# source: https://raw.githubusercontent.com/jonasreycian/freqtrade_strategy/1088952805c239a7d37a42c7ff69ee84cecc117a/user_data/strategies/uptrend_strategy.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # --- Do not remove these libs --- import numpy as np # noqa import pandas as pd # noqa from pandas import DataFrame from freqtrade.strategy import ( BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter, informative, ) # -------------------------------- # Add your lib to import here import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib from datetime import datetime from freqtrade.persistence import Trade from freqtrade.strategy import stoploss_from_open class github_jonasreycian_freqtrade_strategy__uptrend_strategy__20211118_110626(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_buy_trend, populate_sell_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 = 2 # ROI table: minimal_roi = {"0": 0.252, "93": 0.134, "246": 0.057, "595": 0} # Stoploss: stoploss = -0.079 # Trailing stop: trailing_stop = True trailing_stop_positive = 0.26 trailing_stop_positive_offset = 0.337 trailing_only_offset_is_reached = True # Optimal timeframe for the strategy. timeframe = "15m" # Run "populate_indicators()" only for new candle. process_only_new_candles = False # These values can be overridden in the "ask_strategy" section in the config. use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = False # use_custom_stoploss = True # Number of candles the strategy requires before producing valid signals # startup_candle_count: int = 50 # Optional order type mapping. order_types = { "buy": "limit", "sell": "limit", "stoploss": "market", "stoploss_on_exchange": True, } # Optional order time in force. order_time_in_force = {"buy": "gtc", "sell": "gtc"} plot_config = { # Main plot indicators (Moving averages, ...) "main_plot": { "sma5": {"color": "yellow"}, "sma24": {"color": "red"}, "sma50": {"color": "violet"}, "sma100": {"color": "pink"}, }, "subplots": { # Subplots - each dict defines one additional plot "MACD": { "macdhist": {"color": "green"}, }, "RSI": { "rsi_14": {"color": "green"}, "rsi_30": {"color": "red"}, }, }, } # Define informative upper timeframe for each pair. Decorators can be stacked on same # method. Available in populate_indicators as 'rsi_30m' and 'rsi_1h'. @informative("30m") # @informative("1h") # @informative("1d") def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe["rsi_14"] = ta.RSI(dataframe, timeperiod=14) dataframe["rsi_30"] = ta.RSI(dataframe, timeperiod=30) dataframe["sma200"] = ta.SMA(dataframe, timeperiod=200) return dataframe 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 """ # Delete not usable columns dataframe = dataframe.drop( columns=[ "open_30m", "high_30m", "low_30m", "close_30m", "volume_30m", ], axis=1, ) # Calculate rsi of the original dataframe (15m timeframe) dataframe["rsi_14"] = ta.RSI(dataframe, timeperiod=14) dataframe["rsi_30"] = ta.RSI(dataframe, timeperiod=30) # MACD macd = ta.MACD(dataframe) dataframe["macd"] = macd["macd"] dataframe["macdsignal"] = macd["macdsignal"] dataframe["macdhist"] = macd["macdhist"] # # SMA - Simple Moving Average dataframe["sma5"] = ta.SMA(dataframe, timeperiod=5) dataframe["sma10"] = ta.SMA(dataframe, timeperiod=10) dataframe["sma24"] = ta.SMA(dataframe, timeperiod=24) dataframe["sma50"] = ta.SMA(dataframe, timeperiod=50) dataframe["sma100"] = ta.SMA(dataframe, timeperiod=100) dataframe["sma200"] = ta.SMA(dataframe, timeperiod=200) dataframe = dataframe.dropna() # #print("-------------------") # #print("-- inFORMATIVE") # #print("-------------------") # #print(dataframe.head()) # #print(dataframe.columns.tolist()) # Retrieve best bid and best ask from the orderbook # ------------------------------------ """ # first check if dataprovider is available if self.dp: if self.dp.runmode.value in ('live', 'dry_run'): ob = self.dp.orderbook(metadata['pair'], 1) dataframe['best_bid'] = ob['bids'][0][0] dataframe['best_ask'] = ob['asks'][0][0] """ return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the buy signal for the given dataframe :param dataframe: DataFrame populated with indicators :param metadata: Additional information, like the currently traded pair :return: DataFrame with buy column """ # Close price should trail around 4% from sma24 trail_percent = ( abs(dataframe["close"] - dataframe["sma24"]) / dataframe["sma24"] * 100 ) dataframe.loc[ ( ( ( (dataframe["sma5"] > dataframe["sma24"]) & (dataframe["sma24"] > dataframe["sma50"]) & (dataframe["sma50"] > dataframe["sma100"]) ) # & (trail_percent <= 4.0) # | (dataframe["sma100"] > dataframe["sma200"]) ) ), "buy", ] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the sell signal for the given dataframe :param dataframe: DataFrame populated with indicators :param metadata: Additional information, like the currently traded pair :return: DataFrame with buy column """ # dataframe.loc[ # ( # (dataframe["sma5"] < dataframe["sma10"]) # # | ( # # (dataframe["rsi_14"] < dataframe["rsi_30"]) # # & (dataframe["macdhist"] < 0) # # ) # ), # "sell", # ] = 1 return dataframe pair_list = [ "BTC/USDT", "ETH/USDT", "SHIB/USDT", "OMG/USDT", "MANA/USDT", "LRC/USDT", "XRP/USDT", "LTC/USDT", "CTSI/USDT", "DOT/USDT", "SOL/USDT", "SAND/USDT", "TRX/USDT", "DOGE/USDT", "ADA/USDT", "FIL/USDT", "CHZ/USDT", "MINA/USDT", "IOTX/USDT", "ALGO/USDT", "CHR/USDT", "ATA/USDT", "LINK/USDT", "FTM/USDT", "ENS/USDT", "AVAX/USDT", "NEAR/USDT", "USDC/USDT", "LUNA/USDT", "VET/USDT", "MATIC/USDT", "OGN/USDT", "LTO/USDT", "ZEC/USDT", "AXS/USDT", "EOS/USDT", "ICP/USDT", "UMA/USDT", "ROSE/USDT", "ETC/USDT", "THETA/USDT", "SLP/USDT", "ARPA/USDT", "DYDX/USDT", "TVK/USDT", "NKN/USDT", "ENJ/USDT", "ATOM/USDT", "TWT/USDT", "MASK/USDT", ]