# source: https://raw.githubusercontent.com/anlehong/Strategy_trading/5d4de9a8ca03350121388de39351a44f2523ec16/Strategy_trading.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # isort: skip_file # --- Do not remove these libs --- import numpy as np import pandas as pd from pandas import DataFrame from datetime import datetime from typing import Optional, Union from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, IntParameter, IStrategy, merge_informative_pair) # -------------------------------- # Add your lib to import here import talib.abstract as ta import pandas_ta as pta class Github_anlehong_Strategy_trading__Strategy_trading__20240702_094605(IStrategy): """ This is a strategy template to get you started. More information in https://www.freqtrade.io/en/latest/strategy-customization/ You can: :return: a Dataframe with all mandatory indicators for the strategies - Rename the class name (Do not forget to update class_name) - Add any methods you want to build your strategy - Add any lib you need to build your strategy You must keep: - the lib in the section "Do not remove these libs" - the methods: populate_indicators, populate_entry_trend, populate_exit_trend You should keep: - timeframe, minimal_roi, stoploss, trailing_* """ # Strategy interface version - allow new iterations of the strategy interface. # Check the documentation or the Sample strategy to get the latest version. INTERFACE_VERSION = 3 # Optimal timeframe for the strategy. timeframe = '5m' # Can this strategy go short? can_short: bool = False # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi". minimal_roi = { "0": 0.114, "816": 0.049, "1756": 0.015, "2656": 0 } stoploss = -0.111 # Trailing stoploss trailing_stop = True # trailing_only_offset_is_reached = False # trailing_stop_positive = 0.01 # trailing_stop_positive_offset = 0.0 # Disabled / not configured # 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 = 400 # Optional order type mapping. order_types = { 'entry': 'limit', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } # Optional order time in force. order_time_in_force = { 'entry': 'GTC', 'exit': 'GTC' } def informative_pairs(self): """ Define additional, informative pair/interval combinations to be cached from the exchange. These pair/interval combinations are non-tradeable, unless they are part of the whitelist as well. For more information, please consult the documentation :return: List of tuples in the format (pair, interval) Sample: return [("ETH/USDT", "5m"), ("BTC/USDT", "15m"), ] """ return [ ("BTC/USDT:USDT", "1h"), ] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Adds several different TA indicators to the given DataFrame Performance Note: For the best performance be frugal on the number of indicators you are using. Let uncomment only the indicator you are using in your strategies or your hyperopt configuration, otherwise you will waste your memory and CPU usage. :param dataframe: Dataframe with data from the exchange :param metadata: Additional information, like the currently traded pair :return: a Dataframe with all mandatory indicators for the strategies """ if not self.dp: # Don't do anything if DataProvider is not available. return dataframe #### TREND #### inf_tf = '1h' informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf) # Get the 200 hour EMA informative['ma200'] = ta.MA(informative, timeperiod=200) # Support and Resistance for 1h supports_1h, resistances_1h = self.supports_and_resistances(informative,50, field_for_support='low', field_for_resistance='high') informative['support'] = None informative.loc[supports_1h.index, "support"] = supports_1h.values informative['resistance'] = None informative.loc[resistances_1h.index, "resistance"] = resistances_1h.values informative['date'] = pd.to_datetime(informative['date']) # Xét từng nến để tìm các Resistance/Support gần nhất informative['nearest_support_index'] = -1 informative['nearest_support'] = -1 informative['nearest_resistance_index'] = -1 informative['nearest_resistance'] = -1 # Duyệt qua từng hàng trong DataFrame for i in range(len(informative)): # Lấy giá đóng cửa của nến hiện tại close_price = informative.loc[i, 'close'] nearest_support_1h = supports_1h[supports_1h.index < i] nearest_resistance_1h = resistances_1h[resistances_1h.index < i] # Tìm mức hỗ trợ gần nhất (nhỏ hơn giá đóng cửa của nến) nearest_support_1h_index = nearest_support_1h[nearest_support_1h.values < close_price].index.max() if not pd.isna(nearest_support_1h_index): informative.loc[i, 'nearest_support_index'] = nearest_support_1h_index informative.loc[i, 'nearest_support'] = nearest_support_1h[nearest_support_1h_index] # Tìm mức kháng cự gần nhất (lớn hơn giá đóng cửa của nến) nearest_resistance_1h_index = nearest_resistance_1h[nearest_resistance_1h.values > close_price].index.max() if not pd.isna(nearest_resistance_1h_index): informative.loc[i, 'nearest_resistance_index'] = nearest_resistance_1h_index informative.loc[i, 'nearest_resistance'] = nearest_resistance_1h[nearest_resistance_1h_index] dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True) ### AREA OF VALUE & ENTRY TRIGER ### # Price channel dataframe = self.trendlines(dataframe=dataframe, slicing_window=60, distance=20, chart=True, field_for_supports='low', field_for_resistances='high', timeframe='5m') # Shift values of maxslope, minslope, max_y_intercept, min_y_intercept of previous hour to prior hour dataframe['previous_maxslope_5m'] = dataframe['maxslope_5m'].shift(1) dataframe['previous_minslope_5m'] = dataframe['minslope_5m'].shift(1) dataframe['previous_max_y_intercept_5m'] = dataframe['max_y_intercept_5m'].shift(1) dataframe['previous_min_y_intercept_5m'] = dataframe['min_y_intercept_5m'].shift(1) # Peak and Bottom for 5m bottoms_5m, peaks_5m = self.supports_and_resistances(dataframe, 50, field_for_support='low', field_for_resistance='high') dataframe['bottom_5m'] = None dataframe.loc[bottoms_5m.index, "bottom_5m"] = bottoms_5m.values dataframe['peak_5m'] = None dataframe.loc[peaks_5m.index, "peak_5m"] = peaks_5m.values dataframe['date'] = pd.to_datetime(dataframe['date']) # Xét từng nến để tìm các đỉnh/đáy gần nhất dataframe['nearest_peak_index_5m'] = -1 dataframe['nearest_peak_5m'] = -1 dataframe['nearest_bottom_index_5m'] = -1 dataframe['nearest_bottom_5m'] = -1 # Duyệt qua từng hàng trong DataFrame for i in range(len(dataframe)): # Lấy giá đóng cửa của nến hiện tại close_price = dataframe.loc[i, 'close'] # Tìm chỉ số (indice) của mức đáy và mức đỉnh gần nhất nearest_bottom_5m = bottoms_5m[bottoms_5m.index < i] if len(nearest_bottom_5m) != 0: dataframe.loc[i, 'nearest_bottom_index_5m'] = nearest_bottom_5m.index.max() dataframe.loc[i, 'nearest_bottom_5m'] = nearest_bottom_5m[nearest_bottom_5m.index.max()] nearest_peak_5m = peaks_5m[peaks_5m.index < i] if len(nearest_peak_5m) != 0: dataframe.loc[i, 'nearest_peak_index_5m'] = nearest_peak_5m.index.max() dataframe.loc[i, 'nearest_peak_5m'] = nearest_peak_5m[nearest_peak_5m.index.max()] # Xét từng nến để tìm các đỉnh/đáy, vùng kháng cự/ hỗ trợ gần nhất dataframe['previous_nearest_support_1h'] = dataframe['nearest_support_1h'] dataframe['previous_nearest_resistance_1h'] = dataframe['nearest_resistance_1h'] dataframe['previous_nearest_peak_5m'] = dataframe['nearest_peak_5m'].shift(1) dataframe['previous_nearest_bottom_5m'] = dataframe['nearest_bottom_5m'].shift(1) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the entry signal for the given dataframe :param dataframe: DataFrame :param metadata: Additional information, like the currently traded pair :return: DataFrame with entry columns populated """ dataframe.loc[ ( ( dataframe['close'] > dataframe['previous_maxslope_5m'] * dataframe['close'].index + dataframe['previous_max_y_intercept_5m'] ) & ( dataframe['close_1h'] > dataframe['ma200_1h'] ) ), 'enter_long', ] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( ( dataframe['previous_nearest_resistance_1h'] != -1 ) & ( ( dataframe['close'] >= dataframe['previous_nearest_resistance_1h'] - 100 ) & ( dataframe['close'] <= dataframe['previous_nearest_resistance_1h'] + 100 ) ) ), 'exit_long'] = 1 return dataframe """ Put other function for our own strategy in here """ def trendlines(self, dataframe, slicing_window=100, distance=50, chart=True, field_for_supports='low', field_for_resistances='high', timeframe="1h"): """ Return a Pandas dataframe