Alans Date Range CalculatorOverview
Setting a date range for backtesting enables you to evaluate your trading strategy under various market conditions. Traders can test a strategy’s performance during specific periods, such as economic downturns, bull markets, or periods of high volatility. This helps assess the trading strategy’s robustness and adaptability across different scenarios.
Specifying years of data instead of just inputting specific start and end dates offers several advantages:
1. **Consistency**: Using a fixed number of years ensures that the testing period is consistent across different strategies or iterations. This makes it easier to compare performance metrics and draw meaningful conclusions.
2. **Flexibility**: Specifying years allows for automatic adjustment of the start date based on the current date or selected end date. This is particularly useful when new data becomes available or when testing on different assets with varying historical data lengths.
3. **Efficiency**: It simplifies updating and retesting strategies. Instead of recalculating specific start dates each time, traders can quickly adjust the number of years to process, making it easier to test strategies over different timeframes.
4. **Comprehensive Analysis**: Broader timeframes defined by years help you evaluate how your strategy performs over multiple market cycles, providing insights into long-term viability and potential weaknesses.
Defining a date range by specifying years allows for more thorough and systematic backtesting, helping traders develop more reliable and effective trading systems.
Alan's Date Range Calculator: A TradingView Pine Script Indicator
Purpose
This Pine Script indicator calculates and displays a date range for backtesting trading strategies. It allows users to specify the number of years to analyze and an end date, then calculates the corresponding start date. Most importantly, users can copy the inputs and function into their own strategies to quickly add a time span feature for backtesting.
Key Features
User-defined input for the number of years to analyze
Customizable end date with a calendar input
Automatic calculation of the start date
Visual display of both start and end dates on the chart
How It Works
User Inputs
Years of Data to Process: An integer input allowing users to specify the number of years for analysis (default: 20, range: 1-100)
End Date: A calendar input for selecting the end date of the analysis period (default: December 31, 2024)
Date Calculation
The script uses a custom function calcStartDate() to determine the start date. It subtracts the specified number of years from the end date's year and sets the start date to January 1st of that year.
Visual Output
The indicator displays two labels on the chart:
Start Date Label: Shows the calculated start date
End Date Label: Displays the user-specified end date
Both labels are positioned horizontally at the bottom of the chart, with the end date label to the right of the start date label.
Applications
This indicator is particularly useful for traders who want to:
Define specific date ranges for backtesting strategies
Quickly visualize the time span of their analysis
Ensure consistent testing periods across different strategies or assets
Customization
Users can easily adjust the analysis period by changing the number of years or selecting a different end date. This flexibility allows for testing strategies across various market conditions and time frames.
Cerca negli script per "backtest"
XAUUSD 10-Minute StrategyThis XAUUSD 10-Minute Strategy is designed for trading Gold vs. USD on a 10-minute timeframe. By combining multiple technical indicators (MACD, RSI, Bollinger Bands, and ATR), the strategy effectively captures both trend-following and reversal opportunities, with adaptive risk management for varying market volatility. This approach balances high-probability entries with robust volatility management, making it suitable for traders seeking to optimise entries during significant price movements and reversals.
Key Components and Logic:
MACD (12, 26, 9):
Generates buy signals on MACD Line crossovers above the Signal Line and sell signals on crossovers below the Signal Line, helping to capture momentum shifts.
RSI (14):
Utilizes oversold (below 35) and overbought (above 65) levels as a secondary filter to validate entries and avoid overextended price zones.
Bollinger Bands (20, 2):
Uses upper and lower Bollinger Bands to identify potential overbought and oversold conditions, aiming to enter long trades near the lower band and short trades near the upper band.
ATR-Based Stop Loss and Take Profit:
Stop Loss and Take Profit levels are dynamically set as multiples of ATR (3x for stop loss, 5x for take profit), ensuring flexibility with market volatility to optimise exit points.
Entry & Exit Conditions:
Buy Entry: T riggered when any of the following conditions are met:
MACD Line crosses above the Signal Line
RSI is oversold
Price drops below the lower Bollinger Band
Sell Entry: Triggered when any of the following conditions are met:
MACD Line crosses below the Signal Line
RSI is overbought
Price moves above the upper Bollinger Band
Exit Strategy: Trades are closed based on opposing entry signals, with adaptive spread adjustments for realistic exit points.
Backtesting Configuration & Results:
Backtesting Period: July 21, 2024, to October 30, 2024
Symbol Info: XAUUSD, 10-minute timeframe, OANDA data source
Backtesting Capital: Initial capital of $700, with each trade set to 10 contracts (equivalent to approximately 0.1 lots based on the broker’s contract size for gold).
Users should confirm their broker's contract size for gold, as this may differ. This script uses 10 contracts for backtesting purposes, aligned with 0.1 lots on brokers offering a 100-contract specification.
Key Backtesting Performance Metrics:
Net Profit: $4,733.90 USD (676.27% increase)
Total Closed Trades: 526
Win Rate: 53.99%
Profit Factor: 1.44 (1.96 for Long trades, 1.14 for Short trades)
Max Drawdown: $819.75 USD (56.33% of equity)
Sharpe Ratio: 1.726
Average Trade: $9.00 USD (0.04% of equity per trade)
This backtest reflects realistic conditions, with a spread adjustment of 38 points and no slippage or commission applied. The settings aim to simulate typical retail trading conditions. However, please adjust the initial capital, contract size, and other settings based on your account specifics for best results.
Usage:
This strategy is tuned specifically for XAUUSD on a 10-minute timeframe, ideal for both trend-following and reversal trades. The ATR-based stop loss and take profit levels adapt dynamically to market volatility, optimising entries and exits in varied conditions. To backtest this script accurately, ensure your broker’s contract specifications for gold align with the parameters used in this strategy.
All Divergences with trend / SL - Uncle SamThanks to the main inspiration behind this strategy and the hard work of:
"Divergence for many indicators v4 by LonesomeTheBlue"
The "All Divergence" strategy is a versatile approach for identifying and acting upon various divergences in the market. Divergences occur when price and an indicator move in opposite directions, often signaling potential reversals. This strategy incorporates both regular and hidden divergences across multiple indicators (MACD, Stochastics, CCI, etc.) for a comprehensive analysis.
Key Features:
Comprehensive Divergence Analysis: The strategy scans for regular and hidden divergences across a variety of indicators, increasing the probability of identifying potential trade setups.
Trend Filter: To enhance accuracy, a moving average (MA) trend filter is integrated. This ensures trades align with the overall market trend, reducing the risk of false signals.
Customizable Risk Management: Users can adjust parameters for long/short stop-loss and take-profit levels to match their individual risk tolerance.
Additional Risk Management (Optional): An experimental MA-based risk management feature can be enabled to close positions if the market shows consecutive closes against the trend.
Clear Visuals: The script plots pivot points, divergence lines, and stop-loss levels on the chart for easy reference.
Strategy Settings (Defaults):
Enable Long/Short Strategy: True
Long/Short Stop Loss %: 2%
Long/Short Take Profit %: 5%
Enable MA Trend: True
MA Type: HMA (Hull Moving Average)
MA Length: 500
Use MA Risk Management: False (Experimental)
MA Risk Exit Candles: 2 (If enabled)
Pivot Period: 9
Source for Pivot Points: Close
Backtest Details (Example):
The strategy has been backtested on XAUUSD 1H (Goold/USD 1 hour timeframe) with a starting capital of $1,000. The backtest period covers around 2 years. A commission of 0.02% per trade and a 0.1% slippage per trade were factored in to simulate real-world trading costs.
Disclaimer:
This strategy is for educational and informational purposes only. Backtested results are not indicative of future performance. Use this strategy at your own risk. Always conduct your own analysis and consider consulting a financial professional before making any trading decisions.
Important Notes:
The default settings are a good starting point, but feel free to experiment to find optimal parameters for your specific trading style and market.
The MA-based risk management is an experimental feature. Use it with caution and thoroughly test it before deploying in live trading.
Backtest results can vary depending on the market, timeframe, and specific settings used. Always consider slippage and commission fees when evaluating a strategy's potential profitability.
Super Trend Daily 2.0 Alerts BFThis is an alerts script for my Super Trend 2.0 indicator . It is intended as a companion script so you can backtest using the Strategy script and generate alerts using this Study script.
This Study script has the same default settings as the Strategy script and its only purpose is to provide alerts for the long and short signals the Strategy generates. Obviously, if you want to generate alerts based on a Strategy backtest, please ensure the settings are the same in the Study as in the Strategy.
For illustration, I have plotted arrows on the chart for long and short signals, and also colored the background to show when the rate of change function determines a choppy/sideways market.
ALERTS
There are 2 alerts set up:
Long Entry
Short Entry
ILLUSTRATION
Green arrow = Long Entry
Red arrow = Short Entry
White background = No short trades
Aqua background = No long trades
EXAMPLE USE CASE
1. Open a Bitcoin/USD chart on 1D timeframe.
2. Open this script and the Super Trend 2.0 indicator script.
3. Backtest with the Strategy Backtester and change the settings if you like until you get a desirable outcome for your own purposes.
4. Once you are happy with the backtest, change the settings in the Alerts script (this one) so they match the Strategy settings.
5. Set up the alerts according to your preferences.
Grand Master's Candlestick Dominance (ATR Enhanced)### Grand Master's Candlestick Dominance (ATR Enhanced)
**Overview**
Unleash the ancient wisdom of Japanese candlestick charting with a modern twist! This comprehensive Pine Script v5 strategy and indicator scans for over 75 classic and advanced candlestick patterns (bullish, bearish, and neutral), assigning dynamic strength scores (1-10) to each for precise signal filtering. Enhanced with Average True Range (ATR) for volatility-aware body size validation, it dominates the markets by combining timeless pattern recognition with robust confirmation layers. Whether used as a backtestable strategy or visual indicator, it empowers traders to spot high-probability reversals, continuations, and indecision setups with surgical accuracy.
Inspired by Steve Nison's *Japanese Candlestick Charting Techniques*, this tool elevates pattern analysis beyond basics—think Hammers, Engulfing patterns, Morning Stars, and rare gems like Abandoned Baby or Concealing Baby Swallow—all consolidated into intelligent arrays for real-time averaging and prioritization.
**Key Features**
- **Extensive Pattern Library**:
- **Bullish (25+ patterns)**: Hammer (8.0), Bullish Engulfing (10.0), Morning Star (7.0), Three White Soldiers (9.0), Dragonfly Doji (8.0), and more (e.g., Rising Three, Unique Three River Bottom).
- **Bearish (25+ patterns)**: Hanging Man (8.0), Bearish Engulfing (10.0), Evening Star (7.0), Three Black Crows (9.0), Gravestone Doji (8.0), and exotics like Upside Gap Two Crows or Stalled Pattern.
- **Neutral/Indecision (34+ patterns)**: Doji variants (Long-Legged, Four Price), Spinning Tops, Harami Crosses, and multi-bar setups like Upside Tasuki Gap or Advancing Block.
Each pattern includes duration tracking (1-5 bars) and ATR-adjusted body/shadow criteria for relevance in volatile conditions.
- **Smart Confirmation Filters** (All Toggleable):
- **Trend Alignment**: 20-period SMA (customizable) ensures entries align with the prevailing trend; optional higher timeframe (e.g., Daily) MA crossover for multi-timeframe confluence.
- **Support/Resistance (S/R)**: Pivot-based levels with 0.01% tolerance to confirm bounces or breaks.
- **Volume Surge**: 20-period volume MA with 1.5x spike multiplier to validate momentum.
