502 lines
21 KiB
Markdown
502 lines
21 KiB
Markdown
#交易 #量化交易 #freqtrade #策略研究
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[[talib.abstract和qtpylib是什么]]
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[[freqtrade策略中的INTERFACE_VERSION = 3是什么意思？]]统一版本号
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[[freqtrade中Hyperoptable parameters 这段策略的作用是什么？和参数调优相关吗？]]是调优相关，回测中固定，实盘中不要开启实时优化。
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[[freqtrade策略中的minimal_roi机制是怎么设计的]] 动态设计，短时突破收益要求高，长时间持仓收益相对降低。
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[[freqtrade策略中的移动止损（Trailing Stop）是如何设置的？]] 很关键的设置。
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[[freqtrade策略中order_types应该怎么设置？]] 关注最后一个stoploss_on_exchange的设置。
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---
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[[freqtrade中populate_indicators()函数如何理解和使用？]]
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[[freqtrade策略中如何同时加载多时间周期的数据用以策略计算？]] 最终选择config预加载，但是对于Resample重采样的实现方式也得能看懂。初次看到的时候这种方法让我惊为天人。
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[[freqtrade实盘刚刚开始运行的时候是否可以加载相应数量的历史数据用于实时计算生成指标吗？还是得等到足够数量的蜡烛图生成后才能进行计算和交易信号生成？]] warm-up机制，会加载足够的历史行情数据用于指标的计算和当下交易信号的生成。
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[[freqtrade策略中这一句代码的语法是什么意思？]] `def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:`
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---
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### 关于多时间框架的实现方式
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[[freqtrade中根据config设置多时间周期数据预加载后，实现多时间框架趋势判断和交易策略切换选择的策略示例代码]] 这是DeepSeek生成的回答，即第一种方式。
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```python
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# 2. 多周期数据注入
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def __init__(self, config: dict) -> None:
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super().__init__(config)
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self.dp = self.dataprovider
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```
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我感觉我没太看明白，因此询问DeepSeek这句话，期待进行语法讲解。
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回答如下：[[freqtrade中self.dp.get_pair_dataframe()是一种怎样的实现方式？请详解。]]
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我还是没太看明白。
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之后把官方策略库的多时间周期策略扔给DeepSeek进行分析，并让他判断那种写法更优。回答如下：[[freqtrade中@informative装饰器和self.dp.get_pair_dataframe()实现方式的对比。（官方推荐方法为@informative装饰器）]]
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[[freqtrade中如何使用@informative装饰器对多时间框架的指标进行计算和利用]] 这里讲的明确，装饰器更优，而且给了较完整的实战代码。供参考。
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[[freqtrade @informative 的自动重命名规则是什么？]]
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![[multi_tf.py]]
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最终的结论是使用@informative装饰器更加适合。
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先从这里学起吧。
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但是我觉得之前提供的脚本代码非常整洁，通过`dataframe['market_regime']`将1h判断结果传递至5m数据（几种不同的市场环境类型，趋势、震荡、区间等），不同行情使用**独立指标组**避免信号冲突。这个更好理解。**还是要再仔细理解self.dp.get_pair_dataframe()这个DeepSeek推荐给我的实现方式。**
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无论如何，今日从确定多时间框架可以实现再策略中，到如今实现了多种表现方式，不得不说是一种突破。
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日日突破，日日新。
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学习正式如此。
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2025.7.5
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本来说今天有世界末日
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但是对于策略的学习收获颇丰
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开心，觉得写出自己第一个可通过回测的策略的时间
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就快要到了
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**一点感悟，也是一点预测，官方的每一个策略，能够放在github中都是有深意的。
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在之后各种策略的开发过程中，要多多从官方示例策略中找答案，参考借鉴在自己的代码中。**
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---
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### 关于自定义K线区间形态的识别，用以构建匹配Price Action的策略
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[[freqtrade是否可以实现基于K线形态识别的策略？学习开发路线是什么？]]
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[[freqtrade是否支持自定义K线形态的识别监测？]]
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[[基于自定义K线区间形态的识别构建趋势追踪策略是否可行？随后在此基础完善通道、区间和反转策略是否可行？]]
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---
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```python
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# 这是SampleStrategy的源代码，在此学习可以留下标注记录
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# 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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# This class is a sample. Feel free to customize it.
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class MyStrategy(IStrategy):
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"""
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This is a sample strategy to inspire you.
