💬 Forum

VWAPStrategy_12

♡

remiotore/ccxt-freqtrade/strategies/VWAPStrategy_12_converted.py · ★3 · ⑂2 · first seen 2026-07-28 · repo updated 2026-01-11

Basics mode: spot timeframe: 5m interface version: 3
Settings stoploss: -0.2 has minimal roi custom stoploss
Indicators ATR EMA RSI VWAP pandas_ta talib technical
Methods custom_stoploss
15 related strategies (⧉ identical code, ≈ similar name)

Each tile is a different kind of check — from an instant code lint to full sandboxed backtests and forward tests on recent data. Not sure what a check actually proves? See the FAQ →

Source

🔒 Log in to copy 🔒 Log in to download 🔒 Raw 
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
import numpy as np
import pandas as pd
import pandas_ta as ta
import talib.abstract as talib
from technical import qtpylib
from freqtrade.strategy import IStrategy, stoploss_from_open, stoploss_from_absolute
from freqtrade.persistence import Trade
from datetime import datetime

class VWAPStrategy_12(IStrategy):
    INTERFACE_VERSION = 3
    timeframe = '5m'
    minimal_roi = {'0': 1}
    stoploss = -0.2
    use_custom_stoploss = True
    custom_info = {}

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, after_fill: bool, **kwargs) -> float:
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1].squeeze()
        if trade.id in self.custom_info:
            divided_current_profit = current_profit / 2
            if current_profit >= 0.01 and divided_current_profit > self.custom_info[trade.id]:
                self.custom_info[trade.id] = divided_current_profit
                return divided_current_profit
            else:
                return self.custom_info[trade.id]
        elif pd.notna(last_candle['ATR_stoploss']):
            self.custom_info[trade.id] = last_candle['ATR_stoploss']
            return last_candle['ATR_stoploss']
        else:
            return None

    def populate_indicators(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
        dataframe['ATR'] = ta.atr(dataframe['high'], dataframe['low'], dataframe['close'], length=150)
        dataframe['ATR_stoploss'] = dataframe['close'] - dataframe['ATR'] * 3.5
        dataframe['ema200'] = talib.EMA(dataframe, 200)
        dataframe['rsi'] = ta.rsi(dataframe['close'], length=16)
        dataframe['date_copy'] = pd.to_datetime(dataframe['date'])
        dataframe['date_copy'] = dataframe['date_copy'].dt.tz_localize(None)
        dataframe.set_index('date_copy', inplace=True)
        dataframe['VWAP'] = ta.vwap(dataframe['high'], dataframe['low'], dataframe['close'], dataframe['volume'], anchor='D', offset=None)
        sma = dataframe['close'].rolling(14).mean()
        std_dev = dataframe['close'].rolling(14).std()
        dataframe['upper_band'] = sma + std_dev * 2.0
        dataframe['lower_band'] = sma - std_dev * 2.0
        VWAP_signal = [0] * len(dataframe)
        backcandles = 15
        for row in range(backcandles, len(dataframe)):
            up_trend = 1
            down_trend = 1
            for i in range(row - backcandles, row + 1):
                if max(dataframe['open'][i], dataframe['close'][i]) >= dataframe['VWAP'][i]:
                    down_trend = 0  # Set down_trend to 0
                if min(dataframe['open'][i], dataframe['close'][i]) <= dataframe['VWAP'][i]:
                    up_trend = 0  # Set up_trend to 0
            if up_trend == 1 and down_trend == 1:
                VWAP_signal[row] = 3  # Neutral signal
            elif up_trend == 1:
                VWAP_signal[row] = 2
            elif down_trend == 1:
                VWAP_signal[row] = 1
        dataframe['VWAP_signal'] = VWAP_signal
        return dataframe

    def populate_entry_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:  # Buy when volume > 0
        # Buy when VWAP_signal is 2 (15 candles above VWAP line - indicating bullish trend)
        # Buy when rsi < 45
        # Buy when the current closing price is less than or equal to the current lower bband
        # Buy when the previous closing price was less than or equal to the lower bband
        # Make sure lower and upper bband is not the same (no momentum)
        dataframe.loc[(dataframe['volume'] > 0) & (dataframe['VWAP_signal'] == 2) & (dataframe['rsi'] < 45) & (dataframe['close'] <= dataframe['lower_band']) & (dataframe['close'].shift(1) <= dataframe['lower_band'].shift(1)) & (dataframe['lower_band'] != dataframe['upper_band']), 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:  # Sell when volume > 0
        # Sell when closing price is the same or above the upper bband
        # Sell when rsi > 55
        # Make sure lower and upper bband is not the same (no momentum)
        dataframe.loc[(dataframe['volume'] > 0) & (dataframe['close'] >= dataframe['upper_band']) & (dataframe['rsi'] > 55) & (dataframe['close'] > dataframe['ema200']) & (dataframe['lower_band'] != dataframe['upper_band']), 'exit_long'] = 1
        return dataframe