- **ATR Body Sizing**: Filters small bodies (<0.3x ATR) and long bodies (>0.8x ATR) for context-aware pattern reliability.
- **Follow-Through**: Ensures post-pattern confirmation via bullish/bearish closes or closes beyond prior bars.
Minimum average strength (default 7.0) and individual pattern thresholds (5.0) prevent weak signals.
- **Entry & Exit Logic**:
- **Long Entry**: Bullish average strength ≥7.0 (outweighing bearish), uptrend, volume spike, near support, follow-through, and HTF alignment.
- **Short Entry**: Mirror for bearish dominance in downtrends near resistance.
- **Exits**: Bearish/neutral shift, or fixed TP (5%) / SL (2%)—pyramiding disabled, 10% equity sizing.
- Backtest range: Jan 1, 2020 – Dec 31, 2025 (editable). Initial capital: $10,000.
- **Interactive Dashboard** (Top-Right Panel):
Real-time insights including:
- Market phase (e.g., "Bullish Phase (Avg Str: 8.2)"), active pattern (e.g., "BULLISH: Bullish Engulfing (Str: 10.0, Bars: 2)"), and trend status.
- Strength breakdowns (Bull/Bear/Neutral counts & averages).
- Filter status (e.g., "Volume: ✔ Spike", "ATR: Enabled (L:0.8, S:0.3)").
- Backtest stats: Total trades, win rate, streak, and last entry/exit details (price & timestamp).
Toggle mode: Strategy (live trades) or Indicator (signals only).
- **Advanced Alerts** (15+ Toggleable Types):
Set up via TradingView's "Any alert() function call" for bar-close triggers:
- Entry/Exit signals with strength & pattern details.
- Strong patterns (≥2 bullish/bearish), neutral indecision, volume spikes.
- S/R breakouts, HTF reversals, high-confidence singles (≥8.0 strength).
- Conflicting signals, MA crossovers, ATR volatility bursts, multi-bar completions.
Example: "STRONG BULLISH PATTERN detected! Strength: 9.5 | Top Pattern: Three White Soldiers | Trend: Up".
**Customization & Usage Tips**
- **Inputs Groups**: Strategy toggles, confirmations, exits, backtest dates, and 15+ alert switches—all intuitively grouped.
- **Optimization**: Tune min strengths for aggressive (lower) or conservative (higher) trading; enable/disable filters to suit your style (e.g., disable S/R for scalping).
- **Best For**: Forex, stocks, crypto on 1H–Daily charts. Test on historical data to refine TP/SL.
- **Limitations**: No external data installs; relies on built-in TA functions. Patterns are probabilistic—combine with your risk management.
Master the candles like a grandmaster. Deploy on TradingView, backtest relentlessly, and let dominance begin! Questions? Drop a comment.
*Version: 1.0 | Updated: September 2025 | Credits: Built on Pine Script v5 with nods to Nison's timeless techniques.*
Advanced Multi-Timeframe Trading System (Risk Managed)Description:
This strategy is an original approach that combines two main analytical components to identify potential trade opportunities while simulating realistic trading conditions:
1. Market Trend Analysis via an Approximate Hurst Exponent
• What It Does:
The strategy computes a rough measure of market trending using an approximate Hurst exponent. A value above 0.5 suggests persistent, trending behavior, while a value below 0.5 indicates a tendency toward mean-reversion.
• How It’s Used:
The Hurst exponent is calculated on both the chart’s current timeframe and a higher timeframe (default: Daily) to capture both local and broader market dynamics.
2. Fibonacci Retracement Levels
• What It Does:
Using daily high and low data from a selected timeframe (default: Daily), the script computes key Fibonacci retracement levels.
• How It’s Used:
• The 61.8% level (Golden Ratio) serves as a key threshold:
• A long entry is signaled when the price crosses above this level if the daily Hurst exponent confirms a trending market.
• The 38.2% level is used to identify short-entry opportunities when the price crosses below it and the daily Hurst indicates non-trending conditions.
Signal Logic:
• Long Entry:
When the price crosses above the 61.8% Fibonacci level (Golden Ratio) and the daily Hurst exponent is greater than 0.5, suggesting a trending market.
• Short Entry:
When the price crosses below the 38.2% Fibonacci level and the daily Hurst exponent is less than 0.5, indicating a less trending or potentially reversing market.
Risk Management & Trade Execution:
• Stop-Loss:
Each trade is risk-managed with a stop-loss set at 2% below (for longs) or above (for shorts) the entry price. This ensures that no single trade risks more than a small, sustainable portion of the account.
• Take Profit:
A take profit order targets a risk-reward ratio of 1:2 (i.e., the target profit is twice the amount risked).
• Position Sizing:
Trades are executed with a fixed position size equal to 10% of account equity.
• Trade Frequency Limits:
• Daily Limit: A maximum of 5 trades per day
• Overall Limit: No more than 510 trades during the backtesting period (e.g., since 2019)
These limits are imposed to simulate realistic trading frequency and to avoid overtrading in backtest results.
Backtesting Parameters:
• Initial Capital: $10,000
• Commission: 0.1% per trade
• Slippage: 1 tick per bar
These settings aim to reflect the conditions faced by the average trader and help ensure that the backtesting results are realistic and not misleading.
Chart Overlays & Visual Aids:
• Fibonacci Levels:
The key Fibonacci retracement levels are plotted on the chart, and the zone between the 61.8% and 38.2% levels is highlighted to show a key retracement area.
• Market Trend Background:
The chart background is tinted green when the daily Hurst exponent indicates a trending market (value > 0.5) and red otherwise.
• Information Table:
An on-chart table displays key parameters such as the current Hurst exponent, daily Hurst value, the number of trades executed today, and the global trade count.
Disclaimer:
Past performance is not indicative of future results. This strategy is experimental and provided solely for educational purposes. It is essential that you backtest and paper trade using your own settings before considering any live deployment. The Hurst exponent calculation is an approximation and should be interpreted as a rough gauge of market behavior. Adjust the parameters and risk management settings according to your personal risk tolerance and market conditions.
Additional Notes:
• Originality & Usefulness:
This script is an original mashup that combines trend analysis with Fibonacci retracement methods. The description above explains how these components work together to provide trading signals.
• Realistic Results:
The strategy uses realistic account sizes, commission rates, slippage, and risk management rules to generate backtesting results that are representative of real-world trading.
• Educational Purpose:
This script is intended to support the TradingView community by offering insights into combining multiple analysis techniques in one strategy. It is not a “get-rich-quick” system but rather an educational tool to help traders understand risk management and trade signal logic.
By using this script, you acknowledge that trading involves risk and that you are responsible for testing and adjusting the strategy to fit your own trading environment. This publication is fully open source, and any modifications should include proper attribution if significant portions of the code are reused.
DNSE VN301!, SMA & EMA Cross StrategyDiscover the tailored Pinescript to trade VN30F1M Future Contracts intraday, the strategy focuses on SMA & EMA crosses to identify potential entry/exit points. The script closes all positions by 14:25 to avoid holding any contracts overnight.
HNX:VN301!
www.tradingview.com
Setting & Backtest result:
1-minute chart, initial capital of VND 100 million, entering 4 contracts per time, backtest result from Jan-2024 to Nov-2024 yielded a return over 40%, executed over 1,000 trades (average of 4 trades/day), winning trades rate ~ 30% with a profit factor of 1.10.
The default setting of the script:
A decent optimization is reached when SMA and EMA periods are set to 60 and 15 respectively while the Long/Short stop-loss level is set to 20 ticks (2 points) from the entry price.
Entry & Exit conditions:
Long signals are generated when ema(15) crosses over sma(60) while Short signals happen when ema(15) crosses under sma(60). Long orders are closed when ema(15) crosses under sma(60) while Short orders are closed when ema(15) crosses over sma(60).
Exit conditions happen when (whichever came first):
Another Long/Short signal is generated
The Stop-loss level is reached
The Cut-off time is reached (14:25 every day)
*Disclaimers:
Futures Contracts Trading are subjected to a high degree of risk and price movements can fluctuate significantly. This script functions as a reference source and should be used after users have clearly understood how futures trading works, accessed their risk tolerance level, and are knowledgeable of the functioning logic behind the script.
Users are solely responsible for their investment decisions, and DNSE is not responsible for any potential losses from applying such a strategy to real-life trading activities. Past performance is not indicative/guarantee of future results, kindly reach out to us should you have specific questions about this script.
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Khám phá Pinescript được thiết kế riêng để giao dịch Hợp đồng tương lai VN30F1M trong ngày, chiến lược tập trung vào các đường SMA & EMA cắt nhau để xác định các điểm vào/ra tiềm năng. Chiến lược sẽ đóng tất cả các vị thế trước 14:25 để tránh giữ bất kỳ hợp đồng nào qua đêm.
Thiết lập & Kết quả backtest:
Chart 1 phút, vốn ban đầu là 100 triệu đồng, vào 4 hợp đồng mỗi lần, kết quả backtest từ tháng 1/2024 tới tháng 11/2024 mang lại lợi nhuận trên 40%, thực hiện hơn 1.000 giao dịch (trung bình 4 giao dịch/ngày), tỷ lệ giao dịch thắng ~ 30% với hệ số lợi nhuận là 1,10.
Thiết lập mặc định của chiến lược:
Đạt được một mức tối ưu ổn khi SMA và EMA periods được đặt lần lượt là 60 và 15 trong khi mức cắt lỗ được đặt thành 20 tick (2 điểm) từ giá vào.
Điều kiện Mở và Đóng vị thế:
Tín hiệu Long được tạo ra khi ema(15) cắt trên sma(60) trong khi tín hiệu Short xảy ra khi ema(15) cắt dưới sma(60). Lệnh Long được đóng khi ema(15) cắt dưới sma(60) trong khi lệnh Short được đóng khi ema(15) cắt lên sma(60).
Điều kiện đóng vị thể xảy ra khi (tùy điều kiện nào đến trước):
Một tín hiệu Long/Short khác được tạo ra
Giá chạm mức cắt lỗ
Lệnh chưa đóng nhưng tới giờ cut-off (14:25 hàng ngày)
*Tuyên bố miễn trừ trách nhiệm:
Giao dịch hợp đồng tương lai có mức rủi ro cao và giá có thể dao động đáng kể. Chiến lược này hoạt động như một nguồn tham khảo và nên được sử dụng sau khi người dùng đã hiểu rõ cách thức giao dịch hợp đồng tương lai, đã đánh giá mức độ chấp nhận rủi ro của bản thân và hiểu rõ về logic vận hành của chiến lược này.
Người dùng hoàn toàn chịu trách nhiệm về các quyết định đầu tư của mình và DNSE không chịu trách nhiệm về bất kỳ khoản lỗ tiềm ẩn nào khi áp dụng chiến lược này vào các hoạt động giao dịch thực tế. Hiệu suất trong quá khứ không chỉ ra/cam kết kết quả trong tương lai, vui lòng liên hệ với chúng tôi nếu bạn có thắc mắc cụ thể về chiến lược giao dịch này.
Hyperbolic Tangent Volatility Stop [InvestorUnknown]The Hyperbolic Tangent Volatility Stop (HTVS) is an advanced technical analysis tool that combines the smoothing capabilities of the Hyperbolic Tangent Moving Average (HTMA) with a volatility-based stop mechanism. This indicator is designed to identify trends and reversals while accounting for market volatility.
Hyperbolic Tangent Moving Average (HTMA):
The HTMA is at the heart of the HTVS. This custom moving average uses a hyperbolic tangent transformation to smooth out price fluctuations, focusing on significant trends while ignoring minor noise. The transformation reduces the sensitivity to sharp price movements, providing a clearer view of the underlying market direction.
