363 lines
13 KiB
Python
363 lines
13 KiB
Python
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
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# flake8: noqa: F401
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# isort: skip_file
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# --- Do not remove these imports ---
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import numpy as np
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import pandas as pd
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from datetime import datetime, timedelta, timezone
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from pandas import DataFrame
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from typing import Optional, Union
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from freqtrade.strategy import (
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IStrategy,
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Trade,
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Order,
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PairLocks,
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informative, # @informative decorator
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# Hyperopt Parameters
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BooleanParameter,
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CategoricalParameter,
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DecimalParameter,
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IntParameter,
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RealParameter,
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# timeframe helpers
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timeframe_to_minutes,
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timeframe_to_next_date,
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timeframe_to_prev_date,
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# Strategy helper functions
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merge_informative_pair,
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stoploss_from_absolute,
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stoploss_from_open,
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)
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# --------------------------------
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# Add your lib to import here
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import talib.abstract as ta
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from technical import qtpylib
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# 基于x.py策略的MA60/MA120趋势跟踪策略
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class MA60MA120TrendStrategy(IStrategy):
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"""
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基于x.py策略的MA60/MA120趋势跟踪策略
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策略逻辑：
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1. 买入条件：
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- 价格在MA60上方且距离MA60不超过10%
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- MA60呈上升趋势（过去5天中至少3天上升）
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- 成交量放大（当日成交量大于过去5日平均）
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- 前5天中下跌天数不超过2天
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- 当天收盘价大于5天前的价格
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- 价格在MA120上方
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- MA120过去5天不是下降趋势
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- RSI小于阈值（默认70）
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2. 卖出条件：
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- 价格跌破MA60且跌幅超过10%
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更多信息: https://www.freqtrade.io/en/latest/strategy-customization/
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"""
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# Strategy interface version - allow new iterations of the strategy interface.
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# Check the documentation or the Sample strategy to get the latest version.
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INTERFACE_VERSION = 3
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# Can this strategy go short?
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can_short: bool = False
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# Minimal ROI designed for the strategy.
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# 基于x.py策略，使用更保守的ROI设置
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# minimal_roi = {
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# "30": 0.3, # 20% 目标收益
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# # ""
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# }
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# Optimal stoploss designed for the strategy.
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# 基于x.py策略的卖出条件：跌破MA60且跌幅超过10%
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stoploss = -1
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# Trailing stoploss
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# trailing_stop = True
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# trailing_only_offset_is_reached = False
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# trailing_stop_positive = 0.3
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# trailing_stop_positive_offset = 0.0 # Disabled / not configured
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# Optimal timeframe for the strategy.
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# 使用日线数据，因为x.py策略基于日线
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timeframe = "1d"
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# Run "populate_indicators()" only for new candle.
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process_only_new_candles = True
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# These values can be overridden in the config.
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use_exit_signal = True
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exit_profit_only = False
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ignore_roi_if_entry_signal = False
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# 移除原有的RSI参数，因为新策略使用固定值
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# 可以添加其他可优化的参数
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ma60_period = IntParameter(low=30, high=90, default=60, space="buy", optimize=True, load=True)
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ma120_period = IntParameter(low=60, high=180, default=120, space="buy", optimize=True, load=True)
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rsi_threshold = IntParameter(low=50, high=80, default=70, space="buy", optimize=True, load=True)
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volume_surge_multiplier = DecimalParameter(low=0.5, high=2.0, default=1.0, space="buy", optimize=True, load=True)
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# Number of candles the strategy requires before producing valid signals
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# 需要更多数据来计算MA120和趋势指标
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startup_candle_count: int = 200
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# Optional order type mapping.
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order_types = {
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"entry": "limit",
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"exit": "limit",
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"stoploss": "market",
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"stoploss_on_exchange": False,
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}
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# Optional order time in force.
