diff --git a/K线动能理论.txt b/K线动能理论.txt index 9a88025..20e22e1 100644 --- a/K线动能理论.txt +++ b/K线动能理论.txt @@ -5,7 +5,7 @@ MACD归零轴的两种情况，两者是或的关系，满足任意一种都是 4. K线先触碰EMA52，而MACD黄白线都未归零轴 高位空 -当MACD的黄白线原理零轴运行时，与零轴有一定的距离，形成了零轴的高危形态。随着K线出现缓慢上涨或者下跌，或者盘整，MACD的能量柱出现衰减，同时能量柱与MACD黄白线形成空间夹角，随着能量柱越来越小，夹角越来越大形成高位空。这种容易形成回调下跌，特别是导致次一级的MACD穿越零轴 +当MACD的黄白线远离零轴运行时，与零轴有一定的距离，形成了零轴的高危形态。随着K线出现缓慢上涨或者下跌，或者盘整，MACD的能量柱出现衰减，同时能量柱与MACD黄白线形成空间夹角，随着能量柱越来越小，夹角越来越大形成高位空。这种容易形成回调下跌，特别是导致次一级的MACD穿越零轴 穿越零轴的定义，需要同时满足以下条件 diff --git a/config/ChanLun_BTC_K.json b/config/ChanLun_BTC_K.json new file mode 100644 index 0000000..e1f5214 --- /dev/null +++ b/config/ChanLun_BTC_K.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": true, + "db_url": "sqlite:///tradesv3.chanlun_btc_k.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": false, + "token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y", + "chat_id": "580807463" + }, + "api_server": { + "enabled": true, + "listen_ip_address": "127.0.0.1", + "listen_port": 8815, + "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_K.py b/strategies/ChanLun_BTC_K.py new file mode 100644 index 0000000..626cb25 --- /dev/null +++ b/strategies/ChanLun_BTC_K.py @@ -0,0 +1,324 @@ +# --- Do not remove these libs --- +from statistics import median +from freqtrade.strategy import IStrategy, stoploss_from_absolute +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 ChanPY import ChanPY +# -------------------------------- +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, Order +from typing import Optional +import logging +import numpy as np +import pandas as pd +from functools import reduce +logger = logging.getLogger(__name__) +### Now you can use logger.info('asfd') to log +# freqtrade plot-dataframe --strategy ChanLun_BTC_K --datadir user_data/data/binance -c ./user_data/Chan/config/ChanLun_BTC_K.json --timerange=20250309- + +# freqtrade trade -c ./user_data/Chan/config/ChanLun_BTC_K.json --strategy ChanLun_BTC_K --strategy-path ./user_data/Chan/strategies +# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_BTC_K.json --strategy ChanLun_BTC_K --strategy-path ./user_data/Chan/strategies --timerange=20250721- +# freqtrade download-data -c ./user_data/Chan/config/ChanLun_BTC_K.json -t 1m --pairs BTC/USDT:USDT --timerange=20250405- +# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi stoploss --strategy ChanLun_BTC_K --strategy-path ./user_data/Chan/strategies -c ./user_data/Chan/config/ChanLun_BTC_K.json -e 200 --timerange=20250201-20250401 + +# sudo docker compose run --rm chan_btc backtesting -c ./user_data/Chan/config/ChanLun_BTC_K.json --strategy ChanLun_BTC_K --strategy-path ./user_data/Chan/strategies --timerange=20250101- +# sudo docker compose run --rm chan_btc download-data -c ./user_data/Chan/config/ChanLun_BTC_K.json --pairs BTC/USDT:USDT -t 1m --timerange 20240101- +# sudo docker compose run --rm chan_btc trade -c ./user_data/Chan/config/ChanLun_BTC_K.json --strategy ChanLun_BTC_K --strategy-path ./user_data/Chan/strategies + +class ChanLun_BTC_K(IStrategy): + INTERFACE_VERSION: int = 3 + + # 策略参数 + minimal_roi = { + "0": 0.05, # 5% 利润即可退出 + "30": 0.03, # 30分钟后3%利润退出 + "60": 0.02, # 1小时后2%利润退出 + "120": 0.01 # 2小时后1%利润退出 + } + + stoploss = -0.03 # 3%止损 + + # 时间框架 + timeframe = '1m' + + # 指标参数 + macd_fast = 12 + macd_slow = 26 + macd_signal = 9 + ema_short = 24 + ema_long = 52 + + # 背离检测参数 + divergence_lookback = 20 # 背离检测回看周期 + min_divergence_bars = 5 # 最小背离确认K线数 + + def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + """ + 计算技术指标 + """ + # MACD指标 + macd = ta.MACD(dataframe, fastperiod=self.macd_fast, slowperiod=self.macd_slow, signalperiod=self.macd_signal) + dataframe['macd'] = macd['macd'] + dataframe['macdsignal'] = macd['macdsignal'] + dataframe['macdhist'] = macd['macdhist'] + + # EMA均线 + dataframe['ema_24'] = ta.EMA(dataframe, timeperiod=self.ema_short) + dataframe['ema_52'] = ta.EMA(dataframe, timeperiod=self.ema_long) + + # 零轴判断 + dataframe['above_zero'] = (dataframe['macd'] > 0) & (dataframe['macdsignal'] > 0) + dataframe['below_zero'] = (dataframe['macd'] < 0) & (dataframe['macdsignal'] < 0) + dataframe['cross_zero'] = ( + (dataframe['macd'].shift(1) < 0) & (dataframe['macd'] > 0) | + (dataframe['macdsignal'].shift(1) < 0) & (dataframe['macdsignal'] > 0) + ) + + # 高位空形态检测 + # 高位空：MACD黄白线处于高位，K线缓慢上涨或横盘，能量柱衰减，形成夹角 + dataframe['high_position'] = ( + # MACD黄白线远离零轴（高位） + ((dataframe['macd'] > 50) & (dataframe['macdsignal'] > 50)) | + ((dataframe['macd'] < -50) & (dataframe['macdsignal'] < -50)) + ) + # 能量柱衰减检测 + dataframe['histogram_decreasing'] = dataframe['macdhist'] < dataframe['macdhist'].shift(1) + dataframe['histogram_increasing'] = dataframe['macdhist'] > dataframe['macdhist'].shift(1) + + # 高位空形态：高位 + 能量柱衰减 + 黄白线横盘 + dataframe['high_position_empty'] = ( + dataframe['high_position'] & + dataframe['histogram_decreasing'] & + # K线缓慢上涨或横盘（价格变化不大） + (abs(dataframe['close'] - dataframe['close'].shift(3)) / dataframe['close'].shift(3) < 0.02) & + # MACD黄白线横盘（变化不大） + (abs(dataframe['macd'] - dataframe['macd'].shift(3)) < 0.05) & + (abs(dataframe['macdsignal'] - dataframe['macdsignal'].shift(3)) < 0.05) + ) + + + # 归零轴检测 + dataframe['near_zero'] = ( + (abs(dataframe['macd']) < 0.1) & (abs(dataframe['macdsignal']) < 0.1) + ) + + # 价格与EMA52关系 + dataframe['price_above_ema52'] = dataframe['close'] > dataframe['ema_52'] + dataframe['price_below_ema52'] = dataframe['close'] < dataframe['ema_52'] + dataframe['price_near_ema52'] = abs(dataframe['close'] - dataframe['ema_52']) / dataframe['ema_52'] < 0.01 + + # 背离检测 + dataframe = self.detect_divergence(dataframe) + + # 跳空检测 + dataframe = self.detect_gaps(dataframe) + + return dataframe + + def detect_divergence(self, dataframe: DataFrame) -> DataFrame: + """ + 检测背离形态 + """ + # 顶背离检测 + dataframe['top_divergence'] = False + dataframe['bottom_divergence'] = False + + for i in range(self.divergence_lookback, len(dataframe)): + # 顶背离：价格创新高，MACD未创新高 + if (dataframe['close'].iloc[i] > dataframe['close'].iloc[i-self.divergence_lookback:i].max() and + dataframe['macd'].iloc[i] < dataframe['macd'].iloc[i-self.divergence_lookback:i].max() and + dataframe['above_zero'].iloc[i]): + dataframe.loc[dataframe.index[i], 'top_divergence'] = True + + # 底背离：价格创新低，MACD未创新低 + if (dataframe['close'].iloc[i] < dataframe['close'].iloc[i-self.divergence_lookback:i].min() and + dataframe['macd'].iloc[i] > dataframe['macd'].iloc[i-self.divergence_lookback:i].min() and + dataframe['below_zero'].iloc[i]): + dataframe.loc[dataframe.index[i], 'bottom_divergence'] = True + + return dataframe + + def detect_gaps(self, dataframe: DataFrame) -> DataFrame: + """ + 检测跳空形态 + """ + # 连续跳空检测 + dataframe['continuous_gap'] = False + dataframe['separate_gap'] = False + + for i in range(5, len(dataframe)): + # 连续跳空：能量柱连续增长 + if (dataframe['histogram_increasing'].iloc[i-2:i+1].all() and + dataframe['macdhist'].iloc[i] > 0 and + dataframe['macdhist'].iloc[i] > dataframe['macdhist'].iloc[i-1]): + dataframe.loc[dataframe.index[i], 'continuous_gap'] = True + + # 分立跳空：能量柱被反向能量柱分隔 + if (i > 10 and + dataframe['macdhist'].iloc[i] > 0 and + dataframe['macdhist'].iloc[i-5:i].min() < 0 and + dataframe['macdhist'].iloc[i] > dataframe['macdhist'].iloc[i-5:i].max()): + dataframe.loc[dataframe.index[i], 'separate_gap'] = True + + return dataframe + + def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + """ + 买入信号生成 + """ + conditions = [] + + # 条件1: 底背离确认买点 + conditions.append( + dataframe['bottom_divergence'] & + dataframe['below_zero'] & + dataframe['price_near_ema52'] + ) + + # 条件2: 单位调整周期内的连续跳空背离 + conditions.append( + dataframe['continuous_gap'] & + dataframe['below_zero'] & + dataframe['near_zero'] + ) + + # 条件3: 底部形态V字反转 + conditions.append( + dataframe['price_above_ema52'] & + dataframe['near_zero'] & + dataframe['histogram_increasing'] & + (dataframe['close'] > dataframe['close'].shift(5)) + ) + + # 条件4: 抢底原理（第三阶段背离/动能不足） + conditions.append( + dataframe['below_zero'] & + dataframe['near_zero'] & + dataframe['histogram_decreasing'] & + (dataframe['macd'] > dataframe['macd'].shift(3)) # MACD开始收敛 + ) + + # 条件5: 归零轴反弹 + conditions.append( + dataframe['near_zero'] & + dataframe['price_near_ema52'] & + dataframe['histogram_increasing'] & + (dataframe['close'] > dataframe['close'].shift(1)) + ) + + # 条件6: 零轴之下高位空形态（归零轴需求） + conditions.append( + dataframe['high_position_empty'] & + dataframe['below_zero'] + ) + + if conditions: + dataframe.loc[ + reduce(lambda x, y: x | y, conditions), + 'enter_long'] = 1 + + return dataframe + + def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + """ + 卖出信号生成 + """ + conditions = [] + + # 条件1: 顶背离确认卖点 + conditions.append( + dataframe['top_divergence'] & + dataframe['above_zero'] + ) + + # 条件2: 高位空形态 + conditions.append( + dataframe['high_position_empty'] & + dataframe['above_zero'] + ) + + # 条件3: 穿零轴下跌 + conditions.append( + dataframe['cross_zero'] & + dataframe['price_below_ema52'] & + (dataframe['macd'] < 0) + ) + + # 条件4: 能量柱隐形形态（无能量配合的上涨） + conditions.append( + dataframe['above_zero'] & + (dataframe['macdhist'] < 0) & + (dataframe['close'] > dataframe['close'].shift(1)) + ) + + # 条件5: 线段背离（价格创新高但MACD未创新高） + conditions.append( + dataframe['above_zero'] & + (dataframe['close'] > dataframe['close'].shift(10).max()) & + (dataframe['macd'] < dataframe['macd'].shift(10).max()) + ) + + if conditions: + dataframe.loc[ + reduce(lambda x, y: x | y, conditions), + 'exit_long'] = 1 + + 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: + """ + 交易确认 + """ + # 获取当前数据 + dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) + last_candle = dataframe.iloc[-1].squeeze() + + # 买入确认 + if side == 'buy': + # 确保MACD在零轴下方且有反弹迹象 + if not (last_candle['below_zero'] or last_candle['near_zero']): + return False + + # 确保价格接近EMA52 + if not last_candle['price_near_ema52']: + return False + + # 卖出确认 + elif side == 'sell': + # 确保MACD在零轴上方且有下跌迹象 + if not (last_candle['above_zero'] or last_candle['near_zero']): + return False + + return True + + def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, + current_profit: float, **kwargs) -> float: + """ + 自定义止损 + """ + dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) + last_candle = dataframe.iloc[-1].squeeze() + + # 如果出现顶背离，立即止损 + if last_candle['top_divergence']: + return -0.01 # 1%止损 + + # 如果价格跌破EMA52，止损 + if last_candle['price_below_ema52'] and current_profit < 0: + return -0.02 # 2%止损 + + # 如果MACD穿零轴向下，止损 + if last_candle['cross_zero'] and last_candle['macd'] < 0: + return -0.015 # 1.5%止损 + + return self.stoploss \ No newline at end of file diff --git a/web/app.py b/web/app.py index 2c34fd9..a602177 100644 --- a/web/app.py +++ b/web/app.py @@ -255,8 +255,8 @@ def get_a_stock_kl_data(symbol, timeframe, limit=1000, start_time=None, end_time return None def add_indicators(df): - fast = 12 - slow = 26 + fast = 24 + slow = 52 period = 9 macd = ta.MACD(df, fastperiod=fast, slowperiod=slow, signalperiod=period) @@ -312,10 +312,10 @@ def add_indicators(df): def calculate_macd(df): """计算MACD指标""" - exp1 = df['close'].ewm(span=10, adjust=False).mean() - exp2 = df['close'].ewm(span=26, adjust=False).mean() + exp1 = df['close'].ewm(span=24, adjust=False).mean() + exp2 = df['close'].ewm(span=52, adjust=False).mean() macd = exp1 - exp2 - signal = macd.ewm(span=9, adjust=False).mean() + signal = macd.ewm(span=18, adjust=False).mean() histogram = macd - signal return {