The hyperbolic tangent function (tanh) is commonly used in mathematical fields like calculus, machine learning and signal processing due to its properties of “squashing” inputs into a range between -1 and 1. The function provides a non-linear transformation that can reduce the impact of extreme values while retaining a certain level of smoothness.
tanh(x) =>
e_x = math.exp(x)
e_neg_x = math.exp(-x)
(e_x - e_neg_x) / (e_x + e_neg_x)
The HTMA is calculated by applying a non-linear transformation to the difference between the source price and its simple moving average, then adjusting it using the standard deviation of the price data. The result is a moving average that better tracks the real market direction.
htma(src, len, mul) =>
tanh_src = tanh((src - ta.sma(src, len)) * mul) * ta.stdev(src, len) + ta.sma(src, len)
htma = ta.sma(tanh_src, len)
Important Note: The Hyperbolic Tangent function becomes less accurate with very high prices. For assets priced above 100,000, the results may deteriorate, and for prices exceeding 1 million, the function may stop functioning properly. Therefore, this indicator is better suited for assets with lower prices or lower price ratios.
Volatility Stop (VolStop):
HTVS employs a Volatility Stop mechanism based on the Average True Range (ATR). This stop dynamically adjusts based on market volatility, ensuring that the indicator adapts to changing conditions and avoids false signals in choppy markets.
The VolStop follows the price, with a higher ATR pushing the stop farther away to avoid premature exits during volatile periods. Conversely, when volatility is low, the stop tightens to lock in profits as the trend progresses.
The ATR Length and ATR Multiplier are customizable, allowing traders to control how tightly or loosely the stop follows the price.
pine_volStop(src, atrlen, atrfactor) =>
if not na(src)
var max = src
var min = src
var uptrend = true
var float stop = na
atrM = nz(ta.atr(atrlen) * atrfactor, ta.tr)
max := math.max(max, src)
min := math.min(min, src)
stop := nz(uptrend ? math.max(stop, max - atrM) : math.min(stop, min + atrM), src)
uptrend := src - stop >= 0.0
if uptrend != nz(uptrend , true)
max := src
min := src
stop := uptrend ? max - atrM : min + atrM
Backtest Mode:
HTVS includes a built-in backtest mode, allowing traders to evaluate the indicator's performance on historical data. In backtest mode, it calculates the cumulative equity curve and compares it to a simple buy and hold strategy.
Backtesting features can be adjusted to focus on specific signal types, such as Long Only, Short Only, or Long & Short.
An optional Buy and Hold Equity plot provides insight into how the indicator performs relative to simply holding the asset over time.
The indicator includes a Hints Table, which provides useful recommendations on how to best display the indicator for different use cases. For example, when using the overlay mode, it suggests displaying the indicator in the same pane as price action, while backtest mode is recommended to be used in a separate pane for better clarity.
The Hyperbolic Tangent Volatility Stop offers traders a balanced approach to trend-following, using the robustness of the HTMA for smoothing and the adaptability of the Volatility Stop to avoid whipsaw trades during volatile periods. With its backtesting features and alert system, this indicator provides a comprehensive toolkit for active traders.
RSI SMA Crossover StrategyOverview
RSI SMA Crossover Strategy works the same way as traditional MA crossover strategies, but using RSI instead of price. When RSI crosses over the SMA, a long position is opened (buy). When RSI crosses under the SMA, the long position is closed (sell).
This strategy can be very effective when the right inputs are used (see below). Be sure to use the backtesting tool to determine the optimal parameters for a given asset/timeframe.
Inputs/Parameters
RSI Length: length for RSI calculation (default = 50)
SMA Length: length for SMA calculation (default = 25)
Strategy Properties
Initial Capital = $1000
No default properties are defined for Slippage, Commission, etc, so be sure to set these values to get accurate backtesting results. This script is being published open-source for a reason - save yourself a copy and adjust the settings as you like!
Backtesting Results
Testing on Bitcoin (all time index) 1D chart, with all default parameters.
$1,000 initial investment on 10/07/2010 turns into almost $2.5 billion as of 08/30/2022 (compared to $334 million if the initial investment was held over the same period)
Remember, results can vary greatly based on the variables mentioned above, so always be sure to backtest.
Stochastic Pop and Drop by Jake Bernstein v1 [Bitduke]I found a simple strategy by Jake Bernstein, modified it a little and created a strategy with Risk Management System (SL+TP); After that I test it on the different cryptocurrency pairs.
About the Indicator
Basically it's the strategy of 2 indicators: Stochastic Oscillator to define the bias and Average Directional Index to confirm it.
One again, It uses Stochastic Oscillator to define the trading bias. In particular, the trading bias was deemed bullish when the weekly 14-period Stochastic Oscillator was above some default value (in him paper - 50) and rising and vice versa.
Once the trading bias is established, Steckler used the Average Directional Index (ADX) to define a slowdown in the trend. ADX measures the strength of the trend and a move below 20 signals a weak trend.
Modifications
I didn't implement Average Directional Index (ADX) and test just different sources for data, oscillator periods and different levels in relation to the crypto market.
So, it shows good results with two tight thresholds at 55 and 45 level.
The bar chart below the defining the bullish and bearish periods (green and red) and gives a signal to enter the trade (purple bars).
Backtesting
Backtested on XBTUSD , BTCPERP (FTX) pairs. You may notice it shows good results on 3h timeframe.
Relatively low drawdown
~ 10% (from 2019 to date) FTX
~ 22% (4 years from 2016) Bitmex
I backtested on the different altcoin pairs as well, but the results were just not good.
Relatively good results were shown by some index pairs from the FTX exchange ( FTX:SHITPERP ), but I think there is a few data for backtesting to be asure in them.
Bitmex 3h (2017 - 2020) :
i.imgur.com
FTX 3h (2019 - 2020):
i.imgur.com
Possible Improvements
- Regarding trading algorithm it would be good to check with strategy with ADX somehow. Maybe for the better entries
- As for Risk Management system, it can be improved by adding trailing stop to the strategy.
Link: school.stockcharts.com
RSI ADX Bollinger Analysis High-level purpose and design philosophy
This indicator — RSI-ADX-Bollinger Analysis — is a compact, educational market-analysis toolkit that blends momentum (RSI), trend strength (ADX), volatility structure (Bollinger Bands) and simple volumetrics to provide traders a snapshot of market condition and trade idea quality. The design philosophy is explicit and layered: use each component to answer a different question about price action (momentum, conviction, volatility, participation), then combine answers to form a more robust, explainable signal. The mashup is intended for analysis and learning, not automatic execution: it surfaces the why behind signals so traders can test, learn and apply rules with risk management.
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What each indicator contributes (component-by-component)
RSI (Relative Strength Index) — role and behavior: RSI measures short-term momentum by comparing recent gains to recent losses. A high RSI (near or above the overbought threshold) indicates strong recent buying pressure and potential exhaustion if price is extended. A low RSI (near or below the oversold threshold) indicates strong recent selling pressure and potential exhaustion or a value area for mean-reversion. In this dashboard RSI is used as the primary momentum trigger: it helps identify whether price is locally over-extended on the buy or sell side.
ADX (Average Directional Index) — role and behavior: ADX measures trend strength independently of direction. When ADX rises above a chosen threshold (e.g., 25), it signals that the market is trending with conviction; ADX below the threshold suggests range or weak trend. Because patterns and momentum signals perform differently in trending vs. ranging markets, ADX is used here as a filter: only when ADX indicates sufficient directional strength does the system treat RSI+BB breakouts as meaningful trade candidates.
Bollinger Bands — role and behavior: Bollinger Bands (20-period basis ± N standard deviations) show volatility envelope and relative price position vs. a volatility-adjusted mean. Price outside the upper band suggests pronounced extension relative to recent volatility; price outside the lower band suggests extended weakness. A band expansion (increasing width) signals volatility breakout potential; contraction signals range-bound conditions and potential squeeze. In this dashboard, Bollinger Bands provide the volatility/structural context: RSI extremes plus price beyond the band imply a stronger, volatility-backed move.
Volume split & basic MA trend — role and behavior: Buy-like and sell-like volume (simple heuristic using close>open or closeopen) or sell-like (close1.2 for validation and compare win rate and expectancy.
4. TF alignment: Accept signals only when higher timeframe (e.g., 4h) trend agrees — compare results.
5. Parameter sensitivity: Vary RSI threshold (70/30 vs 80/20), Bollinger stddev (2 vs 2.5), and ADX threshold (25 vs 30) and measure stability of results.
These exercises teach both statistical thinking and the specific failure modes of the mashup.
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Limitations, failure modes and caveats (explicit & teachable)
• ADX and Bollinger measures lag during fast-moving news events — signals can be late or wrong during earnings, macro shocks, or illiquid sessions.
• Volume classification by open/close is a heuristic; it does not equal TAPEDATA, footprint or signed volume. Use it as supportive evidence, not definitive proof.
• RSI can remain overbought or oversold for extended stretches in persistent trends — relying solely on RSI extremes without ADX or BB context invites large drawdowns.
• Small-cap or low-liquidity instruments yield noisy band behavior and unreliable volume ratios.
Being explicit about these limitations is a strong point in a TradingView description — it demonstrates transparency and educational intent.
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Originality & mashup justification (text you can paste)
This script intentionally combines classical momentum (RSI), volatility envelope (Bollinger Bands) and trend-strength (ADX) because each indicator answers a different and complementary question: RSI answers is price locally extreme?, Bollinger answers is price outside normal volatility?, and ADX answers is the market moving with conviction?. Volume participation then acts as a practical check for real market involvement. This combination is not a simple “indicator mashup”; it is a designed ensemble where each element reduces the others’ failure modes and together produce a teachable, testable signal framework. The script’s purpose is educational and analytical — to show traders how to interpret the interplay of momentum, volatility, and trend strength.
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TradingView publication guidance & compliance checklist
To satisfy TradingView rules about mashups and descriptions, include the following items in your script description (without exposing source code):
1. Purpose statement: One or two lines describing the script’s objective (educational multi-indicator market overview and idea filter).
2. Component list: Name the major modules (RSI, Bollinger Bands, ADX, volume heuristic, SMA trend checks, signal tracking) and one-sentence reason for each.
3. How they interact: A succinct non-code explanation: “RSI finds momentum extremes; Bollinger confirms volatility expansion; ADX confirms trend strength; all three must align for a BUY/SELL.”
4. Inputs: List adjustable inputs (RSI length and thresholds, BB length & stddev, ADX threshold & smoothing, volume MA, table position/size).
5. Usage instructions: Short workflow (check TF alignment → confirm participation → define stop & R:R → backtest).
6. Limitations & assumptions: Explicitly state volume is approximated, ADX has lag, and avoid promising guaranteed profits.
7. Non-promotional language: No external contact info, ads, claims of exclusivity or guaranteed outcomes.
8. Trademark clause: If you used trademark symbols, remove or provide registration proof.
9. Risk disclaimer: Add the copy-ready disclaimer below.
This matches TradingView’s request for meaningful descriptions that explain originality and inter-component reasoning.
