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添加新的动能理论

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jackyu66git
2025-08-02 03:16:57 +08:00
parent f03cae91f7
commit 8f22306766
4 changed files with 413 additions and 6 deletions
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K线动能理论.txt
+1 -1
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@@ -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
config/ChanLun_BTC_K.json
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@@ -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
}
}
strategies/ChanLun_BTC_K.py
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@@ -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
web/app.py
+5 -5
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@@ -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 {