Timeframe
5m
Direction
Long Only
Stoploss
-10000.0%
Trailing Stop
No
ROI
0m: 10000.0%
Interface Version
2
Startup Candles
N/A
Indicators
0
freqtrade/freqtrade-strategies
author@: lenik
from mcDuck.custom_indicators import klinger_oscilator
from pandas.core.frame import DataFrame
from freqtrade.strategy import IStrategy, merge_informative_pair
from freqtrade.exchange import timeframe_to_minutes
from tabulate import tabulate
class StrategyKlingerSpikes(IStrategy):
INTERFACE_VERSION = 2
# Optimal ticker interval for the strategy.
timeframe = '5m'
informative_timeframe = '1d'
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False
# These values can be overridden in the "ask_strategy" section in the config.
use_sell_signal = True
sell_profit_only = True
ignore_roi_if_buy_signal = False
ignore_buying_expired_candle_after = 720
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 600
minimal_roi = {
"0": 100
}
# Stoploss:
stoploss = -100
# Optional order type mapping.
order_types = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
def informative_pairs(self):
pairs = self.dp.current_whitelist()
informative_pairs = [(pair, self.informative_timeframe) for pair in pairs]
return informative_pairs
def do_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
[klinger_volume_indicator, signal] = klinger_oscilator(dataframe)
dataframe["kvo"] = klinger_volume_indicator
dataframe["ks"] = signal
# print(find_peaks(klinger_volume_indicator))
dataframe1 = dataframe.shift(1)
dataframe2 = dataframe.shift(2)
dataframe.loc[(dataframe1["kvo"] > dataframe2["kvo"]) &
(dataframe1["kvo"] < dataframe["kvo"]) &
(dataframe["kvo"] < dataframe["ks"]), "peak_buy"] = True
dataframe.loc[((dataframe1["kvo"] < dataframe2["kvo"]) &
(dataframe1["kvo"] > dataframe["kvo"]) &
(dataframe["kvo"] > dataframe["ks"])), "peak_sell"] = True
print(metadata['pair'])
print(tabulate(dataframe.copy()[dataframe["peak_sell"] | dataframe["peak_buy"]], headers='keys', tablefmt='psql'))
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
if self.config['runmode'].value in ('backtest', 'hyperopt'):
assert (timeframe_to_minutes(self.timeframe) <=
5), "Backtest this strategy in 5m or 1m timeframe."
if self.timeframe == self.informative_timeframe:
dataframe = self.do_indicators(dataframe, metadata)
else:
if not self.dp:
return dataframe
informative = self.dp.get_pair_dataframe(
pair=metadata['pair'], timeframe=self.informative_timeframe)
informative = self.do_indicators(informative.copy(), metadata)
dataframe = merge_informative_pair(
dataframe, informative, self.timeframe, self.informative_timeframe, ffill=True)
skip_columns = [(s + "_" + self.informative_timeframe)
for s in ['date', 'open', 'high', 'low', 'close', 'volume']]
dataframe.rename(columns=lambda s: s.replace("_{}".format(
self.informative_timeframe), "") if (not s in skip_columns) else s, inplace=True)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
minimum_coin_price = 0.0000015
dataframe.loc[(
(dataframe["peak_buy"] == True) &
(dataframe["volume"] > 0) &
(dataframe["close"] > minimum_coin_price)
), "buy"] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[(
(dataframe["peak_sell"] == True) &
(dataframe["volume"] > 0)
), "sell"] = 1
return dataframe