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Copy-ready short publication description (paste into TradingView)
Advanced RSI-ADX-Bollinger Market Overview — educational multi-indicator dashboard. This script combines RSI (momentum extremes), Bollinger Bands (volatility envelope and band expansion), ADX (trend strength), simple SMA trend bias and a basic buy/sell volume heuristic to surface high-quality idea candidates. Signals require alignment of momentum, volatility expansion and rising ADX; volume participation is displayed to support signal confidence. Inputs are configurable (RSI length/levels, BB length/stddev, ADX length/threshold, volume MA, display options). This tool is intended for analysis and learning — not for automated execution. Users should back test and apply robust risk management. Limitations: volume classification here is a heuristic (close>open), ADX and BB measures lag in fast news events, and results vary by instrument liquidity.
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Copy-ready risk & misuse disclaimer (paste into description or help file)
This script is provided for educational and analytical purposes only and does not constitute financial or investment advice. It does not guarantee profits. Indicators are heuristics and may give false or late signals; always back test and paper-trade before using real capital. The author is not responsible for trading losses resulting from the use or misuse of this indicator. Use proper position sizing and risk controls.
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Risk Disclaimer: This tool is provided for education and analysis only. It is not financial advice and does not guarantee returns. Users assume all risk for trades made based on this script. Back test thoroughly and use proper risk management.
AVWAP+RSI Confluence — 1R TesterRSI + 1R ATR - Monthly P\&L (v4)
WHAT THIS STRATEGY DOES (OVERVIEW)
* Pine strategy (v4) that combines a simple momentum trigger with a symmetric 1R ATR risk model and an on-chart Monthly/Yearly P\&L table.
* Momentum filter: trades only when RSI crosses its own SMA in the direction of the trend (price vs Trend EMA).
* Risk engine: exits use fixed 1R ATR brackets captured at entry (no drifting targets/stops).
* Accounting: the table aggregates percentage returns by month and year using strategy equity.
ENTRY LOGIC (LONGS & OPTIONAL SHORTS)
Indicators used:
* RSI(rsiLen) and its SMA: SMA(RSI, rsiMaLen)
* Trend filter: EMA(emaTrendLen) on price
Longs:
1. RSI crosses above its RSI SMA
2. RSI > rsiBuyThr (filters weak momentum)
3. Close > EMA(emaTrendLen)
Shorts (optional via enableShort):
1. RSI crosses below its RSI SMA
2. RSI < rsiSellThr
3. Close < EMA(emaTrendLen)
EXIT LOGIC AND RISK MODEL (1R ATR)
* On entry, snapshot ATR(atrLen) into atrAtEntry and the average fill price into entryPx.
* Longs: stop = entryPx - ATR \* atrMult; target = entryPx + ATR \* atrMult
* Shorts: mirrored.
* Stops and targets are posted immediately and remain fixed for the life of the trade.
POSITION SIZING AND COSTS
* Default position size: 25% of equity per trade (adjustable in Properties/inputs).
* Commission percent and a small slippage are set in strategy() so backtests include friction by default.
MONTHLY / YEARLY P\&L TABLE (HOW IT WORKS)
* Uses strategy equity to compute bar returns: equity / equity\ - 1.
* Compounds bar returns into current month and current year; commits each finished period at month/year change (or last bar).
* Renders rows as years; columns Jan..Dec plus a Year total column.
* Cells colored by sign; precision and maximum rows are controlled by inputs.
* Values represent percentage returns, not currency P\&L.
VISUAL AIDS
* Two pivot trails (pivot high/low) are plotted for context only; they do not affect entries or exits.
CUSTOMIZATION TIPS
* Raise rsiBuyThr (long) or lower rsiSellThr (short) to filter weak momentum.
* Increase emaTrendLen to tighten trend alignment.
* Adjust atrLen and atrMult to fit your timeframe/instrument volatility.
* Leave enableShort = false if you prefer long-only behavior or shorting is constrained.
NON-REPAINTING AND BACKTEST NOTES
* Signals use bar-close crosses of built-in indicators (RSI, EMA, ATR); no future bars are referenced.
* calc\_on\_every\_tick = true for responsive visuals; Strategy Tester evaluates on bar close in history.
* Backtest stop/limit fills are simulated and may differ from live execution/liquidity.
DISCLAIMERS
* Educational use only. This is not financial advice. Markets involve risk. Past performance does not guarantee future results.
INPUTS (QUICK REFERENCE)
* rsiLen, rsiMaLen, rsiBuyThr, rsiSellThr
* emaTrendLen
* atrLen, atrMult, enableShort
* leftBars, rightBars, prec, showTable, maxYearsRows
SHORT TAGLINE
RSI momentum with 1R ATR brackets and a built-in Monthly/Yearly P\&L table.
TAGS
strategy, RSI, ATR, trend, risk-management, backtest, Pine-v4
Machine Learning BBPct [BackQuant]Machine Learning BBPct
What this is (in one line)
A Bollinger Band %B oscillator enhanced with a simplified K-Nearest Neighbors (KNN) pattern matcher. The model compares today’s context (volatility, momentum, volume, and position inside the bands) to similar situations in recent history and blends that historical consensus back into the raw %B to reduce noise and improve context awareness. It is informational and diagnostic—designed to describe market state, not to sell a trading system.
Background: %B in plain terms
Bollinger %B measures where price sits inside its dynamic envelope: 0 at the lower band, 1 at the upper band, ~ 0.5 near the basis (the moving average). Readings toward 1 indicate pressure near the envelope’s upper edge (often strength or stretch), while readings toward 0 indicate pressure near the lower edge (often weakness or stretch). Because bands adapt to volatility, %B is naturally comparable across regimes.
Why add (simplified) KNN?
Classic %B is reactive and can be whippy in fast regimes. The simplified KNN layer builds a “nearest-neighbor memory” of recent market states and asks: “When the market looked like this before, where did %B tend to be next bar?” It then blends that estimate with the current %B. Key ideas:
• Feature vector . Each bar is summarized by up to five normalized features:
– %B itself (normalized)
– Band width (volatility proxy)
– Price momentum (ROC)
– Volume momentum (ROC of volume)
– Price position within the bands
• Distance metric . Euclidean distance ranks the most similar recent bars.
• Prediction . Average the neighbors’ prior %B (lagged to avoid lookahead), inverse-weighted by distance.
• Blend . Linearly combine raw %B and KNN-predicted %B with a configurable weight; optional filtering then adapts to confidence.
This remains “simplified” KNN: no training/validation split, no KD-trees, no scaling beyond windowed min-max, and no probabilistic calibration.
How the script is organized (by input groups)
1) BBPct Settings
• Price Source – Which price to evaluate (%B is computed from this).
• Calculation Period – Lookback for SMA basis and standard deviation.
• Multiplier – Standard deviation width (e.g., 2.0).
• Apply Smoothing / Type / Length – Optional smoothing of the %B stream before ML (EMA, RMA, DEMA, TEMA, LINREG, HMA, etc.). Turning this off gives you the raw %B.
2) Thresholds
• Overbought/Oversold – Default 0.8 / 0.2 (inside ).
• Extreme OB/OS – Stricter zones (e.g., 0.95 / 0.05) to flag stretch conditions.
3) KNN Machine Learning
• Enable KNN – Switch between pure %B and hybrid.
• K (neighbors) – How many historical analogs to blend (default 8).
• Historical Period – Size of the search window for neighbors.
• ML Weight – Blend between raw %B and KNN estimate.
• Number of Features – Use 2–5 features; higher counts add context but raise the risk of overfitting in short windows.
4) Filtering
• Method – None, Adaptive, Kalman-style (first-order),
or Hull smoothing.
• Strength – How aggressively to smooth. “Adaptive” uses model confidence to modulate its alpha: higher confidence → stronger reliance on the ML estimate.
5) Performance Tracking
• Win-rate Period – Simple running score of past signal outcomes based on target/stop/time-out logic (informational, not a robust backtest).
• Early Entry Lookback – Horizon for forecasting a potential threshold cross.
• Profit Target / Stop Loss – Used only by the internal win-rate heuristic.
6) Self-Optimization
• Enable Self-Optimization – Lightweight, rolling comparison of a few canned settings (K = 8/14/21 via simple rules on %B extremes).
• Optimization Window & Stability Threshold – Governs how quickly preferred K changes and how sensitive the overfitting alarm is.
• Adaptive Thresholds – Adjust the OB/OS lines with volatility regime (ATR ratio), widening in calm markets and tightening in turbulent ones (bounded 0.7–0.9 and 0.1–0.3).
7) UI Settings
• Show Table / Zones / ML Prediction / Early Signals – Toggle informational overlays.
• Signal Line Width, Candle Painting, Colors – Visual preferences.
Step-by-step logic
A) Compute %B
Basis = SMA(source, len); dev = stdev(source, len) × multiplier; Upper/Lower = Basis ± dev.
%B = (price − Lower) / (Upper − Lower). Optional smoothing yields standardBB .
B) Build the feature vector
All features are min-max normalized over the KNN window so distances are in comparable units. Features include normalized %B, normalized band width, normalized price ROC, normalized volume ROC, and normalized position within bands. You can limit to the first N features (2–5).
C) Find nearest neighbors
For each bar inside the lookback window, compute the Euclidean distance between current features and that bar’s features. Sort by distance, keep the top K .
D) Predict and blend
Use inverse-distance weights (with a strong cap for near-zero distances) to average neighbors’ prior %B (lagged by one bar). This becomes the KNN estimate. Blend it with raw %B via the ML weight. A variance of neighbor %B around the prediction becomes an uncertainty proxy ; combined with a stability score (how long parameters remain unchanged), it forms mlConfidence ∈ . The Adaptive filter optionally transforms that confidence into a smoothing coefficient.
E) Adaptive thresholds
Volatility regime (ATR(14) divided by its 50-bar SMA) nudges OB/OS thresholds wider or narrower within fixed bounds. The aim: comparable extremeness across regimes.
F) Early entry heuristic
A tiny two-step slope/acceleration probe extrapolates finalBB forward a few bars. If it is on track to cross OB/OS soon (and slope/acceleration agree), it flags an EARLY_BUY/SELL candidate with an internal confidence score. This is explicitly a heuristic—use as an attention cue, not a signal by itself.
G) Informational win-rate
The script keeps a rolling array of trade outcomes derived from signal transitions + rudimentary exits (target/stop/time). The percentage shown is a rough diagnostic , not a validated backtest.
Outputs and visual language
• ML Bollinger %B (finalBB) – The main line after KNN blending and optional filtering.
• Gradient fill – Greenish tones above 0.5, reddish below, with intensity following distance from the midline.
• Adaptive zones – Overbought/oversold and extreme bands; shaded backgrounds appear at extremes.
• ML Prediction (dots) – The KNN estimate plotted as faint circles; becomes bright white when confidence > 0.7.
• Early arrows – Optional small triangles for approaching OB/OS.
• Candle painting – Light green above the midline, light red below (optional).
• Info panel – Current value, signal classification, ML confidence, optimized K, stability, volatility regime, adaptive thresholds, overfitting flag, early-entry status, and total signals processed.
Signal classification (informational)
The indicator does not fire trade commands; it labels state:
• STRONG_BUY / STRONG_SELL – finalBB beyond extreme OS/OB thresholds.
• BUY / SELL – finalBB beyond adaptive OS/OB.
• EARLY_BUY / EARLY_SELL – forecast suggests a near-term cross with decent internal confidence.
• NEUTRAL – between adaptive bands.
Alerts (what you can automate)
• Entering adaptive OB/OS and extreme OB/OS.
• Midline cross (0.5).
• Overfitting detected (frequent parameter flipping).
• Early signals when early confidence > 0.7.
These are purely descriptive triggers around the indicator’s state.
Practical interpretation
• Mean-reversion context – In range markets, adaptive OS/OB with ML smoothing can reduce whipsaws relative to raw %B.
