Detail of dry-runs

Edit (2025-10-06) : I have stopped all dry-runs. The strategy below is the one I last tested.

Disclaimers

  • I don’t endorse/promote any of the strategies that are being dry-run.
  • Don’t look at short-term performances. Wait for at least 6 months before judging the performances.
  • Do your own check and test before using any of them. Different config, different exchange, different pairlist, and even just by being a different instance of bot can give you different result compared to the dry bot.
  • Past is never future. Always do proper risk management despite any past success.

E0V1E

The original creator is E0V1E, but it has been updated since last time I downloaded the file. The version I’m using is older version, which can be seen below. I don’t hyperopt it. I’m using it as is.

from datetime import datetime, timedelta
import talib.abstract as ta
import pandas_ta as pta
from freqtrade.persistence import Trade
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
from freqtrade.strategy import DecimalParameter, IntParameter
from functools import reduce
TMP_HOLD = []
class E0V1E(IStrategy):
    minimal_roi = {
        "0": 10
    }
    timeframe = '5m'
    process_only_new_candles = True
    startup_candle_count = 120
    order_types = {
        'entry': 'market',
        'exit': 'market',
        'emergency_exit': 'market',
        'force_entry': 'market',
        'force_exit': "market",
        'stoploss': 'market',
        'stoploss_on_exchange': False,
        'stoploss_on_exchange_interval': 60,
        'stoploss_on_exchange_market_ratio': 0.99
    }
    stoploss = -0.18
    is_optimize_32 = False
    buy_rsi_fast_32 = IntParameter(20, 70, default=45, space='buy', optimize=is_optimize_32)
    buy_rsi_32 = IntParameter(15, 50, default=35, space='buy', optimize=is_optimize_32)
    buy_sma15_32 = DecimalParameter(0.900, 1, default=0.961, decimals=3, space='buy', optimize=is_optimize_32)
    buy_cti_32 = DecimalParameter(-1, 0, default=-0.58, decimals=2, space='buy', optimize=is_optimize_32)
    sell_fastx = IntParameter(50, 100, default=75, space='sell', optimize=True)
    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # buy_1 indicators
        dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15)
        dataframe['cti'] = pta.cti(dataframe["close"], length=20)
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20)
        # profit sell indicators
        stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
        dataframe['fastk'] = stoch_fast['fastk']
        return dataframe
    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        dataframe.loc[:, 'enter_tag'] = ''
        buy_1 = (
                (dataframe['rsi_slow'] < dataframe['rsi_slow'].shift(1)) &
                (dataframe['rsi_fast'] < self.buy_rsi_fast_32.value) &
                (dataframe['rsi'] > self.buy_rsi_32.value) &
                (dataframe['close'] < dataframe['sma_15'] * self.buy_sma15_32.value) &
                (dataframe['cti'] < self.buy_cti_32.value)
        )
        conditions.append(buy_1)
        dataframe.loc[buy_1, 'enter_tag'] += 'buy_1'
        if conditions:
            dataframe.loc[
                reduce(lambda x, y: x | y, conditions),
                'enter_long'] = 1
        return dataframe
    def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
                    current_profit: float, **kwargs):
        dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
        current_candle = dataframe.iloc[-1].squeeze()
        if current_time - timedelta(hours=3) > trade.open_date_utc:
            if (current_candle["fastk"] >= 70) and (current_profit >= 0):
                return "fastk_profit_sell_delay"
        if current_profit > 0:
            if current_candle["fastk"] > self.sell_fastx.value:
                return "fastk_profit_sell"
        if current_profit <= -0.1:
            # tmp hold
            if trade.id not in TMP_HOLD:
                TMP_HOLD.append(trade.id)
        for i in TMP_HOLD:
            # start recover sell it
            if trade.id == i and current_profit > -0.1:
                if current_candle["fastk"] > self.sell_fastx.value:
                    TMP_HOLD.remove(i)
                    return "fastk_loss_sell"
        return None
    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[(), ['exit_long', 'exit_tag']] = (0, 'long_out')
        return dataframe

E0V1E with TSL basically above code with addition of this extra snippet to make the exits into TSL.

The config is (I only wrote things that I change from default)

{
    "max_open_trades": 4,
    "stake_currency": "USDT",
    "stake_amount" : "unlimited",
    "available_capital": 70,
    "dry_run": true,
    "dry_run_wallet": 70,
    "pairlists": [
        {
            "method": "VolumePairList",
            "number_assets": 50,
            "sort_key": "quoteVolume",
        },
        {"method": "AgeFilter", "min_days_listed": 30},
    ],
    "trading_mode": "spot",
    "ignore_buying_expired_candle_after": 120,
    "unfilledtimeout": {
        "entry": 2,
        "exit": 1,
        "unit": "minutes"
    },
    "order_types": {
        "entry": "limit",
        "exit": "market",
        "emergency_exit": "market",
        "force_exit": "market",
        "force_entry": "market",
        "stoploss": "market",
        "stoploss_on_exchange": false,
        "stoploss_on_exchange_interval": 60
    },
    "entry_pricing": {
        "price_side": "other",
        "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": "other",
        "use_order_book": true,
        "order_book_top": 1
    },
}

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