with support and resistance lines. :param dataframe: incoming data matrix :param slicing_window: number of candles for slicing window :param distance: Number of candles between two maximum points and two minimum points :param chart: Boolean value saying whether to print chart on web :param field_for_supports: for which column would you like to generate the support lines :param field_for_resistances: for which column would you like to generate the resistance lines :param timeframe: tmieframe use to find trendline """ # Step 1: Using rolling window to find 2 peaks and 2 bottoms in each 100 candles df_high = dataframe[field_for_resistances].copy() df_low = dataframe[field_for_supports].copy() dataframe['peak1_idx'] = df_high.rolling(window=slicing_window).apply(lambda x: x.idxmax()) dataframe['bottom1_idx'] = df_low.rolling(window=slicing_window).apply(lambda x: x.idxmin()) dataframe['peak2_idx'] = df_high.rolling(window=slicing_window).apply(self.find_second_peak, args=(distance, )) dataframe['bottom2_idx'] = df_low.rolling(window=slicing_window).apply(self.find_second_bottom, args=(distance, )) # Step 2: Find maxline through 2 peaks and minline through 2 bottoms in each 100 candles dataframe['maxslope_' + timeframe] = None dataframe.loc[slicing_window - 1:, 'maxslope_' + timeframe] = (np.array(dataframe[field_for_resistances].iloc[dataframe['peak1_idx'][slicing_window - 1:].astype(int)]) - np.array(dataframe[field_for_resistances].iloc[dataframe['peak2_idx'][slicing_window - 1:].astype(int)])) / (np.array(dataframe['peak1_idx'][slicing_window - 1:]) - np.array(dataframe['peak2_idx'][slicing_window - 1:])) # Slope between max points dataframe['minslope_' + timeframe] = None dataframe.loc[slicing_window - 1:, 'minslope_' + timeframe] = (np.array(dataframe[field_for_supports].iloc[dataframe['bottom1_idx'][slicing_window - 1:].astype(int)]) - np.array(dataframe[field_for_supports].iloc[dataframe['bottom2_idx'][slicing_window - 1:].astype(int)])) / (np.array(dataframe['bottom1_idx'][slicing_window - 1:]) - np.array(dataframe['bottom2_idx'][slicing_window - 1:])) # Slope between max points dataframe['max_y_intercept_' + timeframe] = None dataframe.loc[slicing_window - 1:, 'max_y_intercept_' + timeframe] = (np.array(dataframe[field_for_resistances].iloc[dataframe['peak1_idx'][slicing_window - 1:].astype(int)]) - np.array(dataframe['maxslope_' + timeframe][slicing_window - 1:]) * np.array(dataframe['peak1_idx'][slicing_window - 1:])) # y-intercept for max trendline dataframe['min_y_intercept_' + timeframe] = None dataframe.loc[slicing_window - 1:, 'min_y_intercept_' + timeframe] = (np.array(dataframe[field_for_supports].iloc[dataframe['bottom1_idx'][slicing_window - 1:].astype(int)]) - np.array(dataframe['minslope_' + timeframe][slicing_window - 1:]) * np.array(dataframe['bottom1_idx'][slicing_window - 1:])) # y-intercept for min trendline return dataframe def supports_and_resistances(self, dataframe, rollsize, field_for_support='low', field_for_resistance='high'): diffs1 = abs(dataframe['high'].diff().abs().iloc[1:]) diffs2 = abs(dataframe['low'].diff().abs().iloc[1:]) mean_deviation_ressistance = diffs1.mean() mean_deviation_support = diffs2.mean() supports = dataframe[dataframe.low == dataframe[field_for_support].rolling(rollsize, center=True).min()].low resistances = dataframe[dataframe.high == dataframe[field_for_resistance].rolling(rollsize, center=True).max()].high supports = supports[abs(supports.diff()) > mean_deviation_support] resistances = resistances[abs(resistances.diff()) > mean_deviation_ressistance] return supports,resistances def find_second_peak(self, window_data, distance): peak1_idx = window_data.idxmax() if peak1_idx + distance >= window_data.index[-1]: peak2_idx = window_data.loc[: (peak1_idx - distance + 1)].idxmax() else: peak2_idx = window_data.loc[(peak1_idx + distance) :].idxmax() return peak2_idx def find_second_bottom(self, window_data, distance): bottom1_idx = window_data.idxmin() if bottom1_idx + distance >= window_data.index[-1]: bottom2_idx = window_data.loc[: (bottom1_idx - distance + 1)].idxmin() else: bottom2_idx = window_data.loc[(bottom1_idx + distance) :].idxmin() return bottom2_idx