• Trend context – In persistent trends, the KNN blend can keep finalBB nearer the mid/upper region during healthy pullbacks if history supports similar contexts.
• Regime awareness – Watch the volatility regime and adaptive thresholds. If thresholds compress (high vol), “OB/OS” comes sooner; if thresholds widen (calm), it takes more stretch to flag.
• Confidence as a weight – High mlConfidence implies neighbors agree; you may rely more on the ML curve. Low confidence argues for de-emphasizing ML and leaning on raw %B or other tools.
• Stability score – Rising stability indicates consistent parameter selection and fewer flips; dropping stability hints at a shifting backdrop.
Methodological notes
• Normalization uses rolling min-max over the KNN window. This is simple and scale-agnostic but sensitive to outliers; the distance metric will reflect that.
• Distance is unweighted Euclidean. If you raise featureCount, you increase dimensionality; consider keeping K larger and lookback ample to avoid sparse-neighbor artifacts.
• Lag handling intentionally uses neighbors’ previous %B for prediction to avoid lookahead bias.
• Self-optimization is deliberately modest: it only compares a few canned K/threshold choices using simple “did an extreme anticipate movement?” scoring, then enforces a stability regime and an overfitting guard. It is not a grid search or GA.
• Kalman option is a first-order recursive filter (fixed gain), not a full state-space estimator.
• Hull option derives a dynamic length from 1/strength; it is a convenience smoothing alternative.
Limitations and cautions
• Non-stationarity – Nearest neighbors from the recent window may not represent the future under structural breaks (policy shifts, liquidity shocks).
• Curse of dimensionality – Adding features without sufficient lookback can make genuine neighbors rare.
• Overfitting risk – The script includes a crude overfitting detector (frequent parameter flips) and will fall back to defaults when triggered, but this is only a guardrail.
• Win-rate display – The internal score is illustrative; it does not constitute a tradable backtest.
• Latency vs. smoothness – Smoothing and ML blending reduce noise but add lag; tune to your timeframe and objectives.
Tuning guide
• Short-term scalping – Lower len (10–14), slightly lower multiplier (1.8–2.0), small K (5–8), featureCount 3–4, Adaptive filter ON, moderate strength.
• Swing trading – len (20–30), multiplier ~2.0, K (8–14), featureCount 4–5, Adaptive thresholds ON, filter modest.
• Strong trends – Consider higher adaptive_upper/lower bounds (or let volatility regime do it), keep ML weight moderate so raw %B still reflects surges.
• Chop – Higher ML weight and stronger Adaptive filtering; accept lag in exchange for fewer false extremes.
How to use it responsibly
Treat this as a state descriptor and context filter. Pair it with your execution signals (structure breaks, volume footprints, higher-timeframe bias) and risk management. If mlConfidence is low or stability is falling, lean less on the ML line and more on raw %B or external confirmation.
Summary
Machine Learning BBPct augments a familiar oscillator with a transparent, simplified KNN memory of recent conditions. By blending neighbors’ behavior into %B and adapting thresholds to volatility regime—while exposing confidence, stability, and a plain early-entry heuristic—it provides an informational, probability-minded view of stretch and reversion that you can interpret alongside your own process.
Prop Firm Business SimulatorThe prop firm business simulator is exactly what it sounds like. It's a plug and play tool to test out any tradingview strategy and simulate hypothetical performance on CFD Prop Firms.
Now what is a modern day CFD Prop Firm?
These companies sell simulated trading challenges for a challenge fee. If you complete the challenge you get access to simulated capital and you get a portion of the profits you make on those accounts payed out.
I've included some popular firms in the code as presets so it's easy to simulate them. Take into account that this info will likely be out of date soon as these prices and challenge conditions change.
Also, this tool will never be able to 100% simulate prop firm conditions and all their rules. All I aim to do with this tool is provide estimations.
Now why is this tool helpful?
Most traders on here want to turn their passion into their full-time career, prop firms have lately been the buzz in the trading community and market themselves as a faster way to reach that goal.
While this all sounds great on paper, it is sometimes hard to estimate how much money you will have to burn on challenge fees and set realistic monthly payout expectations for yourself and your trading. This is where this tool comes in.
I've specifically developed this for traders that want to treat prop firms as a business. And as a business you want to know your monthly costs and income depending on the trading strategy and prop firm challenge you are using.
How to use this tool
It's quite simple you remove the top part of the script and replace it with your own strategy. Make sure it's written in same version of pinescript before you do that.
//--$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$--//--------------------------------------------------------------------------------------------------------------------------$$$$$$
//--$$$$$--Strategy-- --$$$$$$--// ******************************************************************************************************************************
//--$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$--//--------------------------------------------------------------------------------------------------------------------------$$$$$$
length = input.int(20, minval=1, group="Keltner Channel Breakout")
mult = input(2.0, "Multiplier", group="Keltner Channel Breakout")
src = input(close, title="Source", group="Keltner Channel Breakout")
exp = input(true, "Use Exponential MA", display = display.data_window, group="Keltner Channel Breakout")
BandsStyle = input.string("Average True Range", options = , title="Bands Style", display = display.data_window, group="Keltner Channel Breakout")
atrlength = input(10, "ATR Length", display = display.data_window, group="Keltner Channel Breakout")
esma(source, length)=>
s = ta.sma(source, length)
e = ta.ema(source, length)
exp ? e : s
ma = esma(src, length)
rangema = BandsStyle == "True Range" ? ta.tr(true) : BandsStyle == "Average True Range" ? ta.atr(atrlength) : ta.rma(high - low, length)
upper = ma + rangema * mult
lower = ma - rangema * mult
//--Graphical Display--// *-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-$$$$$$
u = plot(upper, color=#2962FF, title="Upper", force_overlay=true)
plot(ma, color=#2962FF, title="Basis", force_overlay=true)
l = plot(lower, color=#2962FF, title="Lower", force_overlay=true)
fill(u, l, color=color.rgb(33, 150, 243, 95), title="Background")
//--Risk Management--// *-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-$$$$$$
riskPerTradePerc = input.float(1, title="Risk per trade (%)", group="Keltner Channel Breakout")
le = high>upper ? false : true
se = lowlower
strategy.entry('PivRevLE', strategy.long, comment = 'PivRevLE', stop = upper, qty=riskToLots)
if se and upper>lower
strategy.entry('PivRevSE', strategy.short, comment = 'PivRevSE', stop = lower, qty=riskToLots)
The tool will then use the strategy equity of your own strategy and use this to simulat prop firms. Since these CFD prop firms work with different phases and payouts the indicator will simulate the gains until target or max drawdown / daily drawdown limit gets reached. If it reaches target it will go to the next phase and keep on doing that until it fails a challenge.
If in one of the phases there is a reward for completing, like a payout, refund, extra it will add this to the gains.
If you fail the challenge by reaching max drawdown or daily drawdown limit it will substract the challenge fee from the gains.
These gains are then visualised in the calendar so you can get an idea of yearly / monthly gains of the backtest. Remember, it is just a backtest so no guarantees of future income.
The bottom pane (non-overlay) is visualising the performance of the backtest during the phases. This way u can check if it is realistic. For instance if it only takes 1 bar on chart to reach target you are probably risking more than the firm wants you to risk. Also, it becomes much less clear if daily drawdown got hit in those high risk strategies, the results will be less accurate.
The daily drawdown limit get's reset every time there is a new dayofweek on chart.
If you set your prop firm preset setting to "'custom" the settings below that are applied as your prop firm settings. Otherwise it will use one of the template by default it's FTMO 100K.
The strategy I'm using as an example in this script is a simple Keltner Channel breakout strategy. I'm using a 0.05% commission per trade as that is what I found most common on crypto exchanges and it's close to the commissions+spread you get on a cfd prop firm. I'm targeting a 1% risk per trade in the backtest to try and stay within prop firm boundaries of max 1% risk per trade.
Lastly, the original yearly and monthly performance table was developed by Quantnomad and I've build ontop of that code. Here's a link to the original publication:
That's everything for now, hope this indicator helps people visualise the potential of prop firms better or to understand that they are not a good fit for their current financial situation.
BIN Based Support and Resistance [SS]This indicator presents a version of an alternative way to determine support and resistance, using a method called "Bins".
Bins provide for a flexible and interesting way to determine support and resistance levels.
First off, let's discuss BINS:
Bins are ranges or containers into which your data points can be sorted. For example, if you're grouping ages, you might have bins like 0–18, 19–35, 36–50, and 51+. Any data point within these intervals gets placed in the corresponding bin.
Binning simplifies complex data sets by grouping values into categories. This is useful for such things as
Visualizing data in histograms or bar charts.
Reducing noise and highlighting trends.
This indicator groups the price action into 10 separate bins. It determines the Support / Resistance level by averaging the values in the Bins to find an iteration of the "central tendency" or average reoccurring value.
Pros and Cons
Since this is a different approach to support and resistance, I think its important to highlight some of the pros and advantages, but also be open about the cons.
First off the PROS
Bin Based Support and Resistance Levels dynamically adjust to ranges as opposed to hard / fast peaks and valleys. This makes them better at analyzing price action vs simply drawing lines at random peaks and valleys.
Because Bins are analyzing ALL PA within a period's max and min range, Bin Support and Resistance can actually be used similar to Volume profile, where you are able to identify a pseudo-POC, or areas where price tends to consolidate. Take a look at this example on SPY:
You can see these 2 SR lines are close together. This represents that this general price range is an area where price likes to accumulate/consolidate. You can see the SPY ended up coming back to this range and consolidating there for a bit.
This is a strength of using a BIN based approach to calculating support and resistance, because as indicated before, it looks at price action vs peaks and valleys.
As a tip, these areas are areas you want to wait for a break in one direction or the other.
The indicator provides for backtest results of the support and resistance lines, to see how many times certain areas acted as resistance or support. Because this is analyzing and distributing PA evenly throughout the period's max and min, the indicator can tell you which areas tend to have higher rejection zones and which have higher support zones.
Now the CONS
Because bin based SR take an average approach, the SR lines can sometimes be slightly broken before the ticker finds rejection:
To combat this, make sure there is confirmed support. How the indicator actually backtests these lines is by waiting to see if the ticker has 3 consecutive closes above the support line or below the resistance line. So these are things to be mindful of.
It doesn't consider pivots. Most support and resistance indicators either identify max and min peaks and valleys or use pivot points. Pivot points are a great way to identify peaks and valleys and thus by extension support and resistance. However, this is also somewhat of a strength, as using BINS forces the indicator to consider ALL price action and not just the extremes (highs and lows).
Can be slightly skewed in highly volatile environments. Any time there is a massive drop or rally, it can skew the indicator to give extreme ranges to both ends. For example, the Tariff news collapse on ES1!:
Owning to limitations in lookback length, sometimes the min and max range can be exceeded and other traditional areas of support / resistance is where a ticker will find support.
Using the indicator
Here are some basic use/functionalities of the indicator:
Selecting display of backtest results: You can select to have the backtest results shown in a table:
Or directly on the lines:
Inversely, you can toggle them off completely:
You can modify the lookback length. The suggested lookback length is between 250 to 500 candles on smaller timeframes. I also suggest 252 on daily timeframes (which represents 1 trading year).
And that's the indicator!
It is very easy to use, so you should pick it up in no time!
Enjoy and as always, 🚀🚀 safe trades! 🚀🚀
PDF MA For Loop [BackQuant]PDF MA For Loop
Introducing the PDF MA For Loop, an innovative trading indicator that combines Probability Density Function (PDF) smoothing with a dynamic for-loop scoring mechanism. This advanced tool provides traders with precise trend-following signals, helping to identify long and short opportunities with improved clarity and adaptability to market conditions.
