This repository contains a collection of algorithmic trading strategies implemented in Python, designed for use with the QTradeX platform.
To install QTradex, see the Installation section at the link above.
Read the core docs at QtradeX SDK DeepWiki.
Read the bot docs at QtradeX AI Agents DeepWiki.
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Each strategy leverages technical indicators to generate buy and sell signals for trading assets. Below is a brief description of each strategy.
Please note that some of these strategies are a work in progress. Known good strategies are:
cthulu.pyextinction_event.pyiching.pyforty96.pyharmonica.pyma_sabres.pyparabolic_ten.py
Others may not work or backtest profitably.
- Indicators: Uses the Aroon Oscillator, a momentum indicator that measures trend strength based on high and low prices over a specified period (
aroon_period). It ranges from -100 to +100. - Strategy: Triggers buy signals when the Aroon Oscillator crosses above a
buy_thresh(indicating an uptrend) and sell signals when it crosses below asell_thresh(indicating a downtrend).
- Indicators: Combines Short EMA, Aroon Indicator, Money Flow Index (MFI), and Volume Weighted Average Price (VWAP).
- Strategy: Generates a buy signal when the difference between
aroon_upandaroon_downexceeds a threshold (aroon_buy) and theshort_emais below thevwap(suggesting an oversold condition).
- Indicators: Includes ATR (Average True Range), SMA (Simple Moving Average), volatility surge detection, dynamic support/resistance levels, momentum signals, and a unique "blackhole zone" for price compression.
- Strategy: Detects extreme market conditions (e.g., volatility surges) and uses dynamic support/resistance levels to set buy zones for entering positions.
- Indicators: Features SMA, EMA, RSI, Stochastic Oscillator, and ADX (Average Directional Index) with customizable periods.
- Strategy: Combines multiple indicators with thresholds (
buy_threshold,sell_threshold) to confirm buy/sell signals based on trend and momentum.
- Indicators: Uses Short-term EMA, Long-term EMA, RSI, MACD, and Bollinger Bands.
- Strategy: Seeks confluence across multiple indicators to identify high-probability trades, leveraging trend and momentum signals.
- Indicators: Combines SMA, EMA, RSI, MACD, Bollinger Bands, Fisher Transform, and Stochastic Oscillator.
- Strategy: A multi-indicator approach that uses MACD crossovers, RSI overbought/oversold levels, and Stochastic signals for trade execution.
- Indicators: Features a 14-period EMA, Standard Deviation, and dynamic upper/lower channels around the EMA.
- Strategy: Tracks trends with EMA and uses volatility-based channels to identify breakout or reversal points.
- Indicators: Includes Short-, Mid-, and Long-term EMAs, DMI (Directional Movement Indicators), ADX, and ADXR.
- Strategy: Uses ADX to measure trend strength and DMI to determine direction, supplemented by EMA crossovers.
- Indicators: Relies on two EMAs (short-term and long-term) calculated with closing prices.
- Strategy: Triggers trades based on crossovers between the fast and slow EMAs, signaling trend changes.
- Indicators: Features multiple EMAs, dynamic support, resistance, selloff, and despair levels, plus trend detection.
- Strategy: Adjusts buy/sell prices based on market trends (‘bull’, ‘bear’, or neutral) and overrides default behavior during trend shifts.
- Indicators: Calculates EMA values and slopes to form a "hexagram" (12-dimensional dictionary) representing market conditions.
- Strategy: Uses the hexagram's binary string to determine buy, sell, or no-action decisions.
- Indicators: Combines Ultimate Oscillator (UO), Forecast Oscillator (FOSC), and Mesa Sine Wave (MSW).
- Strategy: Requires a threshold number of aligned signals (
buy_threshold,sell_threshold) for trade execution.
- Indicators: Uses six Parabolic SAR values with varying sensitivity and four EMAs (10, 60, 90 periods).
- Strategy: Detects trend reversals with SAR and confirms direction with EMAs.
- Indicators: Calculates EMA slopes to form a 6-dimensional "hexagram" (binary array).
- Strategy: Converts the hexagram into a binary string to lookup buy/sell actions in a tuning dictionary.
- Indicators: Features two EMAs (fast and slow) and an OHLC4 (average of open, high, low, close prices).
- Strategy: Determines market mode (bullish, bearish, or neutral) by comparing start and close prices, guided by EMA trends.
- Indicators: Uses five configurable Moving Averages (EMA, SMA, HMA) and their slopes for trend detection.
- Strategy: Generates dynamic buy/sell signals based on slope alignment and bullish/bearish thresholds.
- Indicators: Combines MACD, RSI, ADX, and Fourier Transform (FFT) with a low-pass filter.
- Strategy: Uses MACD crossovers, RSI levels, and ADX trend strength for buy/sell decisions, filtering noise with FFT.
- Indicators: Features MACD, RSI, Stochastic Oscillator, and ATR.
- Strategy: Confirms entries with RSI and Stochastic, using MACD for trend direction and ATR for volatility.
- Indicators: Identical to
harmonica.pywith six Parabolic SAR values and four EMAs. - Strategy: Tracks trends with SAR and confirms with EMA directionality.
- Indicators: Uses Renko Bars (fixed price movement) and RSI.
- Strategy: Triggers buy signals when Renko shows an uptrend and RSI is oversold; sell signals when Renko shows a downtrend and RSI is overbought.
- Indicators: Includes SMA, EMA, RSI, MACD, Bollinger Bands, and Stochastic Oscillator.
- Strategy: A traditional finance-inspired approach combining trend, momentum, and volatility indicators.
- Indicators: Uses ZLEMA (Zero-Lag EMA), TRIMA (Triangular MA), and Fisher Transform.
- Strategy: Requires a threshold number of conditions (
buy_threshold,sell_threshold) based on momentum and trend signals.
This repository also contains a tunes directory created by QTradeX's tune manager, each labeled with the strategy name and number of parameters. These tunes do not have to be interacted with manually and are automatically indexed by the tune manager.