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Miku_PP_v3.py
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Miku_PP_v3.py
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# --- Do not remove these libs ---
from freqtrade.strategy import IStrategy, merge_informative_pair
from pandas import DataFrame
import talib.abstract as ta
import logging
import freqtrade.vendor.qtpylib.indicators as qtpylib
# --------------------------------
import pandas as pd
import numpy as np
import technical.indicators as ftt
from freqtrade.exchange import timeframe_to_minutes
from technical.util import resample_to_interval, resampled_merge
logger = logging.getLogger(__name__)
def pivots_points(dataframe: pd.DataFrame, timeperiod=1, levels=4) -> pd.DataFrame:
"""
Pivots Points
https://www.tradingview.com/support/solutions/43000521824-pivot-points-standard/
Formula:
Pivot = (Previous High + Previous Low + Previous Close)/3
Resistance #1 = (2 x Pivot) - Previous Low
Support #1 = (2 x Pivot) - Previous High
Resistance #2 = (Pivot - Support #1) + Resistance #1
Support #2 = Pivot - (Resistance #1 - Support #1)
Resistance #3 = (Pivot - Support #2) + Resistance #2
Support #3 = Pivot - (Resistance #2 - Support #2)
...
:param dataframe:
:param timeperiod: Period to compare (in ticker)
:param levels: Num of support/resistance desired
:return: dataframe
"""
data = {}
low = qtpylib.rolling_mean(
series=pd.Series(index=dataframe.index, data=dataframe["low"]), window=timeperiod
)
high = qtpylib.rolling_mean(
series=pd.Series(index=dataframe.index, data=dataframe["high"]), window=timeperiod
)
# Pivot
data["pivot"] = qtpylib.rolling_mean(series=qtpylib.typical_price(dataframe), window=timeperiod)
# Resistance #1
# data["r1"] = (2 * data["pivot"]) - low ... Standard
# R1 = PP + 0.382 * (HIGHprev - LOWprev) ... fibonacci
data["r1"] = data['pivot'] + 0.382 * (high - low)
data["rS1"] = data['pivot'] + 0.0955 * (high - low)
# Resistance #2
# data["s1"] = (2 * data["pivot"]) - high ... Standard
# S1 = PP - 0.382 * (HIGHprev - LOWprev) ... fibonacci
data["s1"] = data["pivot"] - 0.382 * (high - low)
# Calculate Resistances and Supports >1
for i in range(2, levels + 1):
prev_support = data["s" + str(i - 1)]
prev_resistance = data["r" + str(i - 1)]
# Resitance
data["r" + str(i)] = (data["pivot"] - prev_support) + prev_resistance
# Support
data["s" + str(i)] = data["pivot"] - (prev_resistance - prev_support)
return pd.DataFrame(index=dataframe.index, data=data)
def create_ichimoku(dataframe, conversion_line_period, displacement, base_line_periods, laggin_span):
ichimoku = ftt.ichimoku(dataframe,
conversion_line_period=conversion_line_period,
base_line_periods=base_line_periods,
laggin_span=laggin_span,
displacement=displacement
)
dataframe[f'tenkan_sen_{conversion_line_period}'] = ichimoku['tenkan_sen']
dataframe[f'kijun_sen_{conversion_line_period}'] = ichimoku['kijun_sen']
dataframe[f'senkou_a_{conversion_line_period}'] = ichimoku['senkou_span_a']
dataframe[f'senkou_b_{conversion_line_period}'] = ichimoku['senkou_span_b']
class Miku_PP_v3(IStrategy):
"""
Miku_PP_v3
La base de la Estrategia es: Miku_PP_v2 y Miku_1m_5m_CSen44_1_5m
Provando en:
Miku_1m_5m_CSen444v2_N_1_5
SymphonIK
"""
# Optimal timeframe for the strategy
timeframe = '5m'
# generate signals from the 1h timeframe
informative_timeframe = '1d'
