Timeframe
1h
Direction
Long Only
Stoploss
-25.0%
Trailing Stop
No
ROI
0m: 10.0%
Interface Version
N/A
Startup Candles
N/A
Indicators
6
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
# --- 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
class PinCatcher(IStrategy):
INTERFACE_VERSION: int = 3
# Minimal ROI designed for the strategy.
# adjust based on market conditions. We would recommend to keep it low for quick turn arounds
# This attribute will be overridden if the config file contains "minimal_roi"
minimal_roi = {
"0": 0.1
}
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.25
# Optimal timeframe for the strategy
timeframe = '1h'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macd_diff'] = dataframe['macd'] / dataframe['close'] * 100
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
dataframe['sma7'] = ta.SMA(dataframe, timeperiod=7)
dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10)
dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21)
dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)
dataframe['sma200'] = ta.SMA(dataframe, timeperiod=200)
# RSI
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=25)
dataframe['rsi100'] = ta.RSI(dataframe, timeperiod=100)
# required for graphing
bollinger = qtpylib.bollinger_bands(dataframe['close'], window=12, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['sar'] = ta.SAR(dataframe)
dataframe["diff"] = dataframe["close"] - dataframe["sar"]
dataframe["diff_bool"] = dataframe["diff"] > 0
dataframe["positive_trend_periods"] = dataframe["diff_bool"].tail(25).sum()
dataframe["down_direction"] = (dataframe["diff"].rolling(2).sum() / 2) < 0
dataframe["up_direction"] = (dataframe["diff"].rolling(2).sum() / 2) > 0
dataframe["up_far"] = (dataframe["diff"].rolling(20).min()) > 0
dataframe["sar_alpha"] = (dataframe["sar"] / dataframe["sar"].shift(1) - 1) * 100
dataframe["max_sar_alpha"] = dataframe["sar_alpha"].rolling(10).max()
dataframe["min_sar_alpha"] = dataframe["sar_alpha"].rolling(10).min()
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(
(
(dataframe['macd'] < 0) &
(dataframe['macd'] > dataframe['macd'].shift(1)) &
(dataframe['rsi100'] < 50) &
(dataframe['close'] < dataframe["sma10"]) &
(dataframe["sma100"] < dataframe["sma200"]) &
dataframe["down_direction"]
)
|
(
(dataframe["min_sar_alpha"] < -3) &
(dataframe['macd'] > dataframe['macd'].shift(1)) &
(dataframe['close'] < dataframe["sma10"]) &
(dataframe["sma100"] < dataframe["sma200"])
)
)
),
'enter_long'] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# different strategy used for sell points, due to be able to duplicate it to 100%
dataframe.loc[
(
(dataframe['macd_diff'] > 1) & (dataframe['macd'] < dataframe['macd'].shift(1)) & (dataframe["sma100"] > dataframe["sma100"].shift(1))
|
(dataframe["up_far"]) & (dataframe['macd'] < dataframe['macd'].shift(1)) & (dataframe["sma100"] > dataframe["sma100"].shift(1))
|
(
(dataframe["up_far"]) &
(dataframe["max_sar_alpha"] > 0.5) &
(dataframe["rsi100"] > 55) &
(dataframe['macd'] < dataframe['macd'].shift(1)) &
(dataframe["sma100"] > dataframe["sma100"].shift(1))
)
|
(
(dataframe["positive_trend_periods"] > 20) &
(dataframe["max_sar_alpha"] > 3) &
(dataframe['macd'] < dataframe['macd'].shift(1))
)
),
'exit_long'] = 1
return dataframe