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Bill Williams. Awesome Oscillator (AC) Backtest This indicator plots the oscillator as a histogram where blue denotes periods suited for buying and red . for selling. If the current value of AO (Awesome Oscillator) is above previous, the period is considered suited for buying and the period is marked blue. If the AO value is not above previous, the period is considered suited for selling and the indicator marks it as red. You can change long to short in the Input Settings Please, use it only for learning or paper trading. Do not for real trading.
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Historical Volatility Strategy Backtest Strategy buy when HVol above BuyBand and close position when HVol below CloseBand. Markets oscillate from periods of low volatility to high volatility and back. The author`s research indicates that after periods of extremely low volatility, volatility tends to increase and price may move sharply. This increase in volatility tends to correlate with the beginning of short- to intermediate-term moves in price. They have found that we can identify which markets are about to make such a move by measuring the historical volatility and the application of pattern recognition. The indicator is calculating as the standard deviation of day-to-day logarithmic closing price changes expressed as an annualized percentage. Please, use it only for learning or paper trading. Do not for real trading.
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Fisher Transform Indicator by Ehlers Backtest v 2.0 Market prices do not have a Gaussian probability density function as many traders think. Their probability curve is not bell-shaped. But trader can create a nearly Gaussian PDF for prices by normalizing them or creating a normalized indicator such as the relative strength index and applying the Fisher transform. Such a transformed output creates the peak swings as relatively rare events. Fisher transform formula is: y = 0.5 * ln ((1+x)/(1-x)) The sharp turning points of these peak swings clearly and unambiguously identify price reversals in a timely manner. For signal used zero. You can change long to short in the Input Settings Please, use it only for learning or paper trading. Do not for real trading.
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Fisher Transform Indicator by Ehlers Backtest Market prices do not have a Gaussian probability density function as many traders think. Their probability curve is not bell-shaped. But trader can create a nearly Gaussian PDF for prices by normalizing them or creating a normalized indicator such as the relative strength index and applying the Fisher transform. Such a transformed output creates the peak swings as relatively rare events. Fisher transform formula is: y = 0.5 * ln ((1+x)/(1-x)) The sharp turning points of these peak swings clearly and unambiguously identify price reversals in a timely manner. You can change long to short in the Input Settings Please, use it only for learning or paper trading. Do not for real trading.
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Ergotic TSI Strategy Backtest r - Length of first EMA smoothing of 1 day momentum 4 s - Length of second EMA smoothing of 1 day smoothing 8 u- Length of third EMA smoothing of 1 day momentum 6 Length of EMA signal line 3 Source of Ergotic TSI Close This is one of the techniques described by William Blau in his book "Momentum, Direction and Divergence" (1995). If you like to learn more, we advise you to read this book. His book focuses on three key aspects of trading: momentum, direction and divergence. Blau, who was an electrical engineer before becoming a trader, thoroughly examines the relationship between price and momentum in step-by-step examples. From this grounding, he then looks at the deficiencies in other oscillators and introduces some innovative techniques, including a fresh twist on Stochastics. On directional issues, he analyzes the intricacies of ADX and offers a unique approach to help define trending and non-trending periods. You can use in the xPrice any series: Open, High, Low, Close, HL2, HLC3, OHLC4 and ect... You can change long to short in the Input Settings Please, use it only for learning or paper trading. Do not for real trading.
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Ergotic MACD Strategy Backtest This is one of the techniques described by William Blau in his book "Momentum, Direction and Divergence" (1995). If you like to learn more, we advise you to read this book. His book focuses on three key aspects of trading: momentum, direction and divergence. Blau, who was an electrical engineer before becoming a trader, thoroughly examines the relationship between price and momentum in step-by-step examples. From this grounding, he then looks at the deficiencies in other oscillators and introduces some innovative techniques, including a fresh twist on Stochastics. On directional issues, he analyzes the intricacies of ADX and offers a unique approach to help define trending and non-trending periods. Blau`s indicator is like usual MACD, but it plots opposite of meaningof stndard MACD indicator. You can change long to short in the Input Settings Please, use it only for learning or paper trading. Do not for real trading.
