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add more strategies

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This commit is contained in:
jackyu66git
2025-05-09 21:10:45 +08:00
parent 65a85823b9
commit 2440e896ea
17 changed files with 887 additions and 271 deletions
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strategies/Chan_SOL_2.py
+289 -145
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@@ -5,13 +5,6 @@ from functools import reduce
from pandas import DataFrame, pandas
import freqtrade.vendor.qtpylib.indicators as qtpylib
import sys
import os
#sys.setrecursionlimit(1000000) #例如这里设置为一百万
#sys.path.append(os.path.abspath("/freqtrade/user_data/Chan"))
sys.path.append(os.path.abspath("/Users/jack/Project/freqtrade/user_data/Chan"))
#sys.path.append(os.path.abspath("/Users/jack/Documents/GitHub/freqtrade/user_data/Chan"))
from ChanLun import ChanLun
# --------------------------------
from technical.util import resample_to_interval, resampled_merge
import talib.abstract as ta
@@ -19,174 +12,325 @@ import freqtrade.vendor.qtpylib.indicators as qtpylib
from datetime import datetime, timedelta, timezone
from freqtrade.persistence import Trade, Order
from typing import Optional
from ChanPY import ChanPY
import logging
logger = logging.getLogger(__name__)
### Now you can use logger.info('asfd') to log
# freqtrade trade -c ./user_data/Chan.json --strategy Chan_SOL_2 --strategy-path ./user_data/strategies
# freqtrade backtesting -c ./user_data/Chan.json --strategy Chan_SOL_2 --strategy-path ./user_data/strategies --timerange=20250309-
# freqtrade download-data -c ./user_data/Chan.json -t 1m --pairs SOL/USDT:USDT --timerange=20240101-
# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi stoploss --strategy Chan_SOL_2 --strategy-path ./user_data/strategies -c ./user_data/Chan.json -e 200 --timerange=20250101-20250215
# freqtrade trade -c ./user_data/Chan/config/ChanLun_SOL.json --strategy ChanLun_SOL_2 --strategy-path ./user_data/Chan/strategies
# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_SOL.json --strategy ChanLun_SOL_2 --strategy-path ./user_data/Chan/strategies --timerange=20250309-
# freqtrade download-data -c ./user_data/Chan/config/ChanLun_SOL.json -t 1m --pairs SOL/USDT:USDT --timerange=20250501-
# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi stoploss --strategy ChanLun_SOL_2 --strategy-path ./user_data/strategies -c ./user_data/ChanLun_SOL.json -e 200 --timerange=20250101-20250215
# sudo docker compose run --rm chan_btc backtesting -c ./user_data/Chan.json --strategy Chan_SOL_2 --strategy-path ./user_data/strategies --timerange=20250101-
# sudo docker compose run --rm chan_btc download-data -c ./user_data/Chan.json --pairs SOL/USDT:USDT -t 1m --timerange 20240101-
# sudo docker compose run --rm chan_btc trade -c ./user_data/Chan.json --strategy Chan_SOL_2 --strategy-path ./user_data/strategies
class Chan_SOL_2(IStrategy):
class ChanLun_SOL_2(IStrategy):
INTERFACE_VERSION: int = 3
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi"
# 优化的ROI设置 - 更快速获利
minimal_roi = {
"0": 0.253,
"120": 0.159,
"240": 0.052,
"360": 0
"0": 0.012, # 立即获利1.2%
"5": 0.01, # 5分钟后获利1%
"15": 0.007, # 15分钟后获利0.7%
"30": 0.005 # 30分钟后获利0.5%
}
can_short = True
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.21
trailing_stop = False
trailing_stop_positive = 0.015
trailing_stop_positive_offset = 0.043
trailing_only_offset_is_reached = False
# Optimal timeframe for the strategy
# timeframe = '15m'
startup_candle_count = 600
time5 = 5
time15 = 15
time30 = 30
time60 = 60
time240 = 240
last_time = datetime.now()
big_size = 0
big_state = "00"
big_state_list = []
chanpy = ChanPY()
chan = ChanLun()
small_size = 0
small_state = "00"
small_state_list = []
stoploss = -0.007 # 降低止损为0.7%
# 追踪止损设置 - 更积极的追踪止损
trailing_stop = True
trailing_stop_positive = 0.003 # 0.3%
trailing_stop_positive_offset = 0.005 # 0.5%
trailing_only_offset_is_reached = True
# 时间周期
timeframe = '5m'
informative_timeframe = '1h'
startup_candle_count = 200
# 只做空头策略
only_short = True
def informative_pairs(self):
