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a/__pycache__/ChanZS.cpython-312.pyc b/__pycache__/ChanZS.cpython-312.pyc index 0d7b9ed..db18ccb 100644 Binary files a/__pycache__/ChanZS.cpython-312.pyc and b/__pycache__/ChanZS.cpython-312.pyc differ diff --git a/config/ChanLun_BTC_15.json b/config/ChanLun_BTC_15.json new file mode 100644 index 0000000..51a4322 --- /dev/null +++ b/config/ChanLun_BTC_15.json @@ -0,0 +1,83 @@ +{ + "$schema": "https://schema.freqtrade.io/schema.json", + "max_open_trades": 1, + "stake_currency": "USDT", + "stake_amount": "unlimited", + "tradable_balance_ratio": 0.99, + "fiat_display_currency": "USD", + "dry_run": false, + "db_url": "sqlite:///tradesv3.chanlun_btc_15.sqlite", + "dry_run_wallet": 1000, + "cancel_open_orders_on_exit": true, + "trading_mode": "futures", + "margin_mode": "isolated", + "can_short" : true, + "timeframe" : "1m", + "process_only_new_candles" : false, + "unfilledtimeout": { + "entry": 1, + "exit": 1, + "exit_timeout_count": 5, + "unit": "minutes" + }, + "entry_pricing": { + "price_side": "same", + "use_order_book": true, + "order_book_top": 1, + "price_last_balance": 0.0, + "check_depth_of_market": { + "enabled": false, + "bids_to_ask_delta": 1 + } + }, + "exit_pricing":{ + "price_side": "same", + "use_order_book": true, + "order_book_top": 1 + }, + "exchange": { + "name": "binance", + "key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8", + "secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l", + "ccxt_config": {}, + "ccxt_async_config": {}, + "pair_whitelist": [ + "BTC/USDT:USDT" + ], + "pair_blacklist": [ + "BNB/.*" + ] + }, + "pairlists": [ + { + "method": "StaticPairList", + "number_assets": 1, + "sort_key": "quoteVolume", + "min_value": 0, + "refresh_period": 1800 + } + ], + "telegram": { + "enabled": true, + "token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y", + "chat_id": "580807463" + }, + "api_server": { + "enabled": false, + "listen_ip_address": "127.0.0.1", + "listen_port": 8811, + "verbosity": "error", + "enable_openapi": false, + "jwt_secret_key": "14d3510740e2c39a973a8895f1aa2704d98d08b86170260085709fa5ea48251d", + "ws_token": "dtKKDnafBrX4icq_ZCw7acJTahTK4h_yvg", + "CORS_origins": [], + "username": "freqtrader", + "password": "FreqTrade007" + }, + "bot_name": "freqtrade", + "initial_state": "running", + "force_entry_enable": false, + "internals": { + "process_throttle_secs": 2 + } +} \ No newline at end of file diff --git a/strategies/ChanLun_BTC_15.py b/strategies/ChanLun_BTC_15.py new file mode 100644 index 0000000..c39be91 --- /dev/null +++ b/strategies/ChanLun_BTC_15.py @@ -0,0 +1,204 @@ +# --- Do not remove these libs --- +from statistics import median +from freqtrade.strategy import IStrategy +import sys +import os +# 添加父目录到系统路径 +sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +from ChanLun import ChanLun +from ChanLun_Classifier import ChanLunClassifier +from ChanEnum import Chan_FX_TYPE, Chan_KLC_FX, Chan_BI_DIR, Chan_KLC_FX +# -------------------------------- +from technical.util import resample_to_interval, resampled_merge +import talib.abstract as ta +from pandas import DataFrame +from datetime import datetime, timedelta +from freqtrade.persistence import Trade +from typing import Optional +import logging +logger = logging.getLogger(__name__) +### Now you can use logger.info('asfd') to log +# freqtrade plot-dataframe --strategy ChanLun_BTC_15 --datadir user_data/data/binance -c ./user_data/ChanLun_SOL_15.json --timerange=20250309- + +# freqtrade trade -c ./user_data/Chan/config/ChanLun_BTC_15.json --strategy ChanLun_BTC_15 --strategy-path ./user_data/Chan/strategies +# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_BTC_15.json --strategy ChanLun_BTC_15 --strategy-path ./user_data/Chan/strategies --timerange=20250416- +# freqtrade download-data -c ./user_data/Chan/config/ChanLun_BTC_15.json -t 1m --pairs BTC/USDT:USDT --timerange=20250405- +# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi stoploss --strategy ChanLun_BTC_15 --strategy-path ./user_data/Chan/strategies -c ./user_data/Chan/config/ChanLun_BTC_15.json -e 200 --timerange=20250201-20250401 + +# sudo docker compose run --rm chan_btc backtesting -c ./user_data/Chan/config/ChanLun_SOL.json --strategy ChanLun_SOL --strategy-path ./user_data/Chan/strategies --timerange=20250101- +# sudo docker compose run --rm chan_btc download-data -c ./user_data/Chan/config/ChanLun_BTC_15.json --pairs BTC/USDT:USDT -t 1m --timerange 20240101- +# sudo docker compose run --rm chan_btc trade -c ./user_data/Chan/config/ChanLun_BTC_15.json --strategy ChanLun_BTC_15 --strategy-path ./user_data/Chan/strategies + +class ChanLun_BTC_15(IStrategy): + INTERFACE_VERSION: int = 3 + # Minimal ROI designed for the strategy. + # This attribute will be overridden if the config file contains "minimal_roi" + # 30m and 1h + minimal_roi = { + "0": 0.30, + "360": 0.2, + "640": 0.1, + "1200": 0 + } + # 5m and 15m + minimal_roi_1 = { + "0": 0.1, + "60": 0.05, + "120": 0.02, + "240": 0 + } + # 15m and 30m + minimal_roi_1 = { + "0": 0.1, + "240": 0.05, + "480": 0.03, + "600": 0 + } + minimal_roi_2 = { + "0": 0.10, + "1200": 0.05, + "2400": 0.025, + "3600": 0 + } + can_short = True + lev = 20.0 + stoploss = -0.3 + trailing_stop = False + trailing_stop_positive = 0.025 + trailing_stop_positive_offset = 0.045 + trailing_only_offset_is_reached = False + + position_adjustment_enable = True + startup_candle_count = 600 + + time5 = 5 + time15 = 15 + time30 = 30 + time60 = 60 + time4h = 240 + time5 = 15 + last_time = datetime.now() + chan = ChanLun() + classifier = ChanLunClassifier(None) + def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + + # resample our dataframes + dataframe_5 = resample_to_interval(dataframe, self.get_ticker_indicator() * 5) + dataframe_15 = resample_to_interval(dataframe, self.get_ticker_indicator() * 15) + dataframe_30 = resample_to_interval(dataframe, self.get_ticker_indicator() * 30) + dataframe_60 = resample_to_interval(dataframe, self.get_ticker_indicator() * 60) + dataframe_4h = resample_to_interval(dataframe, self.get_ticker_indicator() * 240) + + #dataframe_1d = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1d') + #dataframe_1w = resample_to_interval(dataframe_1d, self.get_ticker_indicator() * 10080) + #dataframe_1m = resample_to_interval(dataframe_1d, self.get_ticker_indicator() * 43200) + + dataframe_1d = resample_to_interval(dataframe, self.get_ticker_indicator() * 1440) + #dataframe_1w = resample_to_interval(dataframe, self.get_ticker_indicator() * 10080) + #dataframe_1m = resample_to_interval(dataframe, self.get_ticker_indicator() * 43200) + dataframe = self.add_indicators(dataframe) + dataframe_5 = self.add_indicators(dataframe_5) + dataframe_30 = self.add_indicators(dataframe_30) + dataframe_60 = self.add_indicators(dataframe_60) + dataframe_4h = self.add_indicators(dataframe_4h) + dataframe_1d = self.add_indicators(dataframe_1d) + #self.chan.plot_dual(dataframe_5, dataframe_30) + dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) + + state_list, fx_list = self.chan.get_klc_strength_list(dataframe_15) + dataframe_15['state'] = state_list + dataframe_15['fx'] = fx_list + klc_list = self.chan.get_klc_list(dataframe_15) + bi_list = self.chan.cal_bi_list(klc_list) + if self.last_time + timedelta(minutes=1) < datetime.now(): + print(state_list[-1], state_list[-2], state_list[-3], state_list[-4], state_list[-5]) + print(fx_list[-1], fx_list[-2], fx_list[-3], fx_list[-4], fx_list[-5]) + print(klc_list[-1].klc_fx_type, klc_list[-2].klc_fx_type, klc_list[-3].klc_fx_type, klc_list[-4].klc_fx_type, klc_list[-5].klc_fx_type) + print("-------------------------------------------------------------------------------") + self.last_time = datetime.now() + #dataframe = resampled_merge(dataframe, dataframe_5) + dataframe = resampled_merge(dataframe, dataframe_15) + #dataframe = resampled_merge(dataframe, dataframe_30) + #dataframe = resampled_merge(dataframe, dataframe_60) + #dataframe = resampled_merge(dataframe, dataframe_4h) + return dataframe + + def add_indicators(self, df): + fast = 8 + slow = 16 + period = 6 + macd = ta.MACD(df, fastperiod=fast, slowperiod=slow, signalperiod=period) + df['macd'] = macd['macd'] + df['macdsignal'] = macd['macdsignal'] + df['macdhist'] = macd['macdhist'] + df['ma5'] = ta.MA(df, timeperiod=5) + df['ma10'] = ta.MA(df, timeperiod=10) + df['ma30'] = ta.EMA(df, timeperiod=30) + df['ma250'] = ta.MA(df, timeperiod=250) + df['rsi'] = ta.RSI(df, timeperiod=14) + df['volume_ratio'] = self.cal_volume_ratio(df) + return df + def cal_volume_ratio(self, dataframe, window=10): + df = dataframe.copy() + # 计算过去N根K线的平均成交量 + df['avg_volume'] = df['volume'].rolling(window=window).mean() + # 计算量比 + df['volume_ratio'] = df['volume'] / df['avg_volume'] + # 填充缺失值（前N根K线） + df['volume_ratio'] = df['volume_ratio'].fillna(1.0) + return df['volume_ratio'] + def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + state_str = 'resample_{}_state'.format(self.get_ticker_indicator()*self.time5) + fx_str = 'resample_{}_fx'.format(self.get_ticker_indicator()*self.time5) + dataframe.loc[ + ( + #(dataframe['state'] == "-30") + (dataframe[state_str].shift(self.time5*2) > 1.0) & + (dataframe[fx_str].shift(self.time5*2) == -1) + #(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10") & + #(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "-10") & + #(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10") + #(qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal'])) + ), + ['enter_long', 'enter_tag']] = (1, 'long_signal_chan') + dataframe.loc[ + ( + #(dataframe['state'] == "-30") + (dataframe[state_str].shift(self.time5*2) > 1.0) & + (dataframe[fx_str].shift(self.time5*2) == 1) + #(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10") & + #(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "-10") & + #(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10") + #(qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal'])) + ), + ['enter_short', 'enter_tag']] = (1, 'short_signal_chan') + return dataframe + def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + state_str = 'resample_{}_state'.format(self.get_ticker_indicator()*self.time5) + fx_str = 'resample_{}_fx'.format(self.get_ticker_indicator()*self.time5) + dataframe.loc[ + ( + #(dataframe['state']== "30") + (dataframe[state_str].shift(self.time5*2) > 1.0) & + (dataframe[fx_str].shift(self.time5*2) == 1) + #(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "10") & + #(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time60)] == "10") + ), + ['exit_long', 'exit_tag']] = (1, 'long_close_signal_chan') + dataframe.loc[ + ( + #(dataframe['state']== "30") + (dataframe[state_str].shift(self.time5*2) > 1.0) & + (dataframe[fx_str].shift(self.time5*2) == -1) + #(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "10") & + #(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time60)] == "10") + ), + ['exit_short', 'exit_tag']] = (1, 'short_close_signal_chan') + return dataframe + def leverage(self, pair: str, current_time: datetime, current_rate: float, + proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str, + **kwargs) -> float: + return self.lev + + def get_ticker_indicator(self): + return int(self.timeframe[:-1]) \ No newline at end of file