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Elder Ray (Bull Power) Strategy Backtest Developed by Dr Alexander Elder, the Elder-ray indicator measures buying and selling pressure in the market. The Elder-ray is often used as part of the Triple Screen trading system but may also be used on its own. Dr Elder uses a 13-day exponential moving average (EMA) to indicate the market consensus of value. Bull Power measures the ability of buyers to drive prices above the consensus of value. Bear Power reflects the ability of sellers to drive prices below the average consensus of value. Bull Power is calculated by subtracting the 13-day EMA from the day's High. Bear power subtracts the 13-day EMA from the day's Low. You can use in the xPrice any series: Open, High, Low, Close, HL2, HLC3, OHLC4 and ect... You can change long to short in the Input Settings Please, use it only for learning or paper trading. Do not for real trading.
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ECO Strategy Backtest We call this one the ECO for short, but it will be listed on the indicator list at W. Blau’s Ergodic Candlestick Oscillator. The ECO is a momentum indicator. It is based on candlestick bars, and takes into account the size and direction of the candlestick "body". We have found it to be a very good momentum indicator, and especially smooth, because it is unaffected by gaps in price, unlike many other momentum indicators. We like to use this indicator as an additional trend confirmation tool, or as an alternate trend definition tool, in place of a weekly indicator. The simplest way of using the indicator is simply to define the trend based on which side of the "0" line the indicator is located on. If the indicator is above "0", then the trend is up. If the indicator is below "0" then the trend is down. You can add an additional qualifier by noting the "slope" of the indicator, and the crossing points of the slow and fast lines. Some like to use the slope alone to define trend direction. If the lines are sloping upward, the trend is up. Alternately, if the lines are sloping downward, the trend is down. In this view, the point where the lines "cross" is the point where the trend changes. When the ECO is below the "0" line, the trend is down, and we are qualified only to sell on new short signals from the Hi-Lo Activator. In other words, when the ECO is above 0, we are not allowed to take short signals, and when the ECO is below 0, we are not allowed to take long signals. You can change long to short in the Input Settings Please, use it only for learning or paper trading. Do not for real trading.
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DAPD - Strategy Backtest This indicator is similar to Bollinger Bands. It based on DAPD - Daily Average Price Delta. DAPD is based upon a summation for each of the highs (hod) for the 21 days prior to today minus the summation for each of the lows (lod) for the last 21 days prior to today. The result of this calculation would then be divided by 21. It will be buy when high above previos DAPD high and sell if low below previos DAPD low You can change long to short in the Input Settings Please, use it only for learning or paper trading. Do not for real trading.
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CCI Strategy Reversed Backtest The Commodity Channel Index (CCI) is best used with markets that display cyclical or seasonal characteristics, and is formulated to detect the beginning and ending of these cycles by incorporating a moving average together with a divisor that reflects both possible and actual trading ranges. The final index measures the deviation from normal, which indicates major changes in market trend. To put it simply, the Commodity Channel Index (CCI) value shows how the instrument is trading relative to its mean (average) price. When the CCI value is high, it means that the prices are high compared to the average price; when the CCI value is down, it means that the prices are low compared to the average price. The CCI value usually does not fall outside the -300 to 300 range and, in fact, is usually in the -100 to 100 range. You can change long to short in the Input Settings Please, use it only for learning or paper trading. Do not for real trading.
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3-Bar-Reversal-Pattern Strategy Backtest This startegy based on 3-day pattern reversal described in "Are Three-Bar Patterns Reliable For Stocks" article by Thomas Bulkowski, presented in January, 2000 issue of Stocks&Commodities magazine. That pattern conforms to the following rules: - It uses daily prices, not intraday or weekly prices; - The middle day of the three-day pattern has the lowest low of the three days, with no ties allowed; - The last day must have a close above the prior day's high, with no ties allowed; - Each day must have a nonzero trading range. Please, use it only for learning or paper trading. Do not for real trading.
