# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # isort: skip_file # --- Do not remove these imports --- import numpy as np import time import pandas as pd from datetime import datetime, timedelta, timezone from pandas import DataFrame from typing import Dict, Optional, Union, Tuple import requests import os import logging from freqtrade.strategy import ( IStrategy, Trade, Order, PairLocks, informative, # @informative decorator # Hyperopt Parameters BooleanParameter, CategoricalParameter, DecimalParameter, IntParameter, RealParameter, # timeframe helpers timeframe_to_minutes, timeframe_to_next_date, timeframe_to_prev_date, # Strategy helper functions merge_informative_pair, stoploss_from_absolute, stoploss_from_open, ) # -------------------------------- # Add your lib to import here import talib.abstract as ta import pandas_ta as pta import requests from technical import qtpylib from collections import deque def measure_time(func): def wrapper(*args, **kwargs): start_time = time.time() result = func(*args, **kwargs) end_time = time.time() execution_time = end_time - start_time logging.debug(f"{func.__name__} took {execution_time:.4f} seconds to execute") return result return wrapper class TimeBasedDeque: def __init__(self, max_age=3600): self.max_age = max_age self.queue = deque() def add(self, item): """Insert a new item with the current timestamp.""" timestamp = time.time() self.queue.append((timestamp, item)) self.cleanup() def length(self): return len(self.queue) def cleanup(self): now = time.time() while self.queue and (now - self.queue[0][0] > self.max_age): self.queue.popleft() def get_items_last_x_seconds(self, seconds): """Retrieve items from the last X seconds.""" threshold = time.time() - (seconds) return [item for t, item in self.queue if t >= threshold] bias_endpoint = os.getenv("BIAS_ENDPOINT", "") class AnandaStrategySplit(IStrategy): """ This is a strategy template to get you started. More information in https://www.freqtrade.io/en/latest/strategy-customization/ You can: :return: a Dataframe with all mandatory indicators for the strategies - Rename the class name (Do not forget to update class_name) - Add any methods you want to build your strategy - Add any lib you need to build your strategy You must keep: - the lib in the section "Do not remove these libs" - the methods: populate_indicators, populate_entry_trend, populate_exit_trend You should keep: - timeframe, minimal_roi, stoploss, trailing_* """ api_profit = "" bot_username = None bot_password = None # Strategy interface version - allow new iterations of the strategy interface. # Check the documentation or the Sample strategy to get the latest version. INTERFACE_VERSION = 3 # Optimal timeframe for the strategy. timeframe = "1m" # Can this strategy go short? can_short: bool = True # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi". minimal_roi = { "60": 0.01, "30": 0.02, "0": 0.04 } # Optimal stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". stoploss = -0.10 # Trailing stoploss trailing_stop = False # trailing_only_offset_is_reached = False # trailing_stop_positive = 0.01 # trailing_stop_positive_offset = 0.0 # Disabled / not configured # Run "populate_indicators()" only for new candle. process_only_new_candles = True # These values can be overridden in the config. use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 30 # Strategy parameters buy_rsi = IntParameter(10, 40, default=30, space="buy") sell_rsi = IntParameter(60, 90, default=70, space="sell") return_on_invest = 0.08 @measure_time def get_config(self): response = requests.get(f"{bias_endpoint}/configs") response.raise_for_status() config_json = response.json() config_dict = {item['name']: item['value'] for item in config_json} return config_dict @measure_time def get_bias(self, pair): market_bias = {} reason = "" try: response = requests.get(f"{bias_endpoint}/sentiment/{pair}") response.raise_for_status() market_bias = response.json() reason = market_bias.get("final", {}).get("reason", "") market_bias = market_bias.get("final", {}).get("bias", "neutral") except Exception as e: s = f"Error getting market bias: {e}" logging.error(s) market_bias = "neutral" raise e finally: return market_bias, reason def set_sentiment(self, pair, sentiment): try: response = requests.post(f"{bias_endpoint}/sentiment/{pair}", json={"bias": sentiment}) response.raise_for_status() except Exception as e: logging.error(f"Error setting sentiment: {e}") def get_leverage(self, pair, proposed_leverage): leverage = 5 # proposed_leverage try: response = requests.get(f"{bias_endpoint}/leverage?pair={pair}") response.raise_for_status() leverage = response.json().get("leverage", proposed_leverage) except Exception as e: logging.error(f"Error getting leverage: {e}") raise e finally: return leverage def informative_pairs(self): """ Define additional, informative pair/interval combinations to be cached from the exchange. These pair/interval combinations are non-tradeable, unless they are part of the whitelist as well. For more information, please consult the documentation :return: List of tuples in the format (pair, interval) Sample: return [("ETH/USDT", "5m"), ("BTC/USDT", "15m"), ] """ return [] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Adds several different TA indicators to the given DataFrame Performance Note: For the best performance be frugal on the number of indicators you are using. Let uncomment only the indicator you are using in your strategies or your hyperopt configuration, otherwise you will waste your memory and CPU usage. :param dataframe: Dataframe with data from the exchange :param metadata: Additional information, like the currently traded pair :return: a Dataframe with all mandatory indicators for the strategies """ logging.warn("Ignore populate indicators") 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.drop(dataframe.index, inplace=True) symbol = metadata['pair'].split("/")[0] logging.warn(f"Populate entry with new api for {symbol}") market_bias, reason = self.get_bias(symbol) logging.info(f"Market bias is {market_bias} for {symbol}, reason: {reason}") if market_bias == "neutral": logging.info(f"Market bias is {market_bias} for {symbol}, skipping order.") dataframe.loc[:, ['enter_long', 'enter_tag']] = (0, reason) dataframe.loc[:, ['enter_short', 'enter_tag']] = (0, reason) elif market_bias == "long": dataframe.loc[:, ['enter_long', 'enter_tag']] = (1, reason) s = f"Market bias is {market_bias} for {symbol}, going long." logging.info(s) self.dp.send_msg(s) elif market_bias == "short": dataframe.loc[:, ['enter_short', 'enter_tag']] = (1, reason) s = f"Market bias is {market_bias} for {symbol}, going short." logging.info(s) self.dp.send_msg(s) else: raise Exception(f"Market Bias unknown {market_bias}") return dataframe def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: str | None, side: str, **kwargs) -> float: """ Customize leverage for each new trade. This method is only called in futures mode. :param pair: Pair that's currently analyzed :param current_time: Current time :param current_rate: Current rate :param proposed_leverage: Proposed leverage :param max_leverage: Max leverage :param entry_tag: Entry tag :param side: Side :return: Leverage """ proposed_leverage = self.get_leverage(pair, proposed_leverage) logging.info(f"Using leverage {proposed_leverage} for {pair}") return proposed_leverage 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 """ logging.warn("Ignore exit, using roi and stoploss") return dataframe def calculate_winrate(self): winrate = 0.0 try: logging.info(f"Calculating winrate from profit API") response = requests.get(self.api_profit) response.raise_for_status() winrate = response.json().get("winrate", 0.0) except Exception as e: logging.exception(f"Error calculating winrate: {e}") return winrate def update_profit(self, symbol: str, profit: float, is_short: bool): try: response = requests.post(f"{bias_endpoint}/profit", json={"profit": profit, "symbol": symbol, "is_short": is_short}) response.raise_for_status() except Exception as e: logging.error(f"Error updating profit: {e}") finally: return True def custom_exit_check(self, symbol: str): """ Check if the sentiment is reversed :param symbol: Symbol to check :return: True if the sentiment is reversed, False otherwise """ try: response = requests.get(f"{bias_endpoint}/customexit/{symbol}") response.raise_for_status() return response.json().get("exit", False) except Exception as e: logging.error(f"Error getting reverse logic: {e}") return False def adjust_stoploss(self, config): """ Adjust the stoploss based on the current profit :return: None """ calculated_winrate = self.calculate_winrate() logging.info(f"Calculated winrate: {calculated_winrate}") winrate_high = float(config.get("WinrateHigh", 0.7)) winrate_low = float(config.get("WinrateLow", 0.3)) if calculated_winrate >= winrate_high: logging.info(f"Winrate is high, adjusting stoploss") self.stoploss = float(config.get("StoplossWinrateHigh", -0.12)) elif calculated_winrate <= winrate_low: logging.info(f"Winrate is low, adjusting stoploss") self.stoploss = float(config.get("StoplossWinrateLow", -0.18)) else: logging.info(f"Winrate is neutral, adjusting stoploss to default") self.stoploss = float(config.get("StoplossWinrateNeutral", -0.18)) return False def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs): logging.info(f"Running custom exit for {pair}") is_short = trade.is_short is_long = not trade.is_short symbol = pair.split("/")[0] logging.info(f"Current profit: {current_profit}") logging.info("updating profit") self.update_profit(symbol, current_profit, is_short) logging.info("updated profit") logging.info("Checking return on invest") config = self.get_config() self.return_on_invest = float(config.get("ReturnOnInvest", 0.08)) self.adjust_stoploss(config) if current_profit >= self.return_on_invest: message = f"current_profit={current_profit} is greater or equal {self.return_on_invest}, realizing profit " logging.info(message) self.dp.send_msg(message) return message logging.info("Checking if sentiment is reversed") if self.custom_exit_check(symbol): message = f"Sentiment reversed, exiting trade for {symbol}" logging.info(message) self.dp.send_msg(message) return True logging.info(f"current_profit={current_profit}, self.stoploss={self.stoploss} symbol={symbol}") if current_profit <= self.stoploss: message = f"current_profit={current_profit} is less than stoploss {self.stoploss}, exiting trade" logging.info(message) self.dp.send_msg(message) return True logging.info("ignoring exit") return False def bot_start(self, **kwargs) -> None: logging.info(f"Bot started...") logging.info(f"Bot started with config: {self.config}") self.api_profit = self.config['bias']['api_profit'] self.bot_username = self.config['api_server']['username'] self.bot_password = self.config['api_server']['password'] logging.info(f"Bot or bot loop started...") def bot_loop_start(self, **kwargs) -> None: self.bot_start() def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float, proposed_stake: float, min_stake: float | None, max_stake: float, leverage: float, entry_tag: str | None, side: str, **kwargs) -> float: win_rate = self.calculate_winrate() config = self.get_config() win_rate_high = float(config.get("WinrateHigh", 0.7)) win_rate_low = float(config.get("WinrateLow", 0.3)) max_stake = float(config.get("MaxStake", 1000)) min_stake = float(config.get("MinStake", 10)) default_stake = float(config.get("DefaultStake", 100)) stake_increment_step = float(config.get("StakeIncrementStep", 100)) if win_rate >= win_rate_high and self.custom_stake < max_stake: self.custom_stake += stake_increment_step logging.info(f"Winrate is high, increasing stake to {self.custom_stake}") self.dp.send_msg(f"Winrate is high, increasing stake to {self.custom_stake}") elif win_rate <= win_rate_low and self.custom_stake > min_stake: self.custom_stake -= stake_increment_step logging.info(f"Winrate is low, decreasing stake to {self.custom_stake}") self.dp.send_msg(f"Winrate is low, decreasing stake to {self.custom_stake}") elif win_rate > win_rate_low and win_rate < win_rate_high and self.custom_stake != default_stake: self.custom_stake = default_stake logging.info(f"Winrate is neutral, using default stake {self.custom_stake}") self.dp.send_msg(f"Winrate is neutral, using default stake {self.custom_stake}") self.custom_stake = min(self.custom_stake, max_stake) self.custom_stake = max(self.custom_stake, min_stake) return self.custom_stake