If you would like to check out the stand alone PDF Moving Average:
Core Concept: Probability Density Function (PDF) Smoothing
The PDF smoothing method is a unique approach that applies adaptive weights to price data based on a Probability Density Function. This ensures that recent data points receive appropriate emphasis while maintaining a smooth transition across the data set. The result is a moving average that is not only smoother but also more responsive to market changes.
Key parameters in PDF smoothing:
Variance : Controls the spread of the PDF, where a higher value results in broader smoothing and a lower value makes the moving average more sensitive.
Mean : Centers the PDF around a specific value, influencing the weighting and responsiveness of the smoothing process.
By combining PDF smoothing with traditional moving averages (EMA or SMA), the indicator creates a hybrid signal that balances responsiveness and reliability.
For-Loop Scoring Mechanism
At the heart of this indicator is the for-loop scoring mechanism, which evaluates the smoothed PDF moving average over a defined range of historical data points. This process assigns a score to the current market condition based on whether the PDF moving average is greater than or less than previous values.
Long Signal: A long signal is generated when the score exceeds the Long Threshold (default set at 40), indicating upward momentum.
Short Signal: A short signal is triggered when the score crosses below the Short Threshold (default set at -10), suggesting potential downward momentum.
This dynamic scoring system ensures that the indicator remains adaptive, capturing trends and shifts in market sentiment effectively.
Customization Options
The PDF MA For Loop includes a variety of customizable settings to fit different trading styles and strategies:
Calculation Settings
Price Source : Select the input price for the calculation (default is the close price).
Smoothing Method : Choose between EMA or SMA for the additional smoothing layer, providing flexibility to adapt to market conditions.
Smoothing Period : Adjust the lookback period for the smoothing function, with shorter periods providing more sensitivity and longer periods offering greater stability.
Variance & Mean : Fine-tune the PDF function parameters to control the weighting of the smoothing process.
Signal Settings
Thresholds : Customize the upper and lower thresholds to define the sensitivity of the long and short signals.
For Loop Range : Set the range of historical data points analyzed by the for-loop, influencing the depth of the scoring mechanism.
UI Settings
Signal Line Width: Adjust the thickness of the plotted signal line for better visibility.
Candle Coloring: Enable or disable the coloring of candlesticks based on trend direction (green for long, red for short, gray for neutral).
Background Coloring: Add background shading to highlight long and short signals for an enhanced visual experience.
Alerts and Automation
The indicator includes built-in alert conditions to notify traders of important market events:
Long Signal Alert: Notifies when the score exceeds the upper threshold, indicating a bullish trend.
Short Signal Alert: Notifies when the score crosses below the lower threshold, signaling a bearish trend.
These alerts can be configured for real-time notifications, allowing traders to respond quickly to market changes without constant chart monitoring.
Trading Applications
The PDF MA For Loop is versatile and can be applied across various trading strategies and market conditions:
Trend Following: The PDF smoothing method combined with for-loop scoring makes this indicator particularly effective for identifying and following trends.
Reversal Trading: By observing the thresholds and score, traders can anticipate potential reversals when the trend shifts from long to short (or vice versa).
Risk Management: The dynamic thresholds and scoring provide clear signals, allowing traders to enter and exit trades with greater confidence and precision.
Final Thoughts
The PDF MA For Loopis merges advanced mathematical concepts with practical trading tools. By leveraging Probability Density Function smoothing and a dynamic for-loop scoring system, it provides traders with clear, actionable signals while adapting to market conditions.
Whether you’re looking for an edge in trend-following strategies or seeking precision in identifying reversals, this indicator offers the flexibility and power to enhance your trading decisions
As always, backtesting and integrating the PDF MA For Loop into a comprehensive trading strategy is recommended for optimal performance, as no single indicator should be used in isolation.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
MACD Aggressive Scalp SimpleComment on the Script
Purpose and Structure:
The script is a scalping strategy based on the MACD indicator combined with EMA (50) as a trend filter.
It uses the MACD histogram's crossover/crossunder of zero to trigger entries and exits, allowing the trader to capitalize on short-term momentum shifts.
The use of strategy.close ensures that positions are closed when specified conditions are met, although adjustments were made to align with Pine Script version 6.
Strengths:
Simplicity and Clarity: The logic is straightforward and focuses on essential scalping principles (momentum-based entries and exits).
Visual Indicators: The plotted MACD line, signal line, and histogram columns provide clear visual feedback for the strategy's operation.
Trend Confirmation: Incorporating the EMA(50) as a trend filter helps avoid trades that go against the prevailing trend, reducing the likelihood of false signals.
Dynamic Exit Conditions: The conditional logic for closing positions based on weakening momentum (via MACD histogram change) is a good way to protect profits or minimize losses.
Potential Improvements:
Parameter Inputs:
Make the MACD (12, 26, 9) and EMA(50) values adjustable by the user through input statements for better customization during backtesting.
Example:
pine
Copy code
macdFast = input(12, title="MACD Fast Length")
macdSlow = input(26, title="MACD Slow Length")
macdSignal = input(9, title="MACD Signal Line Length")
emaLength = input(50, title="EMA Length")
Stop Loss and Take Profit:
The strategy currently lacks explicit stop-loss or take-profit levels, which are critical in a scalping strategy to manage risk and lock in profits.
ATR-based or fixed-percentage exits could be added for better control.
Position Size and Risk Management:
While the script uses 50% of equity per trade, additional options (e.g., fixed position sizes or risk-adjusted sizes) would be beneficial for flexibility.
Avoid Overlapping Signals:
Add logic to prevent overlapping signals (e.g., opening a new position immediately after closing one on the same bar).
Backtesting Optimization:
Consider adding labels or markers (label.new or plotshape) to visualize entry and exit points on the chart for better debugging and analysis.
The inclusion of performance metrics like max drawdown, Sharpe ratio, or profit factor would help assess the strategy's robustness during backtesting.
Compatibility with Live Trading:
The strategy could be further enhanced with alert conditions using alertcondition to notify the trader of buy/sell signals in real-time.
MadTrend [InvestorUnknown]The MadTrend indicator is an experimental tool that combines the Median and Median Absolute Deviation (MAD) to generate signals, much like the popular Supertrend indicator. In addition to identifying Long and Short positions, MadTrend introduces RISK-ON and RISK-OFF states for each trade direction, providing traders with nuanced insights into market conditions.
Core Concepts
Median and Median Absolute Deviation (MAD)
Median: The middle value in a sorted list of numbers, offering a robust measure of central tendency less affected by outliers.
Median Absolute Deviation (MAD): Measures the average distance between each data point and the median, providing a robust estimation of volatility.
Supertrend-like Functionality
MadTrend utilizes the median and MAD in a manner similar to how Supertrend uses averages and volatility measures to determine trend direction and potential reversal points.
RISK-ON and RISK-OFF States
RISK-ON: Indicates favorable conditions for entering or holding a position in the current trend direction.
RISK-OFF: Suggests caution, signaling RISK-ON end and potential trend weakening or reversal.
Calculating MAD
The mad function calculates the median of the absolute deviations from the median, providing a robust measure of volatility.
// Function to calculate the Median Absolute Deviation (MAD)
mad(series float src, simple int length) =>
med = ta.median(src, length) // Calculate median
abs_deviations = math.abs(src - med) // Calculate absolute deviations from median
ta.median(abs_deviations, length) // Return the median of the absolute deviations
MADTrend Function
The MADTrend function calculates the median and MAD-based upper (med_p) and lower (med_m) bands. It determines the trend direction based on price crossing these bands.
MADTrend(series float src, simple int length, simple float mad_mult) =>
// Calculate MAD (volatility measure)
mad_value = mad(close, length)
// Calculate the MAD-based moving average by scaling the price data with MAD
median = ta.median(close, length)
med_p = median + (mad_value * mad_mult)
med_m = median - (mad_value * mad_mult)
var direction = 0
if ta.crossover(src, med_p)
direction := 1
else if ta.crossunder(src, med_m)
direction := -1
Trend Direction and Signals
Long Position (direction = 1): When the price crosses above the upper MAD band (med_p).
Short Position (direction = -1): When the price crosses below the lower MAD band (med_m).
RISK-ON: When the price moves further in the direction of the trend (beyond median +- MAD) after the initial signal.
RISK-OFF: When the price retraces towards the median, signaling potential weakening of the trend.
RISK-ON and RISK-OFF States
RISK-ON LONG: Price moves above the upper band after a Long signal, indicating strengthening bullish momentum.
RISK-OFF LONG: Price falls back below the upper band, suggesting potential weakness in the bullish trend.
RISK-ON SHORT: Price moves below the lower band after a Short signal, indicating strengthening bearish momentum.
RISK-OFF SHORT: Price rises back above the lower band, suggesting potential weakness in the bearish trend.
Picture below show example RISK-ON periods which can be identified by “cloud”
Note: Highlighted areas on the chart indicating RISK-ON and RISK-OFF periods for both Long and Short positions.
Implementation Details
Inputs and Parameters:
Source (input_src): The price data used for calculations (e.g., close, open, high, low).
Median Length (length): The number of periods over which the median and MAD are calculated.
MAD Multiplier (mad_mult): Determines the distance of the upper and lower bands from the median.
Calculations:
Median and MAD are recalculated each period based on the specified length.
Upper (med_p) and Lower (med_m) Bands are computed by adding and subtracting the scaled MAD from the median.
Visual representation of the indicator on a price chart:
Backtesting and Performance Metrics
The MadTrend indicator includes a Backtesting Mode with a performance metrics table to evaluate its effectiveness compared to a simple buy-and-hold strategy.
Equity Calculation:
Calculates the equity curve based on the signals generated by the indicator.
Performance Metrics:
Metrics such as Mean Returns, Standard Deviation, Sharpe Ratio, Sortino Ratio, and Omega Ratio are computed.
The metrics are displayed in a table for both the strategy and the buy-and-hold approach.
Note: Due to the use of labels and plot shapes, automatic chart scaling may not function ideally in Backtest Mode.
Alerts and Notifications
MadTrend provides alert conditions to notify traders of significant events:
Trend Change Alerts
RISK-ON and RISK-OFF Alerts - Provides real-time notifications about the RISK-ON and RISK-OFF states for proactive trade management.
Customization and Calibration
Default Settings: The provided default settings are experimental and not optimized. They serve as a starting point for users.
Parameter Adjustment: Traders are encouraged to calibrate the indicator's parameters (e.g., length, mad_mult) to suit their specific trading style and the characteristics of the asset being analyzed.
Source Input: The indicator allows for different price inputs (open, high, low, close, etc.), offering flexibility in how the median and MAD are calculated.
Important Notes
Market Conditions: The effectiveness of the MadTrend indicator can vary across different market conditions. Regular calibration is recommended.
Backtest Limitations: Backtesting results are historical and do not guarantee future performance.
Risk Management: Always apply sound risk management practices when using any trading indicator.
TrigWave Suite [InvestorUnknown]The TrigWave Suite combines Sine-weighted, Cosine-weighted, and Hyperbolic Tangent moving averages (HTMA) with a Directional Movement System (DMS) and a Relative Strength System (RSS).
Hyperbolic Tangent Moving Average (HTMA)
The HTMA smooths the price by applying a hyperbolic tangent transformation to the difference between the price and a simple moving average. It also adjusts this value by multiplying it by a standard deviation to create a more stable signal.