# get access to all pairs available in whitelist.
pairs = self.dp.current_whitelist()
# Assign tf to each pair so they can be downloaded and cached for strategy.
informative_pairs = [(pair, '1h') for pair in pairs]
# Optionally Add additional "static" pairs
#informative_pairs += [("ETH/USDT:USDT", "5m"),("ETH/USDT:USDT", "15m"),]
informative_pairs = [(pair, self.informative_timeframe) for pair in pairs]
return informative_pairs
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# 获取更高时间周期的数据
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe)
# 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)
self.local_print(dataframe_5)
dataframe_5['state'] = self.chan.resample_klc_list(dataframe_5)
#dataframe_15['state'] = self.chan.resample_klc_list(dataframe_15)
#dataframe_30['state'] = self.chan.resample_klc_list(dataframe_30)
dataframe_60['state'] = self.chan.resample_klc_list(dataframe_60)
#dataframe_4h['state'] = self.chan.resample_klc_list(dataframe_4h)
#dataframe_5['bsps'], dataframe_5['updown'], dataframe_5['bi_sure'] = self.chanpy.get_bsps(dataframe_5)
print("===================================================")
#print(dataframe_60['high'].rolling(window).max())
#print(dataframe_60['low'].rolling(window).min())
#for index in range(0, len(dataframe_5)):
#print(dataframe_5[dataframe_5['bi_sure'] == 1][dataframe_5['state'] == "-10"])
#print(dataframe_5[dataframe_5['bi_sure'] == 1][dataframe_5['state'] == "10"])
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)
# === 高时间周期指标 ===
# 三均线系统
informative['ema50'] = ta.EMA(informative, timeperiod=50)
informative['ema100'] = ta.EMA(informative, timeperiod=100)
informative['ema200'] = ta.SMA(informative, timeperiod=200) # 使用SMA作为长期趋势
# 趋势方向
informative['uptrend'] = (
(informative['ema50'] > informative['ema100']) &
(informative['ema100'] > informative['ema200']) &
(informative['close'] > informative['ema50'])
).astype(int)
informative['downtrend'] = (
(informative['ema50'] < informative['ema100']) &
(informative['ema100'] < informative['ema200']) &
(informative['close'] < informative['ema50'])
).astype(int)
# 强下降趋势
informative['strong_downtrend'] = (
(informative['ema50'] < informative['ema100']) &
(informative['ema100'] < informative['ema200']) &
(informative['close'] < informative['ema50']) &
(informative['ema50'].shift(3) < informative['ema50']) # 确认EMA50下降
).astype(int)
# 添加高时间周期的ADX指标
informative['adx'] = ta.ADX(informative, timeperiod=14)
# 添加高时间周期的波动率
informative['atr'] = ta.ATR(informative, timeperiod=14)
informative['atr_percent'] = (informative['atr'] / informative['close']) * 100
# 高时间周期RSI
informative['rsi'] = ta.RSI(informative, timeperiod=14)
# 将informative数据帧中的列重命名，以便在合并后区分
for col in informative.columns:
if col not in ['date', 'open', 'high', 'low', 'close', 'volume']:
informative[f"{col}_{self.informative_timeframe}"] = informative[col]
# 删除原始列，只保留重命名后的列和必要的日期、OHLCV列
for col in list(informative.columns):
if col not in ['date', 'open', 'high', 'low', 'close', 'volume'] and not col.endswith(f"_{self.informative_timeframe}"):
del informative[col]
# 打印列名以便调试
logger.info(f"Informative columns after renaming: {informative.columns.tolist()}")
# 合并数据 - 使用正确的参数
dataframe = resampled_merge(dataframe, informative, self.informative_timeframe)
# 打印合并后的列名以便调试
logger.info(f"Dataframe columns after merge: {dataframe.columns.tolist()}")
# === 主时间周期指标 ===
# 布林带
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_width'] = ((bollinger['upper'] - bollinger['lower']) / bollinger['mid'])
# 动量指标
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
dataframe['mfi'] = ta.MFI(dataframe, timeperiod=14)
