Simple Scalping Strategy
Timeframe
1m
Direction
Long Only
Stoploss
-0.5%
Trailing Stop
Yes
ROI
0m: 1.0%, 5m: 0.5%, 10m: 0.3%, 15m: 0.2%
Interface Version
N/A
Startup Candles
N/A
Indicators
2
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from datetime import datetime
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
IStrategy, IntParameter)
# --------------------------------
# Add your lib to import here
import talib
import talib.abstract as ta
import pandas_ta as pta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from technical.util import resample_to_interval, resampled_merge
class ScalpingSimple(IStrategy):
"""
Simple Scalping Strategy
This strategy uses basic momentum and trend indicators for scalping
on short timeframes. It's designed to be less restrictive and generate
more trading opportunities.
Key Features:
- Uses RSI and moving averages for signals
- Quick entries and exits
- Volume confirmation
- Simple and effective approach
Expected Performance:
- Higher frequency trading
- Small profits per trade
- Good for volatile markets
"""
INTERFACE_VERSION: int = 3
# Buy hyperspace params:
buy_params = {
"rsi_period": 14,
"rsi_oversold": 30,
"rsi_overbought": 70,
"sma_fast": 5,
"sma_slow": 10,
"volume_multiplier": 1.1,
}
# ROI table - designed for scalping
minimal_roi = {
"0": 0.01, # 1% profit target
"5": 0.005, # 0.5% after 5 minutes
"10": 0.003, # 0.3% after 10 minutes
"15": 0.002, # 0.2% after 15 minutes
"30": 0.001, # 0.1% after 30 minutes
}
# Stoploss - moderate for scalping
stoploss = -0.005 # 0.5% stop loss
# Trailing stop - protect profits
trailing_stop = True
trailing_stop_positive = 0.002 # 0.2% trailing stop
trailing_stop_positive_offset = 0.003 # Start trailing after 0.3% profit
trailing_only_offset_is_reached = True
# Optimal timeframe for scalping
timeframe = '1m' # 1-minute timeframe
# Strategy parameters
rsi_period = IntParameter(10, 20, default=14, space="buy")
rsi_oversold = IntParameter(25, 40, default=30, space="buy")
rsi_overbought = IntParameter(60, 80, default=70, space="buy")
sma_fast = IntParameter(3, 8, default=5, space="buy")
sma_slow = IntParameter(8, 15, default=10, space="buy")
volume_multiplier = DecimalParameter(1.0, 1.5, default=1.1, space="buy")
# 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 = 15
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
"""
# RSI
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=self.rsi_period.value)
dataframe['rsi_oversold'] = dataframe['rsi'] < self.rsi_oversold.value
dataframe['rsi_overbought'] = dataframe['rsi'] > self.rsi_overbought.value
# Moving Averages
dataframe['sma_fast'] = ta.SMA(dataframe, timeperiod=self.sma_fast.value)
dataframe['sma_slow'] = ta.SMA(dataframe, timeperiod=self.sma_slow.value)
# MA crossover
dataframe['sma_cross_up'] = (
(dataframe['sma_fast'].shift(1) <= dataframe['sma_slow'].shift(1)) &
(dataframe['sma_fast'] > dataframe['sma_slow'])
)
dataframe['sma_cross_down'] = (
(dataframe['sma_fast'].shift(1) >= dataframe['sma_slow'].shift(1)) &
(dataframe['sma_fast'] < dataframe['sma_slow'])
)
# Volume indicators
dataframe['volume_sma'] = ta.SMA(dataframe['volume'], timeperiod=20)
dataframe['volume_high'] = dataframe['volume'] > (dataframe['volume_sma'] * self.volume_multiplier.value)
# Price momentum
dataframe['price_change'] = dataframe['close'].pct_change()
dataframe['momentum_positive'] = dataframe['price_change'] > 0
dataframe['momentum_negative'] = dataframe['price_change'] < 0
# Price above/below moving averages
dataframe['price_above_sma'] = dataframe['close'] > dataframe['sma_fast']
dataframe['price_below_sma'] = dataframe['close'] < dataframe['sma_fast']
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the entry signal for the given dataframe
"""
dataframe.loc[
(
# RSI oversold bounce
(dataframe['rsi_oversold'] == True) &
# Price above fast SMA (trending up)
(dataframe['price_above_sma'] == True) &
# Positive momentum
(dataframe['momentum_positive'] == True) &
# Volume confirmation (less restrictive)
(dataframe['volume'] > 0)
),
'enter_long'] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the exit signal for the given dataframe
"""
dataframe.loc[
(
# Exit if RSI becomes overbought
(dataframe['rsi_overbought'] == True) |
# Exit if SMA crosses down
(dataframe['sma_cross_down'] == True) |
# Exit if price falls below SMA
(dataframe['price_below_sma'] == True) &
(dataframe['momentum_negative'] == True)
),
'exit_long'] = 1
return dataframe
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
"""
Custom stoploss logic, returning the new distance relative to current_rate
"""
# Get current market conditions
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = dataframe.iloc[-1].squeeze()
# Tighter stop if RSI is overbought
if last_candle['rsi_overbought']:
return -0.003 # 0.3% stop if RSI overbought
# Tighter stop if momentum is negative
if last_candle['momentum_negative']:
return -0.003 # 0.3% stop if momentum negative
# Normal stop
return self.stoploss