// Function to calculate Hyperbolic Tangent
tanh(x) =>
e_x = math.exp(x)
e_neg_x = math.exp(-x)
(e_x - e_neg_x) / (e_x + e_neg_x)
// Function to calculate Hyperbolic Tangent Moving Average
htma(src, len, mul) =>
tanh_src = tanh((src - ta.sma(src, len)) * mul) * ta.stdev(src, len) + ta.sma(src, len)
htma = ta.sma(tanh_src, len)
Sine-Weighted Moving Average (SWMA)
The SWMA applies sine-based weights to historical prices. This gives more weight to the central data points, making it responsive yet less prone to noise.
// Function to calculate the Sine-Weighted Moving Average
f_Sine_Weighted_MA(series float src, simple int length) =>
var float sine_weights = array.new_float(0)
array.clear(sine_weights) // Clear the array before recalculating weights
for i = 0 to length - 1
weight = math.sin((math.pi * (i + 1)) / length)
array.push(sine_weights, weight)
// Normalize the weights
sum_weights = array.sum(sine_weights)
for i = 0 to length - 1
norm_weight = array.get(sine_weights, i) / sum_weights
array.set(sine_weights, i, norm_weight)
// Calculate Sine-Weighted Moving Average
swma = 0.0
if bar_index >= length
for i = 0 to length - 1
swma := swma + array.get(sine_weights, i) * src
swma
Cosine-Weighted Moving Average (CWMA)
The CWMA uses cosine-based weights for data points, which produces a more stable trend-following behavior, especially in low-volatility markets.
f_Cosine_Weighted_MA(series float src, simple int length) =>
var float cosine_weights = array.new_float(0)
array.clear(cosine_weights) // Clear the array before recalculating weights
for i = 0 to length - 1
weight = math.cos((math.pi * (i + 1)) / length) + 1 // Shift by adding 1
array.push(cosine_weights, weight)
// Normalize the weights
sum_weights = array.sum(cosine_weights)
for i = 0 to length - 1
norm_weight = array.get(cosine_weights, i) / sum_weights
array.set(cosine_weights, i, norm_weight)
// Calculate Cosine-Weighted Moving Average
cwma = 0.0
if bar_index >= length
for i = 0 to length - 1
cwma := cwma + array.get(cosine_weights, i) * src
cwma
Directional Movement System (DMS)
DMS is used to identify trend direction and strength based on directional movement. It uses ADX to gauge trend strength and combines +DI and -DI for directional bias.
// Function to calculate Directional Movement System
f_DMS(simple int dmi_len, simple int adx_len) =>
up = ta.change(high)
down = -ta.change(low)
plusDM = na(up) ? na : (up > down and up > 0 ? up : 0)
minusDM = na(down) ? na : (down > up and down > 0 ? down : 0)
trur = ta.rma(ta.tr, dmi_len)
plus = fixnan(100 * ta.rma(plusDM, dmi_len) / trur)
minus = fixnan(100 * ta.rma(minusDM, dmi_len) / trur)
sum = plus + minus
adx = 100 * ta.rma(math.abs(plus - minus) / (sum == 0 ? 1 : sum), adx_len)
dms_up = plus > minus and adx > minus
dms_down = plus < minus and adx > plus
dms_neutral = not (dms_up or dms_down)
signal = dms_up ? 1 : dms_down ? -1 : 0
Relative Strength System (RSS)
RSS employs RSI and an adjustable moving average type (SMA, EMA, or HMA) to evaluate whether the market is in a bullish or bearish state.
// Function to calculate Relative Strength System
f_RSS(rsi_src, rsi_len, ma_type, ma_len) =>
rsi = ta.rsi(rsi_src, rsi_len)
ma = switch ma_type
"SMA" => ta.sma(rsi, ma_len)
"EMA" => ta.ema(rsi, ma_len)
"HMA" => ta.hma(rsi, ma_len)
signal = (rsi > ma and rsi > 50) ? 1 : (rsi < ma and rsi < 50) ? -1 : 0
ATR Adjustments
To minimize false signals, the HTMA, SWMA, and CWMA signals are adjusted with an Average True Range (ATR) filter:
// Calculate ATR adjusted components for HTMA, CWMA and SWMA
float atr = ta.atr(atr_len)
float htma_up = htma + (atr * atr_mult)
float htma_dn = htma - (atr * atr_mult)
float swma_up = swma + (atr * atr_mult)
float swma_dn = swma - (atr * atr_mult)
float cwma_up = cwma + (atr * atr_mult)
float cwma_dn = cwma - (atr * atr_mult)
This adjustment allows for better adaptation to varying market volatility, making the signal more reliable.
Signals and Trend Calculation
The indicator generates a Trend Signal by aggregating the output from each component. Each component provides a directional signal that is combined to form a unified trend reading. The trend value is then converted into a long (1), short (-1), or neutral (0) state.
Backtesting Mode and Performance Metrics
The Backtesting Mode includes a performance metrics table that compares the Buy and Hold strategy with the TrigWave Suite strategy. Key statistics like Sharpe Ratio, Sortino Ratio, and Omega Ratio are displayed to help users assess performance. Note that due to labels and plotchar use, automatic scaling may not function ideally in backtest mode.
Alerts and Visualization
Trend Direction Alerts: Set up alerts for long and short signals
Color Bars and Gradient Option: Bars are colored based on the trend direction, with an optional gradient for smoother visual feedback.
Important Notes
Customization: Default settings are experimental and not intended for trading/investing purposes. Users are encouraged to adjust and calibrate the settings to optimize results according to their trading style.
Backtest Results Disclaimer: Please note that backtest results are not indicative of future performance, and no strategy guarantees success.
Z-Score Weighted Trend System I [InvestorUnknown]The Z-Score Weighted Trend System I is an advanced and experimental trading indicator designed to utilize a combination of slow and fast indicators for a comprehensive analysis of market trends. The system is designed to identify stable trends using slower indicators while capturing rapid market shifts through dynamically weighted fast indicators. The core of this indicator is the dynamic weighting mechanism that utilizes the Z-score of price , allowing the system to respond effectively to significant market movements.
Dynamic Z-Score-Based Weighting System
The Z-Score Weighted Trend System I utilizes the Z-score of price to assign weights dynamically to fast indicators. This mechanism is designed to capture rapid market shifts at potential turning points, providing timely entry and exit signals.
Traders can choose from two primary weighting mechanisms:
Threshold-Based Weighting: The fast indicators are given weight only when the absolute Z-score exceeds a user-defined threshold. Below this threshold, fast indicators have no impact on the final signal.
Continuous Weighting: By setting the threshold to zero, fast indicators always contribute to the final signal, regardless of Z-score levels. However, this increases the likelihood of false signals during ranging or low-volatility markets
// Calculate weight for Fast Indicators based on Z-Score (Slow Indicator weight is kept to 1 for simplicity)
f_zscore_weights(series float z, simple float weight_thre) =>
float fast_weight = na
float slow_weight = na
if weight_thre > 0
if math.abs(z) <= weight_thre
fast_weight := 0
slow_weight := 1
else
fast_weight := 0 + math.sqrt(math.abs(z))
slow_weight := 1
else
fast_weight := 0 + math.sqrt(math.abs(z))
slow_weight := 1
Choice of Z-Score Normalization
Traders have the flexibility to select different Z-score processing methods to better suit their trading preferences:
Raw Z-Score or Moving Average: Traders can opt for either the raw Z-score or a moving average of the Z-score to smooth out fluctuations.
Normalized Z-Score (ranging from -1 to 1) or Z-Score Percentile: The normalized Z-score is simply the raw Z-score divided by 3, while the Z-score percentile utilizes a normal distribution for transformation.
f_zscore_perc(series float zscore_src, simple int zscore_len, simple string zscore_a, simple string zscore_b, simple string ma_type, simple int ma_len) =>
z = (zscore_src - ta.sma(zscore_src, zscore_len)) / ta.stdev(zscore_src, zscore_len)
zscore = switch zscore_a
"Z-Score" => z
"Z-Score MA" => ma_type == "EMA" ? (ta.ema(z, ma_len)) : (ta.sma(z, ma_len))
output = switch zscore_b
"Normalized Z-Score" => (zscore / 3) > 1 ? 1 : (zscore / 3) < -1 ? -1 : (zscore / 3)
"Z-Score Percentile" => (f_percentileFromZScore(zscore) - 0.5) * 2
output
Slow and Fast Indicators
The indicator uses a combination of slow and fast indicators:
Slow Indicators (constant weight) for stable trend identification: DMI (Directional Movement Index), CCI (Commodity Channel Index), Aroon
Fast Indicators (dynamic weight) to identify rapid trend shifts: ZLEMA (Zero-Lag Exponential Moving Average), IIRF (Infinite Impulse Response Filter)
Each indicator is calculated using for-loop methods to provide a smoothed and averaged view of price data over varying lengths, ensuring stability for slow indicators and responsiveness for fast indicators.
Signal Calculation
The final trading signal is determined by a weighted combination of both slow and fast indicators. The slow indicators provide a stable view of the trend, while the fast indicators offer agile responses to rapid market movements. The signal calculation takes into account the dynamic weighting of fast indicators based on the Z-score:
// Calculate Signal (as weighted average)
float sig = math.round(((DMI*slow_w) + (CCI*slow_w) + (Aroon*slow_w) + (ZLEMA*fast_w) + (IIRF*fast_w)) / (3*slow_w + 2*fast_w), 2)
Backtest Mode and Performance Metrics
The indicator features a detailed backtesting mode, allowing traders to compare the effectiveness of their selected settings against a traditional Buy & Hold strategy. The backtesting provides:
Equity calculation based on signals generated by the indicator.
Performance metrics comparing Buy & Hold metrics with the system’s signals, including: Mean, positive, and negative return percentages, Standard deviations, Sharpe, Sortino, and Omega Ratios
// Calculate Performance Metrics
f_PerformanceMetrics(series float base, int Lookback, simple float startDate, bool Annualize = true) =>
// Initialize variables for positive and negative returns
pos_sum = 0.0
neg_sum = 0.0
pos_count = 0
neg_count = 0
returns_sum = 0.0
returns_squared_sum = 0.0
pos_returns_squared_sum = 0.0
neg_returns_squared_sum = 0.0
// Loop through the past 'Lookback' bars to calculate sums and counts
if (time >= startDate)
for i = 0 to Lookback - 1
r = (base - base ) / base
returns_sum += r
returns_squared_sum += r * r
if r > 0
pos_sum += r
pos_count += 1
pos_returns_squared_sum += r * r
if r < 0
neg_sum += r
neg_count += 1
neg_returns_squared_sum += r * r
float export_array = array.new_float(12)
// Calculate means
mean_all = math.round((returns_sum / Lookback), 4)
mean_pos = math.round((pos_count != 0 ? pos_sum / pos_count : na), 4)
mean_neg = math.round((neg_count != 0 ? neg_sum / neg_count : na), 4)
// Calculate standard deviations
stddev_all = math.round((math.sqrt((returns_squared_sum - (returns_sum * returns_sum) / Lookback) / Lookback)) * 100, 2)
stddev_pos = math.round((pos_count != 0 ? math.sqrt((pos_returns_squared_sum - (pos_sum * pos_sum) / pos_count) / pos_count) : na) * 100, 2)
stddev_neg = math.round((neg_count != 0 ? math.sqrt((neg_returns_squared_sum - (neg_sum * neg_sum) / neg_count) / neg_count) : na) * 100, 2)
// Calculate probabilities
prob_pos = math.round((pos_count / Lookback) * 100, 2)
prob_neg = math.round((neg_count / Lookback) * 100, 2)
prob_neu = math.round(((Lookback - pos_count - neg_count) / Lookback) * 100, 2)
// Calculate ratios
sharpe_ratio = math.round((mean_all / stddev_all * (Annualize ? math.sqrt(Lookback) : 1))* 100, 2)
sortino_ratio = math.round((mean_all / stddev_neg * (Annualize ? math.sqrt(Lookback) : 1))* 100, 2)
omega_ratio = math.round(pos_sum / math.abs(neg_sum), 2)
// Set values in the array
array.set(export_array, 0, mean_all), array.set(export_array, 1, mean_pos), array.set(export_array, 2, mean_neg),
array.set(export_array, 3, stddev_all), array.set(export_array, 4, stddev_pos), array.set(export_array, 5, stddev_neg),
array.set(export_array, 6, prob_pos), array.set(export_array, 7, prob_neu), array.set(export_array, 8, prob_neg),
array.set(export_array, 9, sharpe_ratio), array.set(export_array, 10, sortino_ratio), array.set(export_array, 11, omega_ratio)
// Export the array
export_array
//}
Calibration Mode
A Calibration Mode is included for traders to focus on individual indicators, helping them fine-tune their settings without the influence of other components. In Calibration Mode, the user can visualize each indicator separately, making it easier to adjust parameters.