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order_time_in_force = {"entry": "GTC", "exit": "GTC"}
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plot_config = {
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"main_plot": {
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"ma60": {"color": "blue", "width": 2},
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"ma120": {"color": "orange", "width": 2},
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},
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"subplots": {
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"RSI": {
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"rsi": {"color": "red"},
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"rsi_threshold": {"color": "gray", "type": "line", "width": 1},
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},
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"Volume": {
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"volume": {"color": "lightblue"},
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"volume_5ma": {"color": "blue"},
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},
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"Signals": {
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"buy_signal": {"color": "green", "type": "scatter", "marker": "^"},
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"sell_signal": {"color": "red", "type": "scatter", "marker": "v"},
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},
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},
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}
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def informative_pairs(self):
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"""
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Define additional, informative pair/interval combinations to be cached from the exchange.
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These pair/interval combinations are non-tradeable, unless they are part
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of the whitelist as well.
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For more information, please consult the documentation
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:return: List of tuples in the format (pair, interval)
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Sample: return [("ETH/USDT", "5m"),
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("BTC/USDT", "15m"),
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]
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"""
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return []
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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基于x.py策略的技术指标计算
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实现MA60/MA120趋势跟踪策略的所有必要指标
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:param dataframe: Dataframe with data from the exchange
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:param metadata: Additional information, like the currently traded pair
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:return: a Dataframe with all mandatory indicators for the strategies
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"""
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# 基础移动平均线指标
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# ------------------------------------
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# MA60 - 使用可优化参数
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dataframe['ma60'] = ta.SMA(dataframe, timeperiod=60)
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# MA120 - 使用可优化参数
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dataframe['ma120'] = ta.SMA(dataframe, timeperiod=120)
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# RSI指标
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dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
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# 成交量相关指标
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# ------------------------------------
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# 成交量5日移动平均
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dataframe['volume_5ma'] = dataframe['volume'].rolling(window=5).mean()
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# 成交量放大信号：当日成交量大于过去5日平均的倍数
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dataframe['volume_surge'] = dataframe['volume'] > (dataframe['volume_5ma'] * 1)
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# 价格与MA60关系指标
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# ------------------------------------
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# 价格在MA60上方且距离MA60不超过10%
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dataframe['price_above_ma60'] = (
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(dataframe['close'] > dataframe['ma60']) &
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((dataframe['ma60'] - dataframe['close']) / dataframe['close'] < 0.1)
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)
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# 价格在MA120上方
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dataframe['price_above_ma120'] = dataframe['close'] > dataframe['ma120']
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# 前一天在MA60附近（95%-105%）
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dataframe['prev_near_ma'] = (
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(dataframe['close'].shift(1) >= dataframe['ma60'].shift(1) * 0.95) &
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(dataframe['close'].shift(1) <= dataframe['ma60'].shift(1) * 1.05)
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)
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# 前一天在MA60下方
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dataframe['prev_below_ma'] = dataframe['close'].shift(1) < dataframe['ma60'].shift(1)
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# 突破信号：前一天在MA60附近或下方，当天在MA60上方
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dataframe['breakthrough'] = (
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(dataframe['prev_below_ma'] | dataframe['prev_near_ma']) &
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dataframe['price_above_ma60']
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)
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# MA60趋势指标
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# ------------------------------------
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# 计算MA60的上升趋势（过去5天中至少3天上升）
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trend_days = 5
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ma_trend = []
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for i in range(len(dataframe)):
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if i < trend_days - 1:
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ma_trend.append(False)
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else:
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start_idx = i - trend_days + 1
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ma_values = dataframe['ma60'].iloc[start_idx:i+1].values
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if pd.isna(ma_values).any():
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ma_trend.append(False)
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else:
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# 检查是否呈上升趋势（至少3天上升）
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ma_trend.append(sum(ma_values[1:] > ma_values[:-1]) > trend_days - 3)
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dataframe['ma_uptrend'] = ma_trend
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# MA120趋势指标
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# ------------------------------------
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# 计算MA120的趋势（过去5天）不能为下降
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ma120_trend = []
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for i in range(len(dataframe)):
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if i < 4:
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ma120_trend.append(True) # 数据不足时不限制
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else:
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window = dataframe['ma120'].iloc[i-4:i+1].values
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if pd.isna(window).any():
