Add config and strategies to the repo
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jackyu66git
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# --- Do not remove these libs ---
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from freqtrade.strategy import IStrategy
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from typing import Dict, List
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from functools import reduce
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from pandas import DataFrame, pandas
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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import sys
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import os
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#sys.setrecursionlimit(1000000) #例如这里设置为一百万
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#sys.path.append(os.path.abspath("/freqtrade/user_data/Chan"))
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sys.path.append(os.path.abspath("/Users/jack/Project/freqtrade/user_data/Chan"))
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#sys.path.append(os.path.abspath("/Users/jack/Documents/GitHub/freqtrade/user_data/Chan"))
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from ChanLun import ChanLun
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# --------------------------------
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from technical.util import resample_to_interval, resampled_merge
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import talib.abstract as ta
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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from datetime import datetime, timedelta, timezone
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from freqtrade.persistence import Trade, Order
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from typing import Optional
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from ChanPY import ChanPY
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import logging
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logger = logging.getLogger(__name__)
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from openai import OpenAI
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### Now you can use logger.info('asfd') to log
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# freqtrade trade -c ./user_data/Deepseek_BTC.json --strategy Deepseek_BTC --strategy-path ./user_data/strategies
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# freqtrade backtesting -c ./user_data/Deepseek_BTC.json --strategy Deepseek_BTC --strategy-path ./user_data/strategies --timerange=20250309-
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# freqtrade download-data -c ./user_data/Deepseek_BTC.json -t 1m --pairs BTC/USDT:USDT --timerange=20240101-
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# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi stoploss --strategy Deepseek_BTC --strategy-path ./user_data/strategies -c ./user_data/Chan.json -e 200 --timerange=20250101-20250215
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# sudo docker compose run --rm chan_btc backtesting -c ./user_data/Chan.json --strategy Deepseek_BTC --strategy-path ./user_data/strategies --timerange=20250101-
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# sudo docker compose run --rm chan_btc download-data -c ./user_data/Deepseek_BTC.json --pairs SOL/USDT:USDT -t 1m --timerange 20240101-
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# sudo docker compose run --rm chan_btc trade -c ./user_data/Chan.json --strategy Deepseek_BTC --strategy-path ./user_data/strategies
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def get_ai():
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client = OpenAI(api_key="sk-d802019a175a4a34ac73c4690ce0a291", base_url="https://api.deepseek.com")
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return client
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class Deepseek_BTC(IStrategy):
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INTERFACE_VERSION: int = 3
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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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"0": 0.253,
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"120": 0.159,
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"240": 0.052,
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"360": 0
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}
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can_short = True
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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 = -0.21
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trailing_stop = False
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trailing_stop_positive = 0.015
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trailing_stop_positive_offset = 0.043
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trailing_only_offset_is_reached = False
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# Optimal timeframe for the strategy
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# timeframe = '15m'
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startup_candle_count = 100
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df_size = 0
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state_list = []
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last_time = datetime.now()
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client = get_ai()
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def get_ai_state(self, dataframe):
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# 获取账户数据、订单历史和当前订单
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current_data = {}
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# 获取账户余额信息
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current_data['balance'] = self.wallets.get_all_balances()
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# 获取交易历史
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try:
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# 尝试新的API方式获取已关闭的交易
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closed_trades = Trade.get_trades_proxy(is_open=False)
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trade_history = []
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for trade in closed_trades:
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trade_history.append({
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'pair': trade.pair,
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'open_date': str(trade.open_date),
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'close_date': str(trade.close_date),
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'open_rate': float(trade.open_rate),
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'close_rate': float(trade.close_rate),
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'stake_amount': float(trade.stake_amount),
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'amount': float(trade.amount),
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'profit_ratio': float(trade.profit_ratio) if trade.profit_ratio else 0,
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'profit_abs': float(trade.profit_abs) if trade.profit_abs else 0,
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'trade_duration': trade.close_date.timestamp() - trade.open_date.timestamp() if trade.close_date else 0,
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'is_short': trade.is_short
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})
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current_data['trade_history'] = trade_history
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except Exception as e:
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logger.error(f"获取交易历史时出错: {e}")
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current_data['trade_history'] = []