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
# 均线
dataframe['ema9'] = ta.EMA(dataframe, timeperiod=9)
dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['sma200'] = ta.SMA(dataframe, timeperiod=200)
# 成交量
dataframe['volume_mean'] = dataframe['volume'].rolling(window=20).mean()
dataframe['volume_ratio'] = dataframe['volume'] / dataframe['volume_mean']
# 波动率
dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
# ADX - 趋势强度指标
dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)
# 价格突破
dataframe['upper_break'] = (
(dataframe['close'] > dataframe['bb_upperband']) &
(dataframe['close'].shift() <= dataframe['bb_upperband'].shift())
).astype(int)
dataframe['lower_break'] = (
(dataframe['close'] < dataframe['bb_lowerband']) &
(dataframe['close'].shift() >= dataframe['bb_lowerband'].shift())
).astype(int)
# 均线交叉
dataframe['ema_cross_up'] = (
(dataframe['ema9'] > dataframe['ema21']) &
(dataframe['ema9'].shift() <= dataframe['ema21'].shift())
).astype(int)
dataframe['ema_cross_down'] = (
(dataframe['ema9'] < dataframe['ema21']) &
(dataframe['ema9'].shift() >= dataframe['ema21'].shift())
).astype(int)
# 超买超卖区域
dataframe['rsi_oversold'] = (dataframe['rsi'] < 30).astype(int)
dataframe['rsi_overbought'] = (dataframe['rsi'] > 70).astype(int)
# 价格与均线的关系
dataframe['price_above_ema50'] = (dataframe['close'] > dataframe['ema50']).astype(int)
dataframe['price_below_ema50'] = (dataframe['close'] < dataframe['ema50']).astype(int)
# 趋势强度
dataframe['strong_trend'] = (dataframe['adx'] > 25).astype(int)
# 添加蜡烛图形态识别
dataframe['doji'] = ta.CDLDOJI(dataframe['open'], dataframe['high'], dataframe['low'], dataframe['close'])
dataframe['engulfing'] = ta.CDLENGULFING(dataframe['open'], dataframe['high'], dataframe['low'], dataframe['close'])
dataframe['hammer'] = ta.CDLHAMMER(dataframe['open'], dataframe['high'], dataframe['low'], dataframe['close'])
dataframe['shooting_star'] = ta.CDLSHOOTINGSTAR(dataframe['open'], dataframe['high'], dataframe['low'], dataframe['close'])
# 价格动量
dataframe['momentum'] = dataframe['close'] - dataframe['close'].shift(5)
return dataframe
def print_df(self, df):
for index in range(0, len(df)):
print(df['date'][index], df['rsi'][index], df['state'][index])
def print_resample_df(self, df, time):
for index in range(0, len(df)):
cn1 = 'resample_{}_date'.format(self.get_ticker_indicator()*time)
cn2 = 'resample_{}_rsi'.format(self.get_ticker_indicator()*time)
cn3 = 'resample_{}_state'.format(self.get_ticker_indicator()*time)
print(df[cn1][index], df[cn2][index], df[cn3][index])
def local_print(self, df):
fast = 7
slow = 14
macd = ta.MACD(df, fast=fast, slow=slow)
df['macd'] = macd['macd']
df['macdsignal'] = macd['macdsignal']
df['macdhist'] = macd['macdhist']
df['ema26'] = ta.EMA(df, timeperiod=26)
df['ema52'] = ta.EMA(df, timeperiod=52)
df['ma5'] = ta.MA(df, timeperiod=5)
df['ma10'] = ta.MA(df, timeperiod=10)
df['masub'] = df['ma5'].subtract(df['ma10'])
for index in range(0, len(df)):
print(df['date'][index], df['macdhist'][index], df['macd'][index], df['macdsignal'][index], df['masub'][index])
# (1,1) = 1, (1,0) = 2, (-1,1) = 3, (-1, 0) = 4, (0,0) = 0
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# 检查列名是否存在
downtrend_col = 'resample_60_downtrend_1h'
strong_downtrend_col = 'resample_60_strong_downtrend_1h'
adx_col = 'resample_60_adx_1h'
rsi_col = 'resample_60_rsi_1h'
dataframe.loc[
# 如果列名不存在，使用替代方案
for col, default_value in [
(downtrend_col, 0),
(strong_downtrend_col, 0),
(adx_col, 25),
(rsi_col, 50)
]:
if col not in dataframe.columns:
logger.warning(f"Column {col} not found in dataframe. Creating with default value {default_value}.")
