One-Minute Crypto Scalping with Oversold Indicator Filters
Summary
This long-only scalping strategy is designed to seek frequent small gains on a one-minute chart. It identifies entries when price opens below a short moving average of lows, ADX is elevated, and money flow and stochastic readings are low; it additionally requires the fast stochastic lines to cross upward and CCI to be deeply negative. The combination aims to find a strongly active market experiencing short-term downside exhaustion.
Exits require CCI to be positive and either the open to reach the short moving average of highs or a stochastic line to cross above its upper threshold. The code sets a small minimum return target and a wide stop loss, while its comments recommend many concurrent positions to offset losses. These are parameter choices and author guidance, not validated findings: no backtest, market, or results are provided. Frequent trading, many simultaneous positions, and the very wide stop can create substantial exposure to costs and drawdowns.
Key ideas
- The strategy seeks frequent small long trades on a one-minute timeframe.
- Entry conditions combine elevated ADX with low money flow, stochastic, and CCI readings.
- A stochastic upward cross is required alongside the oversold filters.
- Exits combine positive CCI with a moving-average or stochastic trigger.
- The code includes a wide stop and recommends many parallel positions, but supplies no performance evidence.
Tags
Full text
# SmoothScalp.py
```py
# --- 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.