scalping

Strategy: Scalp · Momentum Bursts

When to use

A user asks for a scalping strategy, "fast in/out", "5m strategy", "ride the thrust", "buy when volume spikes". Single-pair, tight stops, time-stopped trades.

Honest framing

The reference backtest below was unprofitable (33% WR, −0.34% PnL, Sharpe −5.6) on SOL 5m over April 2026. The strategy executes correctly — it's not broken — it's just a losing parameter set on this window. The 0.6% target / 0.4% stop ratio needs ~41% hit rate to break even before fees, which the entry filter didn't deliver. Do not deploy as-is. Tune the entry threshold and validate before recommending to a user.

This skill exists as a structural template for high-turnover momentum entries. Real edge requires parameter search, regime filtering, or a different signal.

Backtest reference

Reference implementation

from freqtrade.strategy import IStrategy
from datetime import datetime
import pandas as pd
import talib.abstract as ta


class SolScalpMomentumStrategy(IStrategy):
    minimal_roi = {"0": 0.006}    # 0.6% profit target
    stoploss = -0.004              # 0.4% stop
    trailing_stop = False
    timeframe = "5m"
    process_only_new_candles = True
    startup_candle_count = 100
    can_short = False

    def populate_indicators(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
        # Session VWAP approximation over the last 288 bars (~24h).
        tp = (dataframe["high"] + dataframe["low"] + dataframe["close"]) / 3.0
        pv = tp * dataframe["volume"]
        dataframe["vwap"] = pv.rolling(288).sum() / dataframe["volume"].rolling(288).sum()
        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
        dataframe["vol_avg20"] = dataframe["volume"].rolling(20).mean()
        dataframe["vol_thrust"] = dataframe["volume"] / dataframe["vol_avg20"]
        return dataframe

    def populate_entry_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
        dataframe.loc[
            (dataframe["close"] > dataframe["vwap"])
            & (dataframe["rsi"] > 70)
            & (dataframe["vol_thrust"] > 2.0),
            "enter_long",
        ] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
        dataframe.loc[(dataframe["rsi"] < 50), "exit_long"] = 1
        return dataframe

    def custom_exit(self, pair: str, trade, current_time: datetime,
                    current_rate: float, current_profit: float, **kwargs):
        # Time stop at 12 minutes (~3 bars on 5m).
        elapsed = (current_time - trade.open_date_utc).total_seconds()
        if elapsed >= 12 * 60:
            return "time_stop_12m"
        return None