63 lines
1.6 KiB
Python
63 lines
1.6 KiB
Python
from market_trade.core.decisionManager_v2 import *
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from market_trade.core.indicators_v2 import *
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from market_trade.core.signals_v2 import *
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import market_trade.data.dataloader
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sigAgrReq = {
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'sig_BB':{
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'className':sig_BB,
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'params':{'source':'close','target':'close'},
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'indicators':{
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'ind_BB':{
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'className':ind_BB,
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'params':{'MeanType':'SMA','window':30,'valueType':'close','kDev':2.5}
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}
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}
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},
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'sig_BB_2':{
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'className':sig_BB,
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'params':{'source':'close','target':'close'},
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'indicators':{
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'ind_BB':{
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'className':ind_BB,
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'params':{'MeanType':'SMA','window':30,'valueType':'close','kDev':2}
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}
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}
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}
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}
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test = decsionManager('Pipa', sigAgrReq)
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import pandas as pd
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df_candle = pd.read_csv("../../data/EURUSD_price_candlestick.csv")
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df_candle["date"] = df_candle["timestamp"]
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sigAgrRetroTemplate = {
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'sig_BB':{
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'signalData': None,
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'indicatorData' :{'ind_BB': None}
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},
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'sig_BB_2':{
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'signalData': None,
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'indicatorData' :{'ind_BB': None}
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}
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}
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retroAns = test.getRetroTrendAns(sigAgrRetroTemplate,df_candle[5000:6000].reset_index(drop=True),40)
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test.generateMatrixProbabilityFromDict(retroAns)
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sigAgrData = {
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'sig_BB':{
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'signalData': df_candle[990:1000],
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'indicatorData' :{'ind_BB': df_candle[:1000]}
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},
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'sig_BB_2':{
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'signalData': df_candle[990:1000],
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'indicatorData' :{'ind_BB': df_candle[:1000]}
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}
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}
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test.getOnlineAns(sigAgrData, 0.0) |