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Custom XABCD Validation and Backtesting ToolOverview: We hear a lot about Gartleys, bats, crabs and the rest of the barnyard crew, but have you ever wondered what other creatures might be lurking out there yet to be discovered? Well wonder no longer, it's time to find out for yourself! The Custom XABCD Validation and Backtesting Tool allows you to define retracement ratios and targets for your very own patterns. Tips: (1) Adjust the patterns entry/stop/target configuration and see how it affects the pattern's backtesting results. (2) Adjust the weights of pattern score components (% error, PRZ confluence, Point D/PRZ confluence), along with the entry minimum score requirements ('If score is above'), and see how it affects the patterns' results. Pattern Scoring: The pattern's score is an attempt to represent the quality of a pattern with a single metric. This is one of the most powerful aspects of the tool because it can quickly tell you whether a trade is worth entering. The score is based on 3 components: (1) Retracement % Accuracy - this measures how closely a pattern's retracement ratios match your defined theoretical values. You can change the "Allowed ratio error %" in Settings to be more or less inclusive. (2) PRZ Level Confluence - Potential Reversal Zone levels are retracements of the XA, BC, and/or XC legs. These levels indicate where a potential reversal might occur (i.e. pivot point D). The PRZ Level Confluence component measures the closeness of the two closest PRZ levels, relative to the height of the of the XA leg. (3) Point D / PRZ Confluence - this measures the closeness of point D to either of the two closest PRZ levels (identified in the PRZ Level Confluence component above), relative to the height of the XA leg. In theory, the closer together these levels are, the higher the probability of a reversal. While the score is percentage-based, it should not be confused with a probability. A score of 96% does not imply a 96% chance of success. It simply represents the average of the three components mentioned above, weighted according to the defined weight parameters. A score of 100% would mean that (1) all leg retracements match the defined theoretical retracement ratios exactly, (2) all PRZ retracement levels are exactly the same value, and (3) pivot point D occurred exactly at the confluent PRZ level. Pattern scoring research has been ongoing since I introduced the concept with my Harmonic Pattern Detection, Prediction and Backtesting Tool (see below). So the way that the score is calculated is subject to change based on the results of that research.
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Strategy Stats [presentTrading]Hello! it's another weekend. This tool is a strategy performance analysis tool. Looking at the TradingView community, it seems few creators focus on this aspect. I've intentionally created a shared version. Welcome to share your idea or question on this. █ Introduction and How it is Different Strategy Stats is a comprehensive performance analytics framework designed specifically for trading strategies. Unlike standard strategy backtesting tools that simply show cumulative profits, this analytics suite provides real-time, multi-timeframe statistical analysis of your trading performance. Multi-timeframe analysis: Automatically tracks performance metrics across the most recent time periods (last 7 days, 30 days, 90 days, 1 year, and 4 years) Advanced statistical measures: Goes beyond basic metrics to include Information Coefficient (IC) and Sortino Ratio Real-time feedback: Updates performance statistics with each new trade Visual analytics: Color-coded performance table provides instant visual feedback on strategy health Integrated risk management: Implements sophisticated take profit mechanisms with 3-step ATR and percentage-based exits BTCUSD Performance The table in the upper right corner is a comprehensive performance dashboard showing trading strategy statistics. Note: While this presentation uses Vegas SuperTrend as the underlying strategy, this is merely an example. The Stats framework can be applied to any trading strategy. The Vegas SuperTrend implementation is included solely to demonstrate how the analytics module integrates with a trading strategy. ⚠️ Timeframe Limitations Important: TradingView's backtesting engine has a maximum storage limit of 10,000 bars. When using this strategy stats framework on smaller timeframes such as 1-hour or 2-hour charts, you may encounter errors if your backtesting period is too long. Recommended Timeframe Usage: Ideal for: 4H, 6H, 8H, Daily charts and above May cause errors on: 1H, 2H charts spanning multiple years Not recommended for: Timeframes below 1H with long history █ Strategy, How it Works: Detailed Explanation The Strategy Stats framework consists of three primary components: statistical data collection, performance analysis, and visualization. 