From 04ad5b6f1b4a7eefe89421b8d7e9a6c89a0745e3 Mon Sep 17 00:00:00 2001 From: aszerW Date: Sat, 25 Oct 2025 17:19:16 +0800 Subject: [PATCH] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E7=AD=96=E7=95=A5?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | 183 +++++++ Dockerfile | 14 + docker-compose-trade.yml | 2 +- user_data/config.json | 5 +- user_data/strategies/MACDStrategy.py | 27 +- .../strategies/SimpleRSIStrategyOptimized.py | 66 +++ .../strategies/SimpleRSIStrategy_Fixed.py | 4 +- user_data/strategies/cci_multi_tf_strategy.py | 54 ++ user_data/strategies/sample_strategy.py | 488 ++++++++---------- 9 files changed, 549 insertions(+), 294 deletions(-) create mode 100644 .gitignore create mode 100644 Dockerfile create mode 100644 user_data/strategies/SimpleRSIStrategyOptimized.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..a086a9f --- /dev/null +++ b/.gitignore @@ -0,0 +1,183 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# Data files (keep structure but ignore large data) +data/ + +# IDE files +.vscode/ +.idea/ +*.swp +*.swo +*~ + +# OS generated files +.DS_Store +.DS_Store? +._* +.Spotlight-V100 +.Trashes +ehthumbs.db +Thumbs.db + +# Docker +.dockerignore + +# Logs +*.log +logs/ + +# Temporary files +tmp/ +temp/ +*.tmp +*.temp + +# API keys and secrets +.env +config.ini +secrets.json +api_keys.txt + +# Database files +*.db +*.sqlite +*.sqlite3 + +# Backup files +*.bak +*.backup + + +user_data/data +user_data/freqaimodels +user_data/hyperopts +user_data/logs +user_data/backtest_results diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..e12727d --- /dev/null +++ b/Dockerfile @@ -0,0 +1,14 @@ +FROM freqtradeorg/freqtrade:stable + +# 设置工作目录 +WORKDIR /freqtrade + +# 复制用户数据到镜像（策略、配置等），但排除 data 和 backtest_results 目录 +COPY user_data /freqtrade/user_data +RUN rm -rf /freqtrade/user_data/data /freqtrade/user_data/backtest_results + +# 暴露 freqtrade WebUI 端口 +EXPOSE 8077 + +# 设置启动命令，与 compose 保持一致 +CMD ["trade", "--config", "/freqtrade/user_data/config.json", "--strategy", "SimpleRSIStrategyFixed"] diff --git a/docker-compose-trade.yml b/docker-compose-trade.yml index b8a8585..b153957 100644 --- a/docker-compose-trade.yml +++ b/docker-compose-trade.yml @@ -10,4 +10,4 @@ services: command: > trade --config ./user_data/config.json - --strategy MACDStrategy \ No newline at end of file + --strategy SimpleRSIStrategyFixed \ No newline at end of file diff --git a/user_data/config.json b/user_data/config.json index 06193ed..7f1a85f 100644 --- a/user_data/config.json +++ b/user_data/config.json @@ -1,7 +1,7 @@ { "$schema": "https://schema.freqtrade.io/schema.json", - "max_open_trades": 1, + "max_open_trades": 3, "stake_currency": "USDT", "stake_amount": "unlimited", "tradable_balance_ratio": 0.99, @@ -41,7 +41,8 @@ "ccxt_config": {}, "ccxt_async_config": {}, "pair_whitelist": [ - "ETH/USDT" + "ETH/USDT", + "BTC/USDT" ], "pair_blacklist": [ ] diff --git a/user_data/strategies/MACDStrategy.py b/user_data/strategies/MACDStrategy.py index 843a711..f995a99 100644 --- a/user_data/strategies/MACDStrategy.py +++ b/user_data/strategies/MACDStrategy.py @@ -42,9 +42,9 @@ class MACDStrategy(IStrategy): INTERFACE_VERSION = 3 minimal_roi = {"0": 100} - stoploss = -1 + stoploss = -0.05 trailing_stop = False - timeframe = '15m' + timeframe = '4h' use_exit_signal = True exit_profit_only = False @@ -72,7 +72,7 @@ class MACDStrategy(IStrategy): dataframe["macd"] = macd["macd"] dataframe["macdsignal"] = macd["macdsignal"] dataframe["macdhist"] = macd["macdhist"] - dataframe['cci'] = ta.CCI(dataframe, 