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More information in https://www.freqtrade.io/en/latest/strategy-customization/
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You can:
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:return: a Dataframe with all mandatory indicators for the strategies
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- Rename the class name (Do not forget to update class_name)
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- Add any methods you want to build your strategy
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- Add any lib you need to build your strategy
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You must keep:
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- the lib in the section "Do not remove these libs"
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- the methods: populate_indicators, populate_entry_trend, populate_exit_trend
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You should keep:
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- timeframe, minimal_roi, stoploss, trailing_*
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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 = True
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# Minimal ROI designed for the strategy.
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# This attribute will be overridden if the config file contains "minimal_roi".
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minimal_roi = {
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# "120": 0.0, # exit after 120 minutes at break even
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#"60": 0.05,
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#"30": 0.04,
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#"0": 0.03,
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"0": 0.10, # 10%盈利立即平仓部分仓位(20%)
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"5": 0.05, # 5分钟后盈利5%平仓部分仓位(30%)
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"10": 0.03, # 10分钟后盈利3%平仓剩余仓位(50%)
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"30": 0.01 # 30分钟后只要有1%盈利就平仓
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}
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# Optimal stoploss designed for the strategy.
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# This attribute will be overridden if the config file contains "stoploss".
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stoploss = -0.05,
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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 = 0.01
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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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timeframe = "5m"
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# Run "populate_indicators()" only for new candle.
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process_only_new_candles = True
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# 得确定一下这句话的真正含义，是只看新生成的蜡烛，不看历史数据，还是其他的意思？
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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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# Hyperoptable parameters
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buy_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True)
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sell_rsi = IntParameter(low=50, high=100, default=70, space="sell", optimize=True, load=True)
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short_rsi = IntParameter(low=51, high=100, default=70, space="sell", optimize=True, load=True)
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exit_short_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True)
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# Number of candles the strategy requires before producing valid signals
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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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"tema": {},
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"sar": {"color": "white"},
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},
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"subplots": {
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"MACD": {
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"macd": {"color": "blue"},
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"macdsignal": {"color": "orange"},
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},
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"RSI": {
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"rsi": {"color": "red"},
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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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Adds several different TA indicators to the given DataFrame
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Performance Note: For the best performance be frugal on the number of indicators
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you are using. Let uncomment only the indicator you are using in your strategies
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or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
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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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# Momentum Indicators
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# ------------------------------------
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# ADX
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#dataframe["adx"] = ta.ADX(dataframe)
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dataframe['atr'] = ta.ATR(dataframe, timeperiod=14) # 添加ATR
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# # Plus Directional Indicator / Movement
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# dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
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# dataframe['plus_di'] = ta.PLUS_DI(dataframe)
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# # Minus Directional Indicator / Movement
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# dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
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# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
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# # Aroon, Aroon Oscillator
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# aroon = ta.AROON(dataframe)
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# dataframe['aroonup'] = aroon['aroonup']
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# dataframe['aroondown'] = aroon['aroondown']
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# dataframe['aroonosc'] = ta.AROONOSC(dataframe)
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# # Awesome Oscillator
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# dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
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# # Keltner Channel
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# keltner = qtpylib.keltner_channel(dataframe)
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# dataframe["kc_upperband"] = keltner["upper"]
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# dataframe["kc_lowerband"] = keltner["lower"]
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# dataframe["kc_middleband"] = keltner["mid"]
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# dataframe["kc_percent"] = (
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# (dataframe["close"] - dataframe["kc_lowerband"]) /
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# (dataframe["kc_upperband"] - dataframe["kc_lowerband"])
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# )
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# dataframe["kc_width"] = (
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# (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) / dataframe["kc_middleband"]
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# )
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# # Ultimate Oscillator
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# dataframe['uo'] = ta.ULTOSC(dataframe)
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# # Commodity Channel Index: values [Oversold:-100, Overbought:100]
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# dataframe['cci'] = ta.CCI(dataframe)
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# RSI
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dataframe["rsi"] = ta.RSI(dataframe)
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# # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy)
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# rsi = 0.1 * (dataframe['rsi'] - 50)
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# dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
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# # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy)
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# dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
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# # Stochastic Slow
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# stoch = ta.STOCH(dataframe)
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# dataframe['slowd'] = stoch['slowd']
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# dataframe['slowk'] = stoch['slowk']
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# Stochastic Fast
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#stoch_fast = ta.STOCHF(dataframe)
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#dataframe["fastd"] = stoch_fast["fastd"]
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#dataframe["fastk"] = stoch_fast["fastk"]
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# # Stochastic RSI
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# Please read https://github.com/freqtrade/freqtrade/issues/2961 before using this.