Alerts
The indicator includes alerts for long and short signals when the indicator changes direction, allowing traders to set automated notifications for key market events.
// Alert Conditions
alertcondition(long_alert, "LONG (Z-Score Weighted Trend System)", "Z-Score Weighted Trend System flipped ⬆LONG⬆")
alertcondition(short_alert, "SHORT (Z-Score Weighted Trend System)", "Z-Score Weighted Trend System flipped ⬇Short⬇")
Important Note:
The default settings of this indicator are not optimized for any particular market condition. They are generic starting points for experimentation. Traders are encouraged to use the calibration tools and backtesting features to adjust the system to their specific trading needs.
The results generated from the backtest are purely historical and are not indicative of future results. Market conditions can change, and the performance of this system may differ under different circumstances. Traders and investors should exercise caution and conduct their own research before using this indicator for any trading decisions.
Normalised T3 Oscillator [BackQuant]Normalised T3 Oscillator
The Normalised T3 Oscillator is an technical indicator designed to provide traders with a refined measure of market momentum by normalizing the T3 Moving Average. This tool was developed to enhance trading decisions by smoothing price data and reducing market noise, allowing for clearer trend recognition and potential signal generation. Below is a detailed breakdown of the Normalised T3 Oscillator, its methodology, and its application in trading scenarios.
1. Conceptual Foundation and Definition of T3
The T3 Moving Average, originally proposed by Tim Tillson, is renowned for its smoothness and responsiveness, achieved through a combination of multiple Exponential Moving Averages and a volume factor. The Normalised T3 Oscillator extends this concept by normalizing these values to oscillate around a central zero line, which aids in highlighting overbought and oversold conditions.
2. Normalization Process
Normalization in this context refers to the adjustment of the T3 values to ensure that the oscillator provides a standard range of output. This is accomplished by calculating the lowest and highest values of the T3 over a user-defined period and scaling the output between -0.5 to +0.5. This process not only aids in standardizing the indicator across different securities and time frames but also enhances comparative analysis.
3. Integration of the Oscillator and Moving Average
A unique feature of the Normalised T3 Oscillator is the inclusion of a secondary smoothing mechanism via a moving average of the oscillator itself, selectable from various types such as SMA, EMA, and more. This moving average acts as a signal line, providing potential buy or sell triggers when the oscillator crosses this line, thus offering dual layers of analysis—momentum and trend confirmation.
4. Visualization and User Interaction
The indicator is designed with user interaction in mind, featuring customizable parameters such as the length of the T3, normalization period, and type of moving average used for signals. Additionally, the oscillator is plotted with a color-coded scheme that visually represents different strength levels of the market conditions, enhancing readability and quick decision-making.
5. Practical Applications and Strategy Integration
Traders can leverage the Normalised T3 Oscillator in various trading strategies, including trend following, counter-trend plays, and as a component of a broader trading system. It is particularly useful in identifying turning points in the market or confirming ongoing trends. The clear visualization and customizable nature of the oscillator facilitate its adaptation to different trading styles and market environments.
6. Advanced Features and Customization
Further enhancing its utility, the indicator includes options such as painting candles according to the trend, showing static levels for quick reference, and alerts for crossover and crossunder events, which can be integrated into automated trading systems. These features allow for a high degree of personalization, enabling traders to mold the tool according to their specific trading preferences and risk management requirements.
7. Theoretical Justification and Empirical Usage
The use of the T3 smoothing mechanism combined with normalization is theoretically sound, aiming to reduce lag and false signals often associated with traditional moving averages. The practical effectiveness of the Normalised T3 Oscillator should be validated through rigorous backtesting and adjustment of parameters to match historical market conditions and volatility.
8. Conclusion and Utility in Market Analysis
Overall, the Normalised T3 Oscillator by BackQuant stands as a sophisticated tool for market analysis, providing traders with a dynamic and adaptable approach to gauging market momentum. Its development is rooted in the understanding of technical nuances and the demand for a more stable, responsive, and customizable trading indicator.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Ichimoku Clouds Strategy Long and ShortOverview:
The Ichimoku Clouds Strategy leverages the Ichimoku Kinko Hyo technique to offer traders a range of innovative features, enhancing market analysis and trading efficiency. This strategy is distinct in its combination of standard methodology and advanced customization, making it suitable for both novice and experienced traders.
Unique Features:
Enhanced Interpretation: The strategy introduces weak, neutral, and strong bullish/bearish signals, enabling detailed interpretation of the Ichimoku cloud and direct chart plotting.
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Dual Trading Modes: Long and Short modes are available, allowing alignment with market trends.
Flexible Risk Management: Offers three styles in each mode, combining fixed risk management with dynamic indicator states for versatile trade management.
Indicator Line Plotting: Enables plotting of Ichimoku indicator lines on the chart for visual decision-making support.
Methodology:
The strategy utilizes the standard Ichimoku Kinko Hyo model, interpreting indicator values with settings adjustable through a user-friendly menu. This approach is enhanced by TradingView's built-in strategy tester for customization and market selection.
Risk Management:
Our approach to risk management is dynamic and indicator-centric. With data from the last year, we focus on dynamic indicator states interpretations to mitigate manual setting causing human factor biases. Users still have the option to set a fixed stop loss and/or take profit per position using the corresponding parameters in settings, aligning with their risk tolerance.
Backtest Results:
Operating window: Date range of backtests is 2023.01.01 - 2024.01.04. 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.
Maximum Single Position Loss: -6.29%
Maximum Single Profit: 22.32%
Net Profit: +10 901.95 USDT (+109.02%)
Total Trades: 119 (51.26% profitability)
Profit Factor: 1.775
Maximum Accumulated Loss: 4 185.37 USDT (-22.87%)
Average Profit per Trade: 91.67 USDT (+0.7%)
Average Trade Duration: 56 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. Backtest is calculated using deep backtest option in TradingView built-in strategy tester
How to Use:
Add the script to favorites for easy access.
Apply to the desired chart and timeframe (optimal performance observed on the 1H chart, ForEx or cryptocurrency top-10 coins with quote asset 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_RSI_trend_followingINFO:
This indicator can be used to build-up a strategy for trading of assets which are currently in trending phase.
My preference is to use it on slowly moving assets like GOLD and on higher timeframes, but practice may show that we find more usefull cases.
This script uses two indicators - MACD and RSI, as the timeframe that those are extracted for is configurable (defaults with the Chart TF, but can be any other selected by the user).
The strategy has the following simple idea - buy if any if the conditions below is true:
The selected TF MACD line crosses above the signal line and the TF RSI is above the user selected trigger value
The selected TF MACD line is above the signal line and the TF RSI crosses above the user selected trigger value
Once we're in position we wait for the selected TF MACD line to cross below the signal line, and then we set a SL at the low of that bar
DETAILS and USAGE:
In the current implementation I find two possible use cases for the indicator:
as a stand-alone indicator on the chart which can also fire alerts that can help to determine if we want to manually enter/exit trades based on them
can be used to connect to the Signal input of the TTS (TempalteTradingStrategy) by jason5480 in order to backtest it, thus effectively turning it into a strategy (instructions below in TTS CONNECTIVITY section)
In the example below we see a position opened at the bar after the buy indicator from the script has been triggered, and then later after the SL indicator from the script has been triggered a SL has been set on the lower wick of the closing candle, and the position eventually got closed once the price hit that level. Note that most of the drawing on the example snapshot below are from the TTS indicator following the buy/sell/SL conditions themseves:
Trading period can be selected from the indicator itself to limit to more interesting periods.
Arrow indications are drawn on the chart to indicate the trading conditions met in the script - green arrow for a buy signal indication and orange for LTF crossunder to indicate setting of SL.
SETTINGS:
Leaving all of the settings as in vanilla use case, as both the MACD and RSI indicator's settings follow the default ones for the stand-alone indicators themselves.
The start-end date is a time filter that can be extermely usefull when backtesting different time periods.
Pesonal preference is using the script on a D/W timeframe, while the indicator is configured to use Monthly chart.
The default value of the RSI filter is left to 50, which can be changed. I.e. if the RSI is above 50 we have a regime filter based on the MACD criteria.
EXTERNAL LIBRARIES:
The script uses a couple of external libraries:
HeWhoMustNotBeNamed/enhanced_ta/14 - collection of TA indicators
jason5480/tts_convention/3 - more details about the Template Trading Strategy below
I would like to highly appreciate and credit the work of both HeWhoMustNotBeNamed and jason5480 for providing them to the community.
TTS SETTINGS (NEEDED IF USED TO BACKTEST WITH TTS):
The TempalteTradingStrategy is a strategy script developed in Pine by jason5480, which I recommend for quick turn-around of testing different ideas on a proven and tested framework
I cannot give enough credit to the developer for the efforts put in building of the infrastructure, so I advice everyone that wants to use it first to get familiar with the concept and by checking
by checking jason5480's profile www.tradingview.com
The TTS itself is extremely functional and have a lot of properties, so its functionality is beyond the scope of the current script -
Again, I strongly recommend to be thoroughly epxlored by everyone that plans on using it.
In the nutshell it is a script that can be feed with buy/sell signals from an external indicator script and based on many configuration options it can determine how to execute the trades.
The TTS has many settings that can be applied, so below I will cover only the ones that differ from the default ones, at least according to my testing - do your own research, you may find something even better :)
The current/latest version that I've been using as of writing and testing this script is TTSv48
Settings which differ from the default ones:
from - False (time filter is from the indicator script itself)
Deal Conditions Mode - External (take enter/exit conditions from an external script)
🔌Signal 🛈➡ - MACD_RSI_trend_following: 🔌Signal to TTSv48 (this is the output from the indicator script, according to the TTS convention)
Sat/Sun - true (for crypto, in order to trade 24/7)
Order Type - STOP (perform stop order)
Distance Method - HHLL (HigherHighLowerLow - in order to set the SL according to the strategy definition from above)
The next are just personal preferenes, you can feel free to experiment according to your trading style
Take Profit Targets - 0 (either 100% in or out, no incremental stepping in or out of positions)
Dist Mul|Len Long/Short- 10 (make sure that we don't close on profitable trades by any reason)
Quantity Method - EQUITY (personal backtesting preference is to consider each backtest as a separate portfolio, so determine the position size by 100% of the allocated equity size)
Equity % - 100 (note above)
