One-Minute Long Scalping with Stochastic and Oversold Filters
Summary
This one-minute long-only scalping strategy seeks frequent entries for small profits. It looks for price opening below a short EMA of lows, ADX above 30, MFI below 30, CCI below -150, and a bullish crossover of the fast stochastic lines while both are below 30. Exit conditions combine price reaching the short EMA of highs or a stochastic move above 70 with CCI above 150. The configured minimum return target is 1%, while the source sets a wide 50% stop loss.
The document recommends many simultaneous trades to offset unavoidable losses, but gives no evidence that this improves results and does not specify position sizing or portfolio constraints. It provides code and indicator thresholds rather than backtest data or performance statistics. Frequent trading can be sensitive to fees, slippage, and liquidity, and the wide stop relative to the small stated target creates a risk profile that needs careful evaluation. The strategy is long-only, so it does not define short entries.
Key ideas
- The strategy seeks frequent long scalps on a one-minute timeframe.
- Entries combine oversold stochastic, MFI, and CCI readings with an ADX threshold.
- Exits require a price or stochastic condition together with high CCI.
- The configured target is 1%, while the stop-loss setting is -50%.
- The claim that many parallel trades cover losses is unsupported by performance evidence.
Tags
Full text
# SmoothScalp
# SmoothScalp
this strategy is based around the idea of generating a lot of potentatils buys and make tiny profits on each trade
we recommend to have at least 60 parallel trades at any time to cover non avoidable losses
## Source (GPL-3.0)
```python
# --- Do not remove these libs ---
from freqtrade.strategy import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame
# --------------------------------
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from typing import Dict, List
from functools import reduce
from pandas import DataFrame, DatetimeIndex, merge
# --------------------------------
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy # noqa
class SmoothScalp(IStrategy):
"""
this strategy is based around the idea of generating a lot of potentatils buys and make tiny profits on each trade
we recommend to have at least 60 parallel trades at any time to cover non avoidable losses
"""
INTERFACE_VERSION: int = 3
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi"
minimal_roi = {
"0": 0.01
}
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
# should not be below 3% loss
stoploss = -0.5
# Optimal timeframe for the strategy
# the shorter the better
timeframe = '1m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['ema_high'] = ta.EMA(dataframe, timeperiod=5, price='high')
dataframe['ema_close'] = ta.EMA(dataframe, timeperiod=5, price='close')
dataframe['ema_low'] = ta.EMA(dataframe, timeperiod=5, price='low')
stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
dataframe['adx'] = ta.ADX(dataframe)
dataframe['cci'] = ta.CCI(dataframe, timeperiod=20)
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
dataframe['mfi'] = ta.MFI(dataframe)
# required for graphing
bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['bb_middleband'] = bollinger['mid']
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
dataframe['cci'] = ta.CCI(dataframe)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(
(dataframe['open'] < dataframe['ema_low']) &
(dataframe['adx'] > 30) &
(dataframe['mfi'] < 30) &
(
(dataframe['fastk'] < 30) &
(dataframe['fastd'] < 30) &
(qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd']))
) &
(dataframe['cci'] < -150)
)
),
'enter_long'] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(
(
(dataframe['open'] >= dataframe['ema_high'])
) |
(
(qtpylib.crossed_above(dataframe['fastk'], 70)) |
(qtpylib.crossed_above(dataframe['fastd'], 70))
)
) & (dataframe['cci'] > 150)
)
,
'exit_long'] = 1
return dataframe
```Shown in full with attribution under the source's licence. Licence: GPL-3.0
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.