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# STOCHRSI is NOT aligned with tradingview, which may result in non-expected results.
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# stoch_rsi = ta.STOCHRSI(dataframe)
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# dataframe['fastd_rsi'] = stoch_rsi['fastd']
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# dataframe['fastk_rsi'] = stoch_rsi['fastk']
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# MACD
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# macd = ta.MACD(dataframe)
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# dataframe["macd"] = macd["macd"]
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# dataframe["macdsignal"] = macd["macdsignal"]
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# dataframe["macdhist"] = macd["macdhist"]
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# MFI
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# dataframe["mfi"] = ta.MFI(dataframe)
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# # ROC
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# dataframe['roc'] = ta.ROC(dataframe)
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# Overlap Studies
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# ------------------------------------
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# Bollinger Bands
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
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dataframe["bb_lowerband"] = bollinger["lower"]
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dataframe["bb_middleband"] = bollinger["mid"]
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dataframe["bb_upperband"] = bollinger["upper"]
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dataframe["bb_percent"] = (dataframe["close"] - dataframe["bb_lowerband"]) / (
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dataframe["bb_upperband"] - dataframe["bb_lowerband"]
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)
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dataframe["bb_width"] = (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe[
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"bb_middleband"
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]
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# Bollinger Bands - Weighted (EMA based instead of SMA)
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# weighted_bollinger = qtpylib.weighted_bollinger_bands(
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# qtpylib.typical_price(dataframe), window=20, stds=2
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# )
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# dataframe["wbb_upperband"] = weighted_bollinger["upper"]
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# dataframe["wbb_lowerband"] = weighted_bollinger["lower"]
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# dataframe["wbb_middleband"] = weighted_bollinger["mid"]
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# dataframe["wbb_percent"] = (
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# (dataframe["close"] - dataframe["wbb_lowerband"]) /
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# (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"])
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# )
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# dataframe["wbb_width"] = (
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# (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) /
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# dataframe["wbb_middleband"]
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# )
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# # EMA - Exponential Moving Average
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# dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
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# dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
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# dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
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# dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
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# dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
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# dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
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# # SMA - Simple Moving Average
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# dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3)
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# dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
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# dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10)
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# dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21)
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# dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
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# dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)
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# Parabolic SAR
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# dataframe["sar"] = ta.SAR(dataframe)
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# TEMA - Triple Exponential Moving Average
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dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9)
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# Cycle Indicator
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# ------------------------------------
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# Hilbert Transform Indicator - SineWave
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# hilbert = ta.HT_SINE(dataframe)
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# dataframe["htsine"] = hilbert["sine"]
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# dataframe["htleadsine"] = hilbert["leadsine"]
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# Pattern Recognition - Bullish candlestick patterns
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# ------------------------------------
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# # Hammer: values [0, 100]
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# dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
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# # Inverted Hammer: values [0, 100]
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# dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
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# # Dragonfly Doji: values [0, 100]
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# dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
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# # Piercing Line: values [0, 100]
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# dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
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# # Morningstar: values [0, 100]
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# dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
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# # Three White Soldiers: values [0, 100]
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# dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
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# Pattern Recognition - Bearish candlestick patterns
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# ------------------------------------
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# # Hanging Man: values [0, 100]
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# dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
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# # Shooting Star: values [0, 100]
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# dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
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# # Gravestone Doji: values [0, 100]
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# dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
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# # 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
|
||
# ------------------------------------
|
||
# # 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]
|
||
"""
|
||
|
||
return dataframe
|
||
|
||
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||
"""
|
||
Based on TA indicators, populates the entry signal for the given dataframe
|
||
:param dataframe: DataFrame
|
||
:param metadata: Additional information, like the currently traded pair
|
||
:return: DataFrame with entry columns populated
|
||
"""
|
||
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
|
||
),
|
||
"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
|
||
:param dataframe: DataFrame
|
||
:param metadata: Additional information, like the currently traded pair
|
||
:return: DataFrame with exit columns populated
|
||
"""
|
||
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
|
||
),
|
||
"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
|
||
|
||
``` |