Timeframe
5m
Direction
Long Only
Stoploss
-10.0%
Trailing Stop
No
ROI
0m: 4.0%
Interface Version
3
Startup Candles
N/A
Indicators
1
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
import talib.abstract as ta
from pandas import DataFrame
from freqtrade.strategy import IStrategy
from freqtrade.strategy.parameters import CategoricalParameter
class strategy_test_v3_recursive_issue(IStrategy):
INTERFACE_VERSION = 3
# Minimal ROI designed for the strategy
minimal_roi = {"0": 0.04}
# Optimal stoploss designed for the strategy
stoploss = -0.10
# Optimal timeframe for the strategy
timeframe = "5m"
scenario = CategoricalParameter(["no_bias", "bias1", "bias2"], default="bias1", space="buy")
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 100
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# bias is introduced here
if self.scenario.value == "no_bias":
dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
else:
dataframe["rsi"] = ta.RSI(dataframe, timeperiod=50)
if self.scenario.value == "bias2":
# Has both bias1 and bias2
dataframe["rsi_lookahead"] = ta.RSI(dataframe, timeperiod=50).shift(-1)
# String columns shouldn't cause issues
dataframe["test_string_column"] = f"a{len(dataframe)}"
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
return dataframe