# WARNING: ichimoku is a long indicator, if you remove or use a
# shorter startup_candle_count your results will be unstable/invalid
# for up to a week from the start of your backtest or dry/live run
# (180 candles = 7.5 days)
startup_candle_count = 444 # MAXIMUM ICHIMOKU
# NOTE: this strat only uses candle information, so processing between
# new candles is a waste of resources as nothing will change
process_only_new_candles = True
minimal_roi = {
"0": 10,
}
plot_config = {
'main_plot': {
'pivot_1d': {},
'rS1_1d': {},
'r1_1d': {},
's1_1d': {},
'senkou_b_88': {},
},
'subplots': {
'MACD': {
'macd_1h': {'color': 'blue'},
'macdsignal_1h': {'color': 'orange'},
},
}
}
# WARNING setting a stoploss for this strategy doesn't make much sense, as it will buy
# back into the trend at the next available opportunity, unless the trend has ended,
# in which case it would sell anyway.
# Stoploss:
stoploss = -0.10
def informative_pairs(self):
pairs = self.dp.current_whitelist()
informative_pairs = [(pair, self.informative_timeframe)
for pair in pairs]
if self.dp:
for pair in pairs:
informative_pairs += [(pair, "1d")]
return informative_pairs
def slow_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
# dataframe "1d"
"""
dataframe1d = self.dp.get_pair_dataframe(
pair=metadata['pair'], timeframe="1d")
# Pivots Points
pp = pivots_points(dataframe1d)
dataframe1d['pivot'] = pp['pivot']
dataframe1d['r1'] = pp['r1']
dataframe1d['s1'] = pp['s1']
dataframe1d['rS1'] = pp['rS1']
# Pivots Points
dataframe = merge_informative_pair(
dataframe, dataframe1d, self.timeframe, "1d", ffill=True)
"""
# dataframe normal
"""
"""
create_ichimoku(dataframe, conversion_line_period=9,
displacement=26, base_line_periods=26, laggin_span=52)
"""
create_ichimoku(dataframe, conversion_line_period=20,
displacement=88, base_line_periods=88, laggin_span=88)
create_ichimoku(dataframe, conversion_line_period=88,
displacement=444, base_line_periods=88, laggin_span=88)
create_ichimoku(dataframe, conversion_line_period=355,
displacement=880, base_line_periods=175, laggin_span=175)
dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20)
"""
Notes: Start Trading
* En 1m
dataframe['ichimoku_ok'] = (
(dataframe['kijun_sen_355_5m'] >= dataframe['tenkan_sen_355_5m']) &
(dataframe['senkou_a_100'] > dataframe['senkou_b_100']) &
(dataframe['senkou_a_20'] > dataframe['senkou_b_20']) &
(dataframe['kijun_sen_20'] > dataframe['tenkan_sen_444']) &
(dataframe['senkou_a_9'] > dataframe['senkou_a_20']) &
(dataframe['tenkan_sen_20'] >= dataframe['kijun_sen_20']) &
(dataframe['tenkan_sen_9'] >= dataframe['tenkan_sen_20']) &
(dataframe['tenkan_sen_9'] >= dataframe['kijun_sen_9'])
).astype('int')
* En 5m
dataframe['ichimoku_ok'] = (
(dataframe['close'] > dataframe['pivot_1d']) &
(dataframe['r1_1d'] > dataframe['close']) &
(dataframe['kijun_sen_355'] >= dataframe['tenkan_sen_355']) &
(dataframe['senkou_a_20'] > dataframe['senkou_b_20']) &
(dataframe['kijun_sen_20'] > dataframe['tenkan_sen_88']) &
(dataframe['senkou_a_9'] > dataframe['senkou_a_20']) &
(dataframe['tenkan_sen_20'] >= dataframe['kijun_sen_20']) &
(dataframe['tenkan_sen_9'] >= dataframe['tenkan_sen_20']) &
(dataframe['tenkan_sen_9'] >= dataframe['kijun_sen_9'])
).astype('int')
(dataframe['pivot_1d'] > dataframe['ema20_5m']) anulo ema20_5m para ver si hace entradas en Dry Run
dataframe['trending_over'] = (
(
(dataframe['senkou_b_444'] > dataframe['close'])
)
|
(
(dataframe['pivot_1d'] > dataframe['close'])
)
).astype('int')
return dataframe
"""
# Start Trading
dataframe['pivots_ok'] = (
(dataframe['close'] > dataframe['pivot_1d']) &
(dataframe['rS1_1d'] > dataframe['close']) &
(dataframe['kijun_sen_355'] >= dataframe['tenkan_sen_355']) &
(dataframe['senkou_a_20'] > dataframe['senkou_b_20'])
).astype('int')
dataframe['trending_over'] = (
(dataframe['senkou_b_88'] > dataframe['close'])
).astype('int')
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe = self.slow_tf_indicators(dataframe, metadata)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['pivots_ok'] > 0)
), 'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['trending_over'] > 0)
), 'sell'] = 1
return dataframe