🔶 Statistical Data Collection The system maintains several critical data arrays: equityHistory: Tracks equity curve over time tradeHistory: Records profit/loss of each trade predictionSignals: Stores trade direction signals (1 for long, -1 for short) actualReturns: Records corresponding actual returns from each trade For each closed trade, the system captures: float tradePnL = strategy.closedtrades.profit(tradeIndex) float tradeReturn = strategy.closedtrades.profit_percent(tradeIndex) int tradeType = entryPrice < exitPrice ? 1 : -1 // Direction 🔶 Performance Metrics Calculation The framework calculates several key performance metrics: Information Coefficient (IC): The correlation between prediction signals and actual returns, measuring forecast skill. IC = Correlation(predictionSignals, actualReturns) Where Correlation is the Pearson correlation coefficient: Correlation(X,Y) = (nΣXY - ΣXY) / √ Sortino Ratio: Measures risk-adjusted return focusing only on downside risk: Sortino = (Avg_Return - Risk_Free_Rate) / Downside_Deviation Where Downside Deviation is: Downside_Deviation = √ R_i represents individual returns, T is the target return (typically the risk-free rate), and n is the number of observations. Maximum Drawdown: Tracks the largest percentage drop from peak to trough: DD = (Peak_Equity - Trough_Equity) / Peak_Equity * 100 🔶 Time Period Calculation The system automatically determines the appropriate number of bars to analyze for each timeframe based on the current chart timeframe: bars_7d = math.max(1, math.round(7 * barsPerDay)) bars_30d = math.max(1, math.round(30 * barsPerDay)) bars_90d = math.max(1, math.round(90 * barsPerDay)) bars_365d = math.max(1, math.round(365 * barsPerDay)) bars_4y = math.max(1, math.round(365 * 4 * barsPerDay)) Where barsPerDay is calculated based on the chart timeframe: barsPerDay = timeframe.isintraday ? 24 * 60 / math.max(1, (timeframe.in_seconds() / 60)) : timeframe.isdaily ? 1 : timeframe.isweekly ? 1/7 : timeframe.ismonthly ? 1/30 : 0.01 🔶 Visual Representation The system presents performance data in a color-coded table with intuitive visual indicators: Green: Excellent performance Lime: Good performance Gray: Neutral performance Orange: Mediocre performance Red: Poor performance █ Trade Direction The Strategy Stats framework supports three trading directions: Long Only: Only takes long positions when entry conditions are met Short Only: Only takes short positions when entry conditions are met Both: Takes both long and short positions depending on market conditions █ Usage To effectively use the Strategy Stats framework: Apply to existing strategies: Add the performance tracking code to any strategy to gain advanced analytics Monitor multiple timeframes: Use the multi-timeframe analysis to identify performance trends Evaluate strategy health: Review IC and Sortino ratios to assess predictive power and risk-adjusted returns Optimize parameters: Use performance data to refine strategy parameters Compare strategies: Apply the framework to multiple strategies to identify the most effective approach For best results, allow the strategy to generate sufficient trade history for meaningful statistical analysis (at least 20-30 trades). █ Default Settings The default settings have been carefully calibrated for cryptocurrency markets: Performance Tracking: Time periods: 7D, 30D, 90D, 1Y, 4Y Statistical measures: Return, Win%, MaxDD, IC, Sortino Ratio IC color thresholds: >0.3 (green), >0.1 (lime), <-0.1 (orange), <-0.3 (red) Sortino color thresholds: >1.0 (green), >0.5 (lime), <0 (red) Multi-Step Take Profit: ATR multipliers: 2.618, 5.0, 10.0 Percentage levels: 3%, 8%, 17% Short multiplier: 1.5x (makes short take profits more aggressive) Stop loss: 20%