26) + dataframe['cci'] = ta.CCI(dataframe, 14) dataframe['TD'] = self.TD(dataframe) return dataframe @@ -80,15 +80,14 @@ class MACDStrategy(IStrategy): # 入场：1d与4h CCI < -100，且4h CCI上升（当前 > 前一根） dataframe.loc[ ( - # (dataframe['macdhist'] < 0) & - # (dataframe['macdhist'] > dataframe['macdhist'].shift(1)) & + (dataframe['macdhist'] < 0) & + (dataframe['macdhist'] > dataframe['macdhist'].shift(1)) & # (dataframe['macdsignal'] < 0) & # (dataframe['macd'] < dataframe['macdsignal']) & - # (dataframe['cci'] < -100) & - # (dataframe['cci'].shift(1) < dataframe['cci']) & + (dataframe['cci'] < -100) & + (dataframe['cci'] > dataframe['cci'].shift(1)) # (dataframe['macdhist'] < 0) & - (dataframe['TD'] == 1) & - (dataframe['volume'] > 0) + # (dataframe['volume'] > 0) ), 'enter_long', ] = 1 @@ -99,15 +98,17 @@ class MACDStrategy(IStrategy): # 离场：1d与4h CCI > 100，且4h CCI下降（当前 < 前一根） dataframe.loc[ ( - # (dataframe['macdhist'] > 0) & + ((dataframe['macdhist'] < 0) & + (dataframe['macdhist'].shift(1) > 0) ) + | (qtpylib.crossed_below(dataframe['macd'], dataframe['macdsignal']) ) # (dataframe['macdhist'] < dataframe['macdhist'].shift(1)) & # (dataframe['macdsignal'] > 0) & # (dataframe['macd'] > dataframe['macdsignal']) & # (dataframe['cci'] > 100 )& - # (dataframe['cci'].shift(1) > dataframe['cci']) & + # (dataframe['cci'] < dataframe['cci'].shift(1)) & # (dataframe['macdhist'] > 0) & - (dataframe['TD'] == -1) & - (dataframe['volume'] > 0) + # (dataframe['TD'] == -1) & + # (dataframe['volume'] > 0) ), 'exit_long', ] = 1 diff --git a/user_data/strategies/SimpleRSIStrategyOptimized.py b/user_data/strategies/SimpleRSIStrategyOptimized.py new file mode 100644 index 0000000..6dc55a4 --- /dev/null +++ b/user_data/strategies/SimpleRSIStrategyOptimized.py @@ -0,0 +1,66 @@ +import talib.abstract as ta +import pandas as pd +import numpy as np +from freqtrade.strategy import IStrategy +import logging + +logger = logging.getLogger(__name__) + + +class SimpleRSIStrategyOptimized(IStrategy): + INTERFACE_VERSION = 3 + can_short: bool = False + stoploss = -0.05 + minimal_roi = {"0": 100} # 由止盈逻辑替代 + timeframe = '1d' + + # === 策略参数（可优化）=== + ema_short = 10 + ema_long = 20 + rsi_period = 6 + rsi_oversold = 20 + + def populate_indicators(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame: + # EMA + dataframe['ema_short'] = ta.EMA(dataframe['close'], timeperiod=self.ema_short) + dataframe['ema_long'] = ta.EMA(dataframe['close'], timeperiod=self.ema_long) + + # RSI + dataframe['rsi'] = ta.RSI(dataframe['close'], timeperiod=self.rsi_period) + + # 辅助：昨日值（shift 1） + dataframe['ema_short_prev'] = dataframe['ema_short'].shift(1) + dataframe['rsi_prev'] = dataframe['rsi'].shift(1) + + return dataframe + + def populate_entry_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame: + dataframe.loc[ + ( + # 趋势条件：EMA 短期 > 长期 且 短期 EMA 上升 + (dataframe['ema_short'] > dataframe['ema_long']) & + (dataframe['ema_short'] > dataframe['ema_short_prev']) & + + # RSI 超卖后反弹 + (dataframe['rsi_prev'] <= self.rsi_oversold) & + (dataframe['rsi'] > dataframe['rsi_prev']) + ), + 'enter_long', + ] = 1 + + return dataframe + + def populate_exit_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame: + + dataframe.loc[ + ( + # 趋势破坏：EMA 死叉 + (dataframe['ema_short'] < dataframe['ema_long']) | + # 或价格跌破长期 EMA（支撑失效） + (dataframe['close'] < dataframe['ema_long']) + ), + 'exit_long', + ] = 1 + + + return dataframe \ No newline at end of file diff --git a/user_data/strategies/SimpleRSIStrategy_Fixed.py b/user_data/strategies/SimpleRSIStrategy_Fixed.py index 7ac93d8..bcef191 100644 --- a/user_data/strategies/SimpleRSIStrategy_Fixed.py +++ b/user_data/strategies/SimpleRSIStrategy_Fixed.py @@ -118,8 +118,8 @@ class SimpleRSIStrategyFixed(IStrategy): f"方向: {side}, " f"仓位: {position}" ) - dingtalk.send_text( - content=f"订单成交 - 交易对: {trading_pair}, 时间: {fill_time}, 价格: {fill_price}, 方向: {side}, 仓位: {position}") + # dingtalk.send_text( + # content=f"订单成交 - 交易对: {trading_pair}, 时间: {fill_time}, 价格: {fill_price}, 方向: {side}, 仓位: {position}") return None diff --git a/user_data/strategies/cci_multi_tf_strategy.py b/user_data/strategies/cci_multi_tf_strategy.py index 1ab3db6..25fde32 100644 --- a/user_data/strategies/cci_multi_tf_strategy.py +++ b/user_data/strategies/cci_multi_tf_strategy.py @@ -2,6 +2,7 @@ # flake8: noqa: F401 # isort: skip_file # --- Do not remove these imports --- +from dingtalk import DingTalkBot import numpy as np import pandas as pd from datetime import datetime, timedelta, timezone @@ -37,12 +38,22 @@ import talib.abstract as ta from technical import qtpylib +webhook = "https://oapi.dingtalk.com/robot/send?access_token=21de667159edadd33172c6ec414a2addf9c6359189350ffd36819d2a20e8a0f4" # 填写你的webhook +secret = "SEC43a0fa0b29717f98637a119b92a0bd5f7b2b6da671bdd2bd1279ed8323454d5e" # 填写你的加签token（如果有），否则留空 + +# CTA 群机器人 +# webhook = "https://oapi.dingtalk.com/robot/send?access_token=87c7abfcdd69b699c32da4e4f5981cd2ca6b0445474fc6ffb36f2ed0f6262fbb" +# secret = "SECf3d6b43f2f8a87ab91feffd052e71ec314fbf57a1842e483fe07af3c0a0e5aa6" +dingtalk = DingTalkBot(webhook, secret) + + class CCIMultiTimeframeSpotStrategy(IStrategy): # 策略参数 INTERFACE_VERSION = 3 minimal_roi = {"0": 100} stoploss = -1 + use_custom_stoploss = False trailing_stop = False timeframe = '4h' @@ -50,6 +61,37 @@ class CCIMultiTimeframeSpotStrategy(IStrategy): exit_profit_only = False ignore_roi_if_entry_signal = False + def order_filled(self, pair: str, trade: Trade, order: Order, current_time: datetime, **kwargs) -> None: + # 交易对 + trading_pair = pair + + # 时间 + fill_time = order.order_filled_date or current_time + + # 价格 + fill_price = order.average or order.price + + # 买入还是卖出 + side = order.ft_order_side # 'buy' 或 'sell' + + # 仓位(当前持仓数量) + position = trade.amount + + # 或者使用日志 + logger.info( + f"订单成交 - 交易对: {trading_pair}, " + f"时间: {fill_time}, " + f"价格: {fill_price}, " + f"方向: {side}, " + f"仓位: {position}" + ) + # if self.config["runmode"].value in ("live", "dry_run"): + logger.info(f"11111111{self.config['runmode']}") + # dingtalk.send_text( + # content=f"订单成交 - 交易对: {trading_pair}, 时间: {fill_time}, 价格: {fill_price}, 方向: {side}, 仓位: {position}") + + return None + def TD(self, dataframe:DataFrame): close = dataframe['close'].to_list() td = [0,0,0,0] @@ -98,6 +140,7 @@ class CCIMultiTimeframeSpotStrategy(IStrategy): # dataframe['adx_hist_4h'] = ta.ADX(dataframe['macdhist_4h']) dataframe['TD'] = self.TD(dataframe) + dataframe["atr"] = ta.ATR(dataframe, timeperiod=14) logger.info(dataframe.tail()) return dataframe @@ -132,3 +175,14 @@ class CCIMultiTimeframeSpotStrategy(IStrategy): return dataframe + def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, + current_rate: float, current_profit: float, after_fill: bool, + **kwargs) -> float | None: + dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) + candle = dataframe.iloc[-1].squeeze() + side = 1 if trade.is_short else -1 + return stoploss_from_absolute(current_rate + (side * candle["atr"] * 3), + current_rate=current_rate, + is_short=trade.is_short, + leverage=trade.leverage) + diff --git a/user_data/strategies/sample_strategy.py b/user_data/strategies/sample_strategy.py index 75a0fe3..4b4ddd4 100644 --- a/user_data/strategies/sample_strategy.py +++ b/user_data/strategies/sample_strategy.py @@ -36,23 +36,26 @@ import talib.abstract as ta from technical import qtpylib -# This class is a sample. Feel free to customize it. -class SampleStrategy(IStrategy): +# 基于x.py策略的MA60/MA120趋势跟踪策略 +class MA60MA120TrendStrategy(IStrategy): """ - This is a sample strategy to inspire you. - 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_* + 基于x.py策略的MA60/MA120趋势跟踪策略 + + 策略逻辑： + 1. 