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ma120_trend.append(True)
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else:
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# 只要有一天不是上升就不是下降趋势
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ma120_trend.append((window[1:] >= window[:-1]).all())
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dataframe['ma120_not_downtrend'] = ma120_trend
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# 价格动量指标
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# ------------------------------------
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# 阳线条件：收盘价 > 开盘价，且阳线幅度至少0.5%
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dataframe['is_bullish'] = (
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(dataframe['close'] > dataframe['open']) &
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((dataframe['close'] - dataframe['open']) / dataframe['open'] >= 0.005)
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)
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# 前5天的下跌天数不超过2天
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dataframe['is_down_day'] = dataframe['close'] < dataframe['open']
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dataframe['down_days_in_5'] = dataframe['is_down_day'].rolling(window=5, min_periods=1).sum()
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dataframe['not_too_many_downs'] = dataframe['down_days_in_5'] <= 2
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# 当天收盘价大于第五天前的价格
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dataframe['price_higher_than_5days_ago'] = dataframe['close'] > dataframe['close'].shift(5)
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# RSI条件 - 使用可优化参数
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dataframe['rsi_lt_threshold'] = dataframe['rsi'] < 70
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# 综合买入信号
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# ------------------------------------
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dataframe["buy_signal"] = (
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dataframe["price_above_ma60"] &
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dataframe["ma_uptrend"] &
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dataframe['ma60'].notna() &
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dataframe["volume_surge"] &
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dataframe["not_too_many_downs"] &
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dataframe["price_higher_than_5days_ago"] &
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dataframe['price_above_ma120'] &
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dataframe['ma120_not_downtrend']
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# &
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# dataframe['rsi_lt_threshold']
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)
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# 卖出信号：价格跌破MA60且跌幅超过10%
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dataframe['sell_signal'] = (
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(~dataframe['price_above_ma60']) &
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((dataframe['ma60'] - dataframe['close']) / dataframe['ma60'] > 0.1) &
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dataframe['ma60'].notna()
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)
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return dataframe
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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基于x.py策略的买入信号逻辑
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实现MA60/MA120趋势跟踪策略的买入条件
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:param dataframe: DataFrame
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:param metadata: Additional information, like the currently traded pair
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:return: DataFrame with entry columns populated
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"""
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# 多头买入信号 - 基于x.py的buy_signal逻辑
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dataframe.loc[
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(
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# 价格在MA60上方且距离MA60不超过10%
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dataframe["price_above_ma60"] &
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# MA60呈上升趋势（过去5天中至少3天上升）
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dataframe["ma_uptrend"] &
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# MA60数据有效
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dataframe['ma60'].notna() &
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# 成交量放大（当日成交量大于过去5日平均）
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dataframe["volume_surge"] &
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# 前5天中下跌天数不超过2天
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dataframe["not_too_many_downs"] &
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# 当天收盘价大于5天前的价格
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dataframe["price_higher_than_5days_ago"] &
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# 价格在MA120上方
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dataframe['price_above_ma120'] &
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# MA120过去5天不是下降趋势
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dataframe['ma120_not_downtrend'] &
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# RSI小于阈值
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dataframe['rsi_lt_threshold'] &
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# 确保成交量不为0
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(dataframe["volume"] > 0)
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),
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"enter_long",
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] = 1
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return dataframe
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def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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基于x.py策略的卖出信号逻辑
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实现MA60/MA120趋势跟踪策略的卖出条件
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:param dataframe: DataFrame
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:param metadata: Additional information, like the currently traded pair
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:return: DataFrame with exit columns populated
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"""
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# 多头卖出信号 - 基于x.py的sell_signal逻辑
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# dataframe.loc[
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# (
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# # 价格跌破MA60且跌幅超过10%
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# (~dataframe['price_above_ma60']) &
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# ((dataframe['ma60'] - dataframe['close']) / dataframe['ma60'] > 0.1) &
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# dataframe['ma60'].notna() &
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# # 确保成交量不为0
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# (dataframe["volume"] > 0)
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# ),
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# "exit_long",
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# ] = 1
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dataframe.loc[
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(
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# 价格跌破MA60且跌幅超过10%
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(dataframe['close'] < dataframe['ma120']) &
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dataframe['ma120'].notna() &
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# 确保成交量不为0
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(dataframe["volume"] > 0)
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),
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"exit_long",
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] = 1
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return dataframe
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