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Fix invalid syntax in SmoothOperator

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closes #105
This commit is contained in:
Matthias
2020-10-28 07:44:28 +01:00
parent 598def9553
commit 705d5b9de8
1 changed files with 25 additions and 61 deletions
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user_data/strategies/berlinguyinca/SmoothOperator.py
+25 -61
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@@ -2,7 +2,7 @@
from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame, DatetimeIndex, merge
from pandas import DataFrame
# --------------------------------
import talib.abstract as ta
@@ -11,6 +11,7 @@ import numpy # noqa
# DO NOT USE, just playing with smooting and graphs!
class SmoothOperator(IStrategy):
"""
@@ -20,8 +21,6 @@ class SmoothOperator(IStrategy):
The concept is about combining several common indicators, with a heavily smoothing, while trying to detect
a none completed peak shape.
"""
# Minimal ROI designed for the strategy.
@@ -38,13 +37,7 @@ class SmoothOperator(IStrategy):
# Optimal ticker interval for the strategy
ticker_interval = '5m'
# resample factor to establish our general trend. Basically don't buy if a trend is not given
resample_factor = 12
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# resampled dataframe to establish if we are in an uptrend, downtrend or sideways trend
dataframe = StrategyHelper.resample(dataframe, self.ticker_interval, self.resample_factor)
##################################################################################
# required for entry and exit
# CCI
@@ -135,9 +128,9 @@ class SmoothOperator(IStrategy):
# |
(
# simple v bottom shape (lopsided to the left to increase reactivity)
# which has to be below a very slow average
# this pattern only catches a few, but normally very good buy points
# simple v bottom shape (lopsided to the left to increase reactivity)
# which has to be below a very slow average
# this pattern only catches a few, but normally very good buy points
(
(dataframe['average'].shift(5) > dataframe['average'].shift(4))
& (dataframe['average'].shift(4) > dataframe['average'].shift(3))
@@ -172,7 +165,7 @@ class SmoothOperator(IStrategy):
&
# ensure we have an overall uptrend
(dataframe['close'] > dataframe)
(dataframe['close'] > dataframe['close'].shift())
),
'buy'] = 1
@@ -183,32 +176,27 @@ class SmoothOperator(IStrategy):
dataframe.loc[
(
(
# This generates very nice sale points, and mostly sit's one stop behind
# the top of the peak
(
(dataframe['mfi_rsi_cci_smooth'] > 100)
& (dataframe['mfi_rsi_cci_smooth'].shift(1) > dataframe['mfi_rsi_cci_smooth'])
& (dataframe['mfi_rsi_cci_smooth'].shift(2) < dataframe['mfi_rsi_cci_smooth'].shift(1))
& (dataframe['mfi_rsi_cci_smooth'].shift(3) < dataframe['mfi_rsi_cci_smooth'].shift(2))
)
|
# This helps with very long, sideways trends, to get out of a market before
# it dumps
(
StrategyHelper.eight_green_candles(dataframe)
)
|
# in case of very overbought market, like some one pumping
# sell
(
(dataframe['cci'] > 200)
& (dataframe['rsi'] > 70)
)
(
(dataframe['mfi_rsi_cci_smooth'] > 100)
& (dataframe['mfi_rsi_cci_smooth'].shift(1) > dataframe['mfi_rsi_cci_smooth'])
& (dataframe['mfi_rsi_cci_smooth'].shift(2) < dataframe['mfi_rsi_cci_smooth'].shift(1))
& (dataframe['mfi_rsi_cci_smooth'].shift(3) < dataframe['mfi_rsi_cci_smooth'].shift(2))
)
|
# This helps with very long, sideways trends, to get out of a market before
# it dumps
(
StrategyHelper.eight_green_candles(dataframe)
)
|
# in case of very overbought market, like some one pumping
# sell
(
(dataframe['cci'] > 200)
& (dataframe['rsi'] > 70)
)
)
),
@@ -313,27 +301,3 @@ class StrategyHelper:
(dataframe['open'].shift(3) > dataframe['close'].shift(3)) &
(dataframe['open'].shift(4) > dataframe['close'].shift(4))
)
@staticmethod
def resample( dataframe, interval, factor):
# defines the reinforcement logic
# resampled dataframe to establish if we are in an uptrend, downtrend or sideways trend
df = dataframe.copy()
df = df.set_index(DatetimeIndex(df['date']))
ohlc_dict = {
'open': 'first',
'high': 'max',
'low': 'min',
'close': 'last'
}
df = df.resample(str(int(interval[:-1]) * factor) + 'min', plotoschow=ohlc_dict)
df['resample_sma'] = ta.SMA(df, timeperiod=25, price='close')
df = df.drop(columns=['open', 'high', 'low', 'close'])
df = df.resample(interval[:-1] + 'min')
df = df.interpolate(method='time')
df['date'] = df.index
df.index = range(len(df))
dataframe = merge(dataframe, df, on='date', how='left')
return dataframe