dataframe[col] = default_value
# 禁用多头入场
dataframe['enter_long'] = 0
# 空头入场条件 - 专注于空头策略
short_conditions = (
# 高时间周期处于下降趋势
(dataframe[downtrend_col] > 0) &
# 趋势强度确认
(dataframe[adx_col] > 25) &
# 条件1: 价格突破上轨后回落 + 成交量确认
(
#(dataframe['state'].shift(1) == "-10") &
#((dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)] == "-11") |
(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.time60)].shift(self.time60) == "11") &
(dataframe['resample_{}_bsps'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) > 0) &
(dataframe['resample_{}_bsps'.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['upper_break'].rolling(window=5).sum() > 0) & # 最近5根K线内有突破上轨
(dataframe['close'] < dataframe['close'].shift(2)) & # 价格开始下跌
(dataframe['close'] < dataframe['ema9']) & # 价格在短期均线下方
(dataframe['volume_ratio'] > 1.3) & # 成交量放大
(dataframe['rsi'] < 70) & # RSI不在极度超买区
(dataframe['rsi'] > 40) & # RSI不在超卖区
(dataframe[rsi_col] < 60) # 高时间周期RSI不过高
) |
# 条件2: 均线死叉 + RSI超买回落 + 趋势确认
(
#(dataframe['state'].shift(1) == "10") &
#((dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)] == "11") |
(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.time60)].shift(self.time60) == "-11") &
(dataframe['resample_{}_bsps'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) < 0)
#(qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal']))
),
['enter_short', 'enter_tag']] = (1, 'short_signal_chan')
(dataframe['ema_cross_down'] > 0) & # 均线死叉
(dataframe['rsi'] > 55) & # RSI相对较高
(dataframe['rsi'] < dataframe['rsi'].shift(3)) & # RSI下降
(dataframe['volume_ratio'] > 1.2) & # 成交量放大
(dataframe['adx'] > 20) & # ADX显示有一定趋势强度
((dataframe['shooting_star'] > 0) | (dataframe['engulfing'] < 0)) # 流星线或看跌吞没形态
) |
# 条件3: 价格在高点回落 + 强趋势
(
(dataframe['close'] < dataframe['high'].shift()) &
(dataframe['high'].shift() > dataframe['high'].shift(2)) &
(dataframe['close'] < dataframe['ema21']) &
(dataframe['adx'] > 30) &
(dataframe['rsi'] < dataframe['rsi'].shift()) &
(dataframe['rsi'].shift() > 65) &
(dataframe['volume_ratio'] > 1.0)
) |
# 条件4: 强下降趋势确认
(
(dataframe[strong_downtrend_col] > 0) &
(dataframe['close'] < dataframe['ema21']) &
(dataframe['close'] < dataframe['close'].shift(3)) &
(dataframe['momentum'] < 0) &
(dataframe['volume_ratio'] > 1.1) &
(dataframe['adx'] > 25)
)
)
dataframe.loc[short_conditions, 'enter_short'] = 1
dataframe.loc[short_conditions, 'enter_tag'] = 'chan_sol_short'
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
# 禁用多头出场
dataframe['exit_long'] = 0
# 空头出场条件 - 更精确的出场
short_exit_conditions = (
# 条件1: 趋势反转信号
(
#(dataframe['state'].shift(1) == "10") &
(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time60)].shift(self.time60) == "10")
#(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['ema_cross_up'] > 0) & # 均线金叉
(dataframe['volume_ratio'] > 1.0) # 成交量确认
) |
# 条件2: 价格突破中期均线
(
#(dataframe['state'].shift(1) == "-10") &
(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time60)].shift(self.time60) == "-10")
#(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')
(dataframe['close'] > dataframe['ema21']) &
(dataframe['close'].shift() < dataframe['ema21'].shift()) & # 确认是刚刚突破
(dataframe['volume_ratio'] > 1.2) # 成交量确认
) |
# 条件3: 超卖信号
(
(dataframe['rsi'] < 30) & # RSI超卖
(dataframe['close'] < dataframe['bb_lowerband']) # 价格突破下轨
) |
# 条件4: 动量减弱
(
(dataframe['rsi'] < 35) &
(dataframe['rsi'] > dataframe['rsi'].shift()) &
(dataframe['rsi'].shift() > dataframe['rsi'].shift(2)) & # RSI连续两根K线上升
(dataframe['momentum'] > 0) # 价格动量转为正
) |
# 条件5: 锤子线形态 (潜在反转信号)
(
(dataframe['hammer'] > 0) &
(dataframe['volume_ratio'] > 1.3)
)
)
dataframe.loc[short_exit_conditions, 'exit_short'] = 1
dataframe.loc[short_exit_conditions, 'exit_tag'] = 'chan_sol_short_exit'
return dataframe
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
time_in_force: str, current_time: datetime, entry_tag: Optional[str],
side: str, **kwargs) -> bool:
"""
在进入交易前进行额外的确认
"""
# 只做空头交易
if side == "sell" and entry_tag == "chan_sol_short":
return True
return False
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: