# source: https://raw.githubusercontent.com/valmy/user_data/17693d20837366ace625d47defe7d4165caeac43/strategies/DowTheoryStrategy.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # isort: skip_file # --- Do not remove these imports --- import numpy as np import pandas as pd from datetime import datetime, timedelta, timezone from pandas import DataFrame from typing import Dict, Optional, Union, Tuple from freqtrade.strategy import ( IStrategy, Trade, Order, PairLocks, informative, BooleanParameter, CategoricalParameter, DecimalParameter, IntParameter, RealParameter, timeframe_to_minutes, timeframe_to_next_date, timeframe_to_prev_date, merge_informative_pair, stoploss_from_absolute, stoploss_from_open, ) # -------------------------------- # Add your lib to import here import talib.abstract as ta import pandas_ta as pta from technical import qtpylib class Github_valmy_user_data__DowTheoryStrategy__20250603_031653(IStrategy): """ Strategy based on Dow Theory principles for trend detection. Key Dow Theory principles implemented: 1. Primary trends identification using higher timeframe 2. Secondary trend corrections using mid timeframe 3. Confirmation through volume 4. Trend continuation until valid reversal signals The strategy uses multiple timeframes to align with Dow Theory's concept of primary, secondary, and minor trends. """ INTERFACE_VERSION = 3 # Optimal timeframe for the strategy - using 15m as primary trend timeframe = "5m" # Can this strategy go short? can_short: bool = True # Minimal ROI designed for the strategy minimal_roi = { "0": 0.05, # 5% profit is enough to exit "240": 0.03, # After 240 minutes, exit at 3% profit "1440": 0.01, # After 24 hours, exit at 1% profit } # Optimal stoploss designed for the strategy stoploss = -0.1 # Trailing stoploss to lock in profits as trend continues trailing_stop = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.02 trailing_only_offset_is_reached = True # Run "populate_indicators()" only for new candle process_only_new_candles = True # These values can be overridden in the config use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 50 # Parameters for tuning trend_strength = IntParameter(20, 50, default=25, space="buy") volume_threshold = DecimalParameter(1.0, 3.0, default=1.5, space="buy") reversal_threshold = IntParameter(10, 30, default=15, space="sell") def informative_pairs(self): """ Define additional, informative pair/interval combinations to be cached from the exchange. We need higher timeframes for primary trend detection and secondary trend confirmations. """ pairs = self.dp.current_whitelist() informative_pairs = [] # 15m timeframe for primary trend detection for pair in pairs: informative_pairs.append((pair, "15m")) # 1h timeframe for major trend confirmation for pair in pairs: informative_pairs.append((pair, "1h")) return informative_pairs @informative('15m') def populate_informative_15m(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Populate indicators for primary trend identification on 15m timeframe """ # Calculate primary trend indicators dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema200'] = ta.EMA(dataframe, timeperiod=200) # Primary trend determination dataframe['uptrend_15m'] = (dataframe['ema50'] > dataframe['ema200']) & (dataframe['close'] > dataframe['ema50']) dataframe['downtrend_15m'] = (dataframe['ema50'] < dataframe['ema200']) & (dataframe['close'] < dataframe['ema50']) # Relative strength dataframe['rsi_15m'] = ta.RSI(dataframe, timeperiod=14) return dataframe @informative('1h') def populate_informative_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Populate indicators for major trend confirmation on 1h timeframe """ # Calculate major trend indicators dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50) dataframe['sma200'] = ta.SMA(dataframe, timeperiod=200) # Major trend determination (longer term) dataframe['major_uptrend'] = dataframe['sma50'] > dataframe['sma200'] dataframe['major_downtrend'] = dataframe['sma50'] < dataframe['sma200'] return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Adds indicators for secondary trends and generates buy/sell signals """ # Secondary trend indicators dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20) dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) # Momentum and volume indicators dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['atr'] = ta.ATR(dataframe, timeperiod=14) # Volume confirmation dataframe['volume_mean'] = dataframe['volume'].rolling(10).mean() dataframe['volume_increased'] = dataframe['volume'] > (dataframe['volume_mean'] * self.volume_threshold.value) # Detect higher highs and higher lows for uptrend dataframe['higher_high'] = ( (dataframe['high'] > dataframe['high'].shift(1)) & (dataframe['high'].shift(1) > dataframe['high'].shift(2)) ) dataframe['higher_low'] = ( (dataframe['low'] > dataframe['low'].shift(1)) & (dataframe['low'].shift(1) > dataframe['low'].shift(2)) ) # Detect lower highs and lower lows for downtrend dataframe['lower_high'] = ( (dataframe['high'] < dataframe['high'].shift(1)) & (dataframe['high'].shift(1) < dataframe['high'].shift(2)) ) dataframe['lower_low'] = ( (dataframe['low'] < dataframe['low'].shift(1)) & (dataframe['low'].shift(1) < dataframe['low'].shift(2)) ) # Identify peak and trough dataframe['peak'] = ta.MAX(dataframe['high'], timeperiod=10) # Local peak dataframe['trough'] = ta.MIN(dataframe['low'], timeperiod=10) # Local trough # Donchian Channels peak and trough detection (using 10-period window) donchian_upper = dataframe['high'].rolling(window=10).max() donchian_lower = dataframe['low'].rolling(window=10).min() dataframe['donchian_peak'] = dataframe['high'] >= donchian_upper dataframe['donchian_trough'] = dataframe['low'] <= donchian_lower return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on Dow Theory principles, identify entry signals """ # Default values dataframe['enter_long'] = 0 dataframe['enter_short'] = 0 # LONG entries - Primary uptrend confirmed by secondary trend bounce dataframe.loc[ ( # Primary trend is up (from 15m timeframe) dataframe['uptrend_15m_15m'] & # Major trend is up (from 1h timeframe) dataframe['major_uptrend_1h'] & # Secondary trend indicators (dataframe['ema20'] > dataframe['ema50']) & # Price bounced off support (secondary correction ended) (dataframe['low'].shift(1) < dataframe['ema50']) & (dataframe['close'] > dataframe['ema50']) & # Volume confirms the movement dataframe['volume_increased'] & # Pattern of higher lows (accumulation) dataframe['higher_low'] & # RSI showing strength but not overbought (dataframe['rsi'] > self.trend_strength.value) & (dataframe['rsi'] < 70) & (dataframe['volume'] > 0) & # Ensure volume is not 0 # Higher peak and higher trough condition (dataframe['peak'] > dataframe['peak'].shift(11)) & (dataframe['trough'] > dataframe['trough'].shift(11)) ), 'enter_long'] = 1 # SHORT entries - Primary downtrend confirmed by secondary trend bounce if self.can_short: dataframe.loc[ ( # Primary trend is down (from 15m timeframe) dataframe['downtrend_15m_15m'] & # Major trend is down (from 1h timeframe) dataframe['major_downtrend_1h'] & # Secondary trend indicators (dataframe['ema20'] < dataframe['ema50']) & # Price bounced off resistance (secondary correction ended) (dataframe['high'].shift(1) > dataframe['ema50']) & (dataframe['close'] < dataframe['ema50']) & # Volume confirms the movement dataframe['volume_increased'] & # Pattern of lower highs (distribution) dataframe['lower_high'] & # RSI showing weakness but not oversold (dataframe['rsi'] < (100 - self.trend_strength.value)) & (dataframe['rsi'] > 30) & (dataframe['volume'] > 0) # Ensure volume is not 0 # Add conditions based on peak and trough & (dataframe['peak'] < dataframe['peak'].shift(11)) & (dataframe['trough'] < dataframe['trough'].shift(11)) ), 'enter_short'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on Dow Theory principles, identify exit signals when trends reverse """ # Default values dataframe['exit_long'] = 0 dataframe['exit_short'] = 0 # Exit LONG positions dataframe.loc[ ( # Reversal signals in primary trend (dataframe['downtrend_15m_15m']) | # RSI overbought (dataframe['rsi'] > 80) | # Trend reversal on high volume ( dataframe['lower_high'] & dataframe['lower_low'] & dataframe['volume_increased'] ) | # Break below key support (dataframe['close'] < dataframe['trough'].shift(1)) | # RSI divergence (falling RSI while price rising) ( (dataframe['close'] > dataframe['close'].shift(3)) & (dataframe['rsi'] < dataframe['rsi'].shift(3)) & (dataframe['rsi'] < self.reversal_threshold.value) ) ), 'exit_long'] = 1 # Exit SHORT positions if self.can_short: dataframe.loc[ ( # Reversal signals in primary trend (dataframe['uptrend_15m_15m']) | # RSI oversold (dataframe['rsi'] < 20) | # Trend reversal on high volume ( dataframe['higher_high'] & dataframe['higher_low'] & dataframe['volume_increased'] ) | # Break above key resistance (dataframe['close'] > dataframe['peak'].shift(1)) | # RSI divergence (rising RSI while price falling) ( (dataframe['close'] < dataframe['close'].shift(3)) & (dataframe['rsi'] > dataframe['rsi'].shift(3)) & (dataframe['rsi'] > (100 - self.reversal_threshold.value)) ) ), 'exit_short'] = 1 return dataframe def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, side: str, **kwargs) -> float: """ Limit leverage to reasonable values when using margin/futures """ # Conservative leverage for Dow Theory - trend following is already strong return min(2.0, max_leverage)