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# 获取当前正在进行的订单
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try:
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# 尝试新的API方式获取开放的交易
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open_trades = Trade.get_trades_proxy(is_open=True)
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current_trades = []
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for trade in open_trades:
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current_trades.append({
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'pair': trade.pair,
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'open_date': str(trade.open_date),
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'open_rate': float(trade.open_rate),
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'stake_amount': float(trade.stake_amount),
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'amount': float(trade.amount),
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'current_rate': float(self.dp.get_ticker(trade.pair)['close']) if self.dp else 0,
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'current_profit_ratio': float(trade.calc_profit_ratio(self.dp.get_ticker(trade.pair)['close'])) if self.dp else 0,
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'trade_duration': datetime.now(timezone.utc).timestamp() - trade.open_date.timestamp(),
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'is_short': trade.is_short,
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'open_orders': [{'order_id': order.order_id, 'order_type': order.ft_order_side} for order in trade.orders]
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})
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current_data['current_trades'] = current_trades
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except Exception as e:
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logger.error(f"获取当前订单时出错: {e}")
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current_data['current_trades'] = []
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# 获取交易所限制和状态
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if hasattr(self, 'exchange'):
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current_data['exchange_info'] = {
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'name': self.exchange.name if hasattr(self.exchange, 'name') else '',
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'trading_mode': self.config.get('trading_mode', ''),
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'stake_currency': self.config.get('stake_currency', ''),
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'dry_run': self.config.get('dry_run', True)
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}
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response = self.client.chat.completions.create(
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model="deepseek-reasoner",
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messages=[
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{"role": "system", "content": "你是缠论高手"},
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{"role": "user", "content": f"我们交易的是币安的比特币合约, 数据格式是json, 数据包括现有的持仓, 仓位历史, 账户余额, 你用缠论分析之后, 给出以下分析, 最近的一个中枢在哪里，现在的趋势是什么，现在是否是买卖点，如果是，是那一类买卖点，应该进行何种操作。交易数据: {current_data}\n图表数据: {dataframe}"},
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],
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stream=False
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)
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res = response.choices[0].message.content
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print(res)
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return res
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def get_state(self, dataframe):
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if self.df_size == 0:
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for index in range(0, len(dataframe)):
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self.state_list.append('0')
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self.df_size = len(dataframe)
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elif self.df_size < len(dataframe):
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state = self.get_ai_state(dataframe)
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self.state_list.append('0')
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self.df_size = len(dataframe)
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dataframe['state'] = self.state_list
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#print(dataframe.tail(10))
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return dataframe
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def informative_pairs(self):
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# get access to all pairs available in whitelist.
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pairs = self.dp.current_whitelist()
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# Assign tf to each pair so they can be downloaded and cached for strategy.
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informative_pairs = [(pair, '1h') for pair in pairs]
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# Optionally Add additional "static" pairs
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#informative_pairs += [("ETH/USDT:USDT", "5m"),("ETH/USDT:USDT", "15m"),]
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return informative_pairs
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe = self.get_state(dataframe)
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return dataframe
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# (1,1) = 1, (1,0) = 2, (-1,1) = 3, (-1, 0) = 4, (0,0) = 0
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe.loc[
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(
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(dataframe['state'] == "10")
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),
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['enter_long', 'enter_tag']] = (1, 'long_signal_chan')
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dataframe.loc[
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(
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(dataframe['state'] == "-10")
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),
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['enter_short', 'enter_tag']] = (1, 'short_signal_chan')
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return dataframe
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def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe.loc[
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(
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(dataframe['state'] == "99")
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),
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['exit_long', 'exit_tag']] = (1, 'long_close_signal_chan')
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dataframe.loc[
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(
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(dataframe['state'] == "99")
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),
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['exit_short', 'exit_tag']] = (1, 'short_close_signal_chan')
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return dataframe
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def leverage(self, pair: str, current_time: datetime, current_rate: float,
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proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
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**kwargs) -> float:
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return 1.0
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def get_ticker_indicator(self):
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return int(self.timeframe[:-1])
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