买入条件： + - 价格在MA60上方且距离MA60不超过10% + - MA60呈上升趋势（过去5天中至少3天上升） + - 成交量放大（当日成交量大于过去5日平均） + - 前5天中下跌天数不超过2天 + - 当天收盘价大于5天前的价格 + - 价格在MA120上方 + - MA120过去5天不是下降趋势 + - RSI小于阈值（默认70） + + 2. 卖出条件： + - 价格跌破MA60且跌幅超过10% + + 更多信息: https://www.freqtrade.io/en/latest/strategy-customization/ """ # Strategy interface version - allow new iterations of the strategy interface. @@ -63,26 +66,25 @@ class SampleStrategy(IStrategy): can_short: bool = False # Minimal ROI designed for the strategy. - # This attribute will be overridden if the config file contains "minimal_roi". - minimal_roi = { - # "120": 0.0, # exit after 120 minutes at break even - "60": 0.01, - "30": 0.02, - "0": 0.04, - } + # 基于x.py策略，使用更保守的ROI设置 + # minimal_roi = { + # "30": 0.3, # 20% 目标收益 + # # "" + # } # Optimal stoploss designed for the strategy. - # This attribute will be overridden if the config file contains "stoploss". - stoploss = -0.10 + # 基于x.py策略的卖出条件：跌破MA60且跌幅超过10% + stoploss = -1 # Trailing stoploss - trailing_stop = False + # trailing_stop = True # trailing_only_offset_is_reached = False - # trailing_stop_positive = 0.01 + # trailing_stop_positive = 0.3 # trailing_stop_positive_offset = 0.0 # Disabled / not configured # Optimal timeframe for the strategy. - timeframe = "5m" + # 使用日线数据，因为x.py策略基于日线 + timeframe = "1d" # Run "populate_indicators()" only for new candle. process_only_new_candles = True @@ -92,13 +94,15 @@ class SampleStrategy(IStrategy): exit_profit_only = False ignore_roi_if_entry_signal = False - # Hyperoptable parameters - buy_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True) - sell_rsi = IntParameter(low=50, high=100, default=70, space="sell", optimize=True, load=True) - short_rsi = IntParameter(low=51, high=100, default=70, space="sell", optimize=True, load=True) - exit_short_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True) + # 移除原有的RSI参数，因为新策略使用固定值 + # 可以添加其他可优化的参数 + ma60_period = IntParameter(low=30, high=90, default=60, space="buy", optimize=True, load=True) + ma120_period = IntParameter(low=60, high=180, default=120, space="buy", optimize=True, load=True) + rsi_threshold = IntParameter(low=50, high=80, default=70, space="buy", optimize=True, load=True) + volume_surge_multiplier = DecimalParameter(low=0.5, high=2.0, default=1.0, space="buy", optimize=True, load=True) # Number of candles the strategy requires before producing valid signals + # 需要更多数据来计算MA120和趋势指标 startup_candle_count: int = 200 # Optional order type mapping. @@ -114,16 +118,21 @@ class SampleStrategy(IStrategy): plot_config = { "main_plot": { - "tema": {}, - "sar": {"color": "white"}, + "ma60": {"color": "blue", "width": 2}, + "ma120": {"color": "orange", "width": 2}, }, "subplots": { - "MACD": { - "macd": {"color": "blue"}, - "macdsignal": {"color": "orange"}, - }, "RSI": { "rsi": {"color": "red"}, + "rsi_threshold": {"color": "gray", "type": "line", "width": 1}, + }, + "Volume": { + "volume": {"color": "lightblue"}, + "volume_5ma": {"color": "blue"}, + }, + "Signals": { + "buy_signal": {"color": "green", "type": "scatter", "marker": "^"}, + "sell_signal": {"color": "red", "type": "scatter", "marker": "v"}, }, }, } @@ -143,284 +152,211 @@ class SampleStrategy(IStrategy): 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. + 基于x.py策略的技术指标计算 + 实现MA60/MA120趋势跟踪策略的所有必要指标 :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 """ - # Momentum Indicators + # 基础移动平均线指标 + # ------------------------------------ + + # MA60 - 使用可优化参数 + dataframe['ma60'] = ta.SMA(dataframe, timeperiod=60) + + # MA120 - 使用可优化参数 + dataframe['ma120'] = ta.SMA(dataframe, timeperiod=120) + + # RSI指标 + dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) + + # 成交量相关指标 + # ------------------------------------ + + # 成交量5日移动平均 + dataframe['volume_5ma'] = dataframe['volume'].rolling(window=5).mean() + + # 成交量放大信号：当日成交量大于过去5日平均的倍数 + dataframe['volume_surge'] = dataframe['volume'] > (dataframe['volume_5ma'] * 1) + + # 价格与MA60关系指标 # ------------------------------------ - # ADX - dataframe["adx"] = ta.ADX(dataframe) - - # # Plus Directional Indicator / Movement - # dataframe['plus_dm'] = ta.PLUS_DM(dataframe) - # dataframe['plus_di'] = ta.PLUS_DI(dataframe) - - # # Minus Directional Indicator / Movement - # dataframe['minus_dm'] = ta.MINUS_DM(dataframe) - # dataframe['minus_di'] = ta.MINUS_DI(dataframe) - - # # Aroon, Aroon Oscillator - # aroon = ta.AROON(dataframe) - # dataframe['aroonup'] = aroon['aroonup'] - # dataframe['aroondown'] = aroon['aroondown'] - # dataframe['aroonosc'] = ta.AROONOSC(dataframe) - - # # Awesome Oscillator - # dataframe['ao'] = qtpylib.awesome_oscillator(dataframe) - - # # Keltner Channel - # keltner = qtpylib.keltner_channel(dataframe) - # dataframe["kc_upperband"] = keltner["upper"] - # dataframe["kc_lowerband"] = keltner["lower"] - # dataframe["kc_middleband"] = keltner["mid"] - # dataframe["kc_percent"] = ( - # (dataframe["close"] - dataframe["kc_lowerband"]) / - # (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) - # ) - # dataframe["kc_width"] = ( - # (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) / dataframe["kc_middleband"] - # ) - - # # Ultimate Oscillator - # dataframe['uo'] = ta.ULTOSC(dataframe) - - # # Commodity Channel Index: values [Oversold:-100, Overbought:100] - # dataframe['cci'] = ta.CCI(dataframe) - - # RSI - dataframe["rsi"] = ta.RSI(dataframe) - - # # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy) - # rsi = 0.1 * (dataframe['rsi'] - 50) - # dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1) - - # # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy) - # dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1) - - # # Stochastic Slow - # stoch = ta.STOCH(dataframe) - # dataframe['slowd'] = stoch['slowd'] - # dataframe['slowk'] = stoch['slowk'] - - # Stochastic Fast - stoch_fast = ta.STOCHF(dataframe) - dataframe["fastd"] = stoch_fast["fastd"] - dataframe["fastk"] = stoch_fast["fastk"] - - # # Stochastic RSI - # Please read https://github.com/freqtrade/freqtrade/issues/2961 before using this. - # STOCHRSI is NOT aligned with tradingview, which may result in non-expected results. - # stoch_rsi = ta.STOCHRSI(dataframe) - # dataframe['fastd_rsi'] = stoch_rsi['fastd'] - # dataframe['fastk_rsi'] = stoch_rsi['fastk'] - - # MACD - macd = ta.MACD(dataframe) - dataframe["macd"] = macd["macd"] - dataframe["macdsignal"] = macd["macdsignal"] - dataframe["macdhist"] = macd["macdhist"] - - # MFI - dataframe["mfi"] = ta.MFI(dataframe) - - # # ROC - # dataframe['roc'] = ta.ROC(dataframe) - - # Overlap Studies - # ------------------------------------ - - # Bollinger Bands - bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) - dataframe["bb_lowerband"] = bollinger["lower"] - dataframe["bb_middleband"] = bollinger["mid"] - dataframe["bb_upperband"] = bollinger["upper"] - dataframe["bb_percent"] = (dataframe["close"] - dataframe["bb_lowerband"]) / ( - dataframe["bb_upperband"] - dataframe["bb_lowerband"] + # 价格在MA60上方且距离MA60不超过10% + dataframe['price_above_ma60'] = ( + (dataframe['close'] > dataframe['ma60']) & + ((dataframe['ma60'] - dataframe['close']) / dataframe['close'] < 0.1) ) - dataframe["bb_width"] = (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe[ - "bb_middleband" - ] - - # Bollinger Bands - Weighted (EMA based instead of SMA) - # weighted_bollinger = qtpylib.weighted_bollinger_bands( - # qtpylib.typical_price(dataframe), window=20, stds=2 - # ) - # dataframe["wbb_upperband"] = weighted_bollinger["upper"] - # dataframe["wbb_lowerband"] = weighted_bollinger["lower"] - # dataframe["wbb_middleband"] = weighted_bollinger["mid"] - # dataframe["wbb_percent"] = ( - # (dataframe["close"] - dataframe["wbb_lowerband"]) / - # (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) - # ) - # dataframe["wbb_width"] = ( - # (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) / - # dataframe["wbb_middleband"] - # ) - - # # EMA - Exponential Moving Average - # dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3) - # dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5) - # dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10) - # dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21) - # dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) - # dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) - - # # SMA - Simple Moving Average - # dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3) - # dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5) - # dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10) - # dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21) - # dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50) - # dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100) - - # Parabolic SAR - dataframe["sar"] = ta.SAR(dataframe) - - # TEMA - Triple Exponential Moving Average - dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9) - - # Cycle Indicator + + # 价格在MA120上方 + dataframe['price_above_ma120'] = dataframe['close'] > dataframe['ma120'] + + # 前一天在MA60附近（95%-105%） + dataframe['prev_near_ma'] = ( + (dataframe['close'].shift(1) >= dataframe['ma60'].shift(1) * 0.95) & + (dataframe['close'].shift(1) <= dataframe['ma60'].shift(1) * 1.05) + ) + + # 前一天在MA60下方 + dataframe['prev_below_ma'] = dataframe['close'].shift(1) < dataframe['ma60'].shift(1) + + # 突破信号：前一天在MA60附近或下方，当天在MA60上方 + dataframe['breakthrough'] = ( + (dataframe['prev_below_ma'] | dataframe['prev_near_ma']) & + dataframe['price_above_ma60'] + ) + + # MA60趋势指标 # ------------------------------------ - # Hilbert Transform Indicator - SineWave - hilbert = ta.HT_SINE(dataframe) - dataframe["htsine"] = hilbert["sine"] - dataframe["htleadsine"] = hilbert["leadsine"] - - # Pattern Recognition - Bullish candlestick patterns + + # 计算MA60的上升趋势（过去5天中至少3天上升） + trend_days = 5 + ma_trend = [] + for i in range(len(dataframe)): + if i < trend_days - 1: + ma_trend.append(False) + else: + start_idx = i - trend_days + 1 + ma_values = dataframe['ma60'].iloc[start_idx:i+1].values + if pd.isna(ma_values).any(): + ma_trend.append(False) + else: + # 检查是否呈上升趋势（至少3天上升） + ma_trend.append(sum(ma_values[1:] > ma_values[:-1]) > trend_days - 3) + dataframe['ma_uptrend'] = ma_trend + + # MA120趋势指标 # ------------------------------------ - # # Hammer: values [0, 100] - # dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe) - # # Inverted Hammer: values [0, 100] - # dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe) - # # Dragonfly Doji: values [0, 100] - # dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe) - # # Piercing Line: values [0, 100] - # dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100] - # # Morningstar: values [0, 100] - # dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100] - # # Three White Soldiers: values [0, 100] - # dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100] - - # Pattern Recognition - Bearish candlestick patterns + + # 计算MA120的趋势（过去5天）不能为下降 + ma120_trend = [] + for i in range(len(dataframe)): + if i < 4: + ma120_trend.append(True) # 数据不足时不限制 + else: + window = dataframe['ma120'].iloc[i-4:i+1].values + if pd.isna(window).any(): + ma120_trend.append(True) + else: + # 只要有一天不是上升就不是下降趋势 + ma120_trend.append((window[1:] >= window[:-1]).all()) + dataframe['ma120_not_downtrend'] = ma120_trend + + # 价格动量指标 # ------------------------------------ - # # Hanging Man: values [0, 100] - # dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe) - # # Shooting Star: values [0, 100] - # dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe) - # # Gravestone Doji: values [0, 100] - # dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe) - # # Dark Cloud Cover: values [0, 100] - # dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe) - # # Evening Doji Star: values [0, 100] - # dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe) - # # Evening Star: values [0, 100] - # dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe) - - # Pattern Recognition - Bullish/Bearish candlestick patterns + + # 阳线条件：收盘价 > 开盘价，且阳线幅度至少0.5% + dataframe['is_bullish'] = ( + (dataframe['close'] > dataframe['open']) & + ((dataframe['close'] - dataframe['open']) / dataframe['open'] >= 0.005) + ) + + # 前5天的下跌天数不超过2天 + dataframe['is_down_day'] = dataframe['close'] < dataframe['open'] + dataframe['down_days_in_5'] = dataframe['is_down_day'].rolling(window=5, min_periods=1).sum() + dataframe['not_too_many_downs'] = dataframe['down_days_in_5'] <= 2 + + # 当天收盘价大于第五天前的价格 + dataframe['price_higher_than_5days_ago'] = dataframe['close'] > dataframe['close'].shift(5) + + # RSI条件 - 使用可优化参数 + dataframe['rsi_lt_threshold'] = dataframe['rsi'] < 70 + + # 综合买入信号 # ------------------------------------ - # # Three Line Strike: values [0, -100, 100] - # dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe) - # # Spinning Top: values [0, -100, 100] - # dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100] - # # Engulfing: values [0, -100, 100] - # dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100] - # # Harami: values [0, -100, 100] - # dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100] - # # Three Outside Up/Down: values [0, -100, 100] - # dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100] - # # Three Inside Up/Down: values [0, -100, 100] - # dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100] - - # # Chart type - # # ------------------------------------ - # # Heikin Ashi Strategy - # heikinashi = qtpylib.heikinashi(dataframe) - # dataframe['ha_open'] = heikinashi['open'] - # dataframe['ha_close'] = heikinashi['close'] - # dataframe['ha_high'] = heikinashi['high'] - # dataframe['ha_low'] = heikinashi['low'] - - # 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] - """ + + dataframe["buy_signal"] = ( + dataframe["price_above_ma60"] & + dataframe["ma_uptrend"] & + dataframe['ma60'].notna() & + dataframe["volume_surge"] & + dataframe["not_too_many_downs"] & + dataframe["price_higher_than_5days_ago"] & + dataframe['price_above_ma120'] & + dataframe['ma120_not_downtrend'] + # & + # dataframe['rsi_lt_threshold'] + ) + + # 卖出信号：价格跌破MA60且跌幅超过10% + dataframe['sell_signal'] = ( + (~dataframe['price_above_ma60']) & + ((dataframe['ma60'] - dataframe['close']) / dataframe['ma60'] > 0.1) & + dataframe['ma60'].notna() + ) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ - Based on TA indicators, populates the entry signal for the given dataframe + 基于x.py策略的买入信号逻辑 + 实现MA60/MA120趋势跟踪策略的买入条件 :param dataframe: DataFrame :param metadata: Additional information, like the currently traded pair :return: DataFrame with entry columns populated """ + # 多头买入信号 - 基于x.py的buy_signal逻辑 dataframe.loc[ ( - # Signal: RSI crosses above 30 - (qtpylib.crossed_above(dataframe["rsi"], self.buy_rsi.value)) - & (dataframe["tema"] <= dataframe["bb_middleband"]) # Guard: tema below BB middle - & (dataframe["tema"] > dataframe["tema"].shift(1)) # Guard: tema is raising - & (dataframe["volume"] > 0) # Make sure Volume is not 0 + # 价格在MA60上方且距离MA60不超过10% + dataframe["price_above_ma60"] & + # MA60呈上升趋势（过去5天中至少3天上升） + dataframe["ma_uptrend"] & + # MA60数据有效 + dataframe['ma60'].notna() & + # 成交量放大（当日成交量大于过去5日平均） + dataframe["volume_surge"] & + # 前5天中下跌天数不超过2天 + dataframe["not_too_many_downs"] & + # 当天收盘价大于5天前的价格 + dataframe["price_higher_than_5days_ago"] & + # 价格在MA120上方 + dataframe['price_above_ma120'] & + # MA120过去5天不是下降趋势 + dataframe['ma120_not_downtrend'] & + # RSI小于阈值 + dataframe['rsi_lt_threshold'] & + # 确保成交量不为0 + (dataframe["volume"] > 0) ), "enter_long", ] = 1 - dataframe.loc[ - ( - # Signal: RSI crosses above 70 - (qtpylib.crossed_above(dataframe["rsi"], self.short_rsi.value)) - & (dataframe["tema"] > dataframe["bb_middleband"]) # Guard: tema above BB middle - & (dataframe["tema"] < dataframe["tema"].shift(1)) # Guard: tema is falling - & (dataframe["volume"] > 0) # Make sure Volume is not 0 - ), - "enter_short", - ] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ - Based on TA indicators, populates the exit signal for the given dataframe + 基于x.py策略的卖出信号逻辑 + 实现MA60/MA120趋势跟踪策略的卖出条件 :param dataframe: DataFrame :param metadata: Additional information, like the currently traded pair :return: DataFrame with exit columns populated """ + # 多头卖出信号 - 基于x.py的sell_signal逻辑 + # dataframe.loc[ + # ( + # # 价格跌破MA60且跌幅超过10% + # (~dataframe['price_above_ma60']) & + # ((dataframe['ma60'] - dataframe['close']) / dataframe['ma60'] > 0.1) & + # dataframe['ma60'].notna() & + # # 确保成交量不为0 + # (dataframe["volume"] > 0) + # ), + # "exit_long", + # ] = 1 + dataframe.loc[ ( - # Signal: RSI crosses above 70 - (qtpylib.crossed_above(dataframe["rsi"], self.sell_rsi.value)) - & (dataframe["tema"] > dataframe["bb_middleband"]) # Guard: tema above BB middle - & (dataframe["tema"] < dataframe["tema"].shift(1)) # Guard: tema is falling - & (dataframe["volume"] > 0) # Make sure Volume is not 0 + # 价格跌破MA60且跌幅超过10% + (dataframe['close'] < dataframe['ma120']) & + dataframe['ma120'].notna() & + # 确保成交量不为0 + (dataframe["volume"] > 0) ), "exit_long", ] = 1 - dataframe.loc[ - ( - # Signal: RSI crosses above 30 - (qtpylib.crossed_above(dataframe["rsi"], self.exit_short_rsi.value)) - & - # Guard: tema below BB middle - (dataframe["tema"] <= dataframe["bb_middleband"]) - & (dataframe["tema"] > dataframe["tema"].shift(1)) # Guard: tema is raising - & (dataframe["volume"] > 0) # Make sure Volume is not 0 - ), - "exit_short", - ] = 1 return dataframe