if there is a high probability of reversal to the downside. This approach helps to not keep open trade if trend is not developing and hold it if price continues going up.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2024.05.01. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 30%
Maximum Single Position Loss: -3.19%
Maximum Single Profit: +24.97%
Net Profit: +3036.90 USDT (+30.37%)
Total Trades: 83 (28.92% win rate)
Profit Factor: 1.953
Maximum Accumulated Loss: 963.98 USDT (-8.29%)
Average Profit per Trade: 36.59 USDT (+1.12%)
Average Trade Duration: 72 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 4h and higher time frames and the BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
Propulsion Blocks | Flux Charts💎 GENERAL OVERVIEW
Introducing our new Propulsion Blocks indicator! This new indicator can find & render ICT's propulsion blocks in the current ticker. It's highly customizable with detection, invalidation and style settings. For more information, please visit the "HOW DOES IT WORK ?" section.
Features of the new Propulsion Blocks indicator :
Render Bullish & Bearish Propulsion Blocks
Customizable Algorithm
Enable / Disable Historic Zones
Visual Customizability
📌 HOW DOES IT WORK ?
Order blocks occur when there is a high amount of market orders exist on a price range. It is possible to find order blocks using specific formations on the chart. One of which this indicator uses requires a large engulfing candlestick right after another one of the opposite direction. Then if the price comes back to retest the area that two candlesticks create, then it's an order block pattern.
Propulsion blocks are a specific type of order block used in the trading methodology. They build on the concept of order blocks and aim to identify potential areas for strong price movements. They are detected when a candlestick wicks to any existing order block, retesting it. Then a strong momentum in the direction of the order block is needed for the propulsion block to get created. Check this example :
You can use them as entry / exit points, or for confirmations for your trades. For example, a successful retest attempt to a bullish propulsion block might hint a strong bullish momentum. This indicator works best when used together with other ICT concepts.
🚩UNIQUENESS
Propulsion blocks can help traders identify key levels in a chart, and can be used mainly for confirmation. This indicator can identify and show them automatically in your chart, and provides customization settings for order & propulsion block detection and invalidation. Another capability of the indicator is that it combines overlapping order & propulsion blocks so you will have a clean look at the chart without any overlapping zones.
⚙️SETTINGS
1. General Configuration
Show Historic Zones -> This setting will hide invalidated propulsion blocks if enabled.
Max Distance To Last Bar -> This setting defines the maximum range that the indicator will find propulsion blocks to the past. Higher options will make older zones visible.
Zone Invalidation -> Select between Wick & Close price for Order Block & Propulsion Block Invalidation.
Swing Length -> Swing length is used when finding order block formations. Smaller values will result in finding smaller order blocks.