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ICT Turtle Soup | Flux Charts💎 GENERAL OVERVIEW Introducing our new ICT Turtle Soup Indicator! This indicator is built around the ICT "Turtle Soup" model. The strategy has 5 steps for execution which are described in this write-up. For more information about the process, check the "HOW DOES IT WORK" section. Features of the new ICT Turtle Soup Indicator : Implementation of ICT's Turtle Soup Strategy Adaptive Entry Method Customizable Execution Settings Customizable Backtesting Dashboard Alerts for Buy, Sell, TP & SL Signals 📌 HOW DOES IT WORK ? The ICT Turtle Soup strategy may have different implementations depending on the selected method of the trader. This indicator's implementation is described as : 1. Mark higher timerame liquidity zones. Liquidity zones are where a lot of market orders sit in the chart. They are usually formed from the long / short position holders' "liquidity" levels. There are various ways to find them, most common one being drawing them on the latest high & low pivot points in the chart, which this indicator does. 2. Mark current timeframe market structure. The market structure is the current flow of the market. It tells you if the market is trending right now, and the way it's trending towards. It's formed from swing higs, swing lows and support / resistance levels. 3. Wait for market to make a liquidity grab on the higher timeframe liquidity zone. A liquidity grab is when the marked liquidity zones have a false breakout, which means that it gets broken for a brief amount of time, but then price falls back to it's previous position. 4. Buyside liquidity grabs are "Short" entries and Sellside liquidity grabs are "Long" entries by default. 5. Wait for the market-structure shift in the current timeframe for entry confirmation. A market-structure shift happens when the current market structure changes, usually when a new swing high / swing low is formed. This indicator uses it as a confirmation for position entry as it gives an insight of the new trend of the market. 6. Place Take-Profit and Stop-Loss levels according to the risk ratio. This indicator uses "Average True Range" when placing the stop-loss & take-profit levels. Average True Range calculates the average size of a candle and the indicator places the stop-loss level using ATR times the risk setting determined by the user, then places the take-profit level trying to keep a minimum of 1:1 risk-reward ratio. This indicator follows these steps and inform you step by step by plotting them in your chart. 🚩UNIQUENESS This indicator is an all-in-one suit for the ICT's Turtle Soup concept. It's capable of plotting the strategy, giving signals, a backtesting dashboard and alerts feature. It's designed for simplyfing a rather complex strategy, helping you to execute it with clean signals. The backtesting dashboard allows you to see how your settings perform in the current ticker. You can also set up alerts to get informed when the strategy is executable for different tickers. ⚙️SETTINGS 1. General Configuration MSS Swing Length -> The swing length when finding liquidity zones for market structure-shift detection. Higher Timeframe -> The higher timeframe to look for liquidity grabs. This timeframe setting must be higher than the current chart's timeframe for the indicator to work. Breakout Method -> If "Wick" is selected, a bar wick will be enough to confirm a market structure-shift. If "Close" is selected, the bar must close above / below the liquidity zone to confirm a market structure-shift. Entry Method -> "Classic" : Works as described on the "HOW DOES IT WORK" section. "Adaptive" : When "Adaptive" is selected, the entry conditions may chance depending on the current performance of the indicator. It saves the entry conditions and the performance of the past entries, then for the new entries it checks if it predicted the liquidity grabs correctly with the current setup, if so, continues with the same logic. If not, it changes behaviour to reverse the entries from long / short to short / long. 2. TP / SL TP / SL Method -> If "Fixed" is selected, you can adjust the TP / SL ratios from the settings below. If "Dynamic" is selected, the TP / SL zones will be auto-determined by the algorithm. Risk -> The risk you're willing to take if "Dynamic" TP / SL Method is selected. Higher risk usually means a better winrate at the cost of losing more if the strategy fails. This setting is has a crucial effect on the performance of the indicator, as different tickers may have different volatility so the indicator may have increased performance when this setting is correctly adjusted.
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