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marketTrade/notebooks/decisionManager_v2.ipynb
Mark 5f8cea85ce added notebooks
2024-03-15 20:13:43 +01:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"id": "98c84a9f",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"import pandas as pd\n",
"import datetime\n",
"import numpy as np\n",
"\n",
"from tqdm import tqdm\n",
"\n",
"from indicators_v2 import *\n",
"from signals_v2 import *\n",
"from dealManager import *\n",
"from trandeVoter import *\n",
"from riskManager import *\n",
"import pickle"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "0b710391",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>open</th>\n",
" <th>close</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2020-09-01 03:00:00+03:00</td>\n",
" <td>1.19370</td>\n",
" <td>1.19388</td>\n",
" <td>1.19391</td>\n",
" <td>1.19368</td>\n",
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" <th>1</th>\n",
" <td>2020-09-01 03:00:05+03:00</td>\n",
" <td>1.19387</td>\n",
" <td>1.19388</td>\n",
" <td>1.19391</td>\n",
" <td>1.19383</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2020-09-01 03:00:10+03:00</td>\n",
" <td>1.19387</td>\n",
" <td>1.19384</td>\n",
" <td>1.19389</td>\n",
" <td>1.19382</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2020-09-01 03:00:15+03:00</td>\n",
" <td>1.19384</td>\n",
" <td>1.19384</td>\n",
" <td>1.19386</td>\n",
" <td>1.19382</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2020-09-01 03:00:20+03:00</td>\n",
" <td>1.19387</td>\n",
" <td>1.19390</td>\n",
" <td>1.19391</td>\n",
" <td>1.19383</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>2020-09-06 21:52:55+03:00</td>\n",
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" <td>1.18382</td>\n",
" <td>1.18393</td>\n",
" <td>1.18371</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99996</th>\n",
" <td>2020-09-06 21:53:00+03:00</td>\n",
" <td>1.18382</td>\n",
" <td>1.18382</td>\n",
" <td>1.18393</td>\n",
" <td>1.18371</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99997</th>\n",
" <td>2020-09-06 21:53:05+03:00</td>\n",
" <td>1.18382</td>\n",
" <td>1.18382</td>\n",
" <td>1.18393</td>\n",
" <td>1.18371</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99998</th>\n",
" <td>2020-09-06 21:53:10+03:00</td>\n",
" <td>1.18382</td>\n",
" <td>1.18382</td>\n",
" <td>1.18393</td>\n",
" <td>1.18371</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99999</th>\n",
" <td>2020-09-06 21:53:15+03:00</td>\n",
" <td>1.18382</td>\n",
" <td>1.18382</td>\n",
" <td>1.18393</td>\n",
" <td>1.18371</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>100000 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" date open close high low\n",
"0 2020-09-01 03:00:00+03:00 1.19370 1.19388 1.19391 1.19368\n",
"1 2020-09-01 03:00:05+03:00 1.19387 1.19388 1.19391 1.19383\n",
"2 2020-09-01 03:00:10+03:00 1.19387 1.19384 1.19389 1.19382\n",
"3 2020-09-01 03:00:15+03:00 1.19384 1.19384 1.19386 1.19382\n",
"4 2020-09-01 03:00:20+03:00 1.19387 1.19390 1.19391 1.19383\n",
"... ... ... ... ... ...\n",
"99995 2020-09-06 21:52:55+03:00 1.18382 1.18382 1.18393 1.18371\n",
"99996 2020-09-06 21:53:00+03:00 1.18382 1.18382 1.18393 1.18371\n",
"99997 2020-09-06 21:53:05+03:00 1.18382 1.18382 1.18393 1.18371\n",
"99998 2020-09-06 21:53:10+03:00 1.18382 1.18382 1.18393 1.18371\n",
"99999 2020-09-06 21:53:15+03:00 1.18382 1.18382 1.18393 1.18371\n",
"\n",
"[100000 rows x 5 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_candle = pd.read_csv(\"../data/EURUSD_price_candlestick.csv\")\n",
"df_candle.rename(columns={'timestamp': 'date'}, inplace=True)\n",
"df_candle"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "98ecb978",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 1.19388\n",
"1 1.19388\n",
"2 1.19384\n",
"3 1.19384\n",
"4 1.19390\n",
" ... \n",
"99995 1.18382\n",
"99996 1.18382\n",
"99997 1.18382\n",
"99998 1.18382\n",
"99999 1.18382\n",
"Name: close, Length: 100000, dtype: float64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_candle['close']"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "5dfc2c27",
"metadata": {},
"outputs": [],
"source": [
"class decsionManager:\n",
" '''\n",
"sigAgrReq = {\n",
" 'sig_BB':{\n",
" 'className':sig_BB,\n",
" 'params':{'source':'close','target':'close'},\n",
" 'indicators':{\n",
" 'ind_BB':{\n",
" 'className':ind_BB,\n",
" 'params':{'MeanType':'SMA','window':30,'valueType':'close','kDev':2.5}\n",
" } \n",
" }\n",
" },\n",
" 'sig_BB_2':{\n",
" 'className':sig_BB,\n",
" 'params':{'source':'close','target':'close'},\n",
" 'indicators':{\n",
" 'ind_BB':{\n",
" 'className':ind_BB,\n",
" 'params':{'MeanType':'SMA','window':30,'valueType':'close','kDev':2}\n",
" } \n",
" }\n",
" }\n",
"}\n",
"\n",
"sigAgrData = {\n",
" 'sig_BB':{\n",
" 'signalData': df_candle[990:1000],\n",
" 'indicatorData' :{'ind_BB': df_candle[:1000]}\n",
" },\n",
" 'sig_BB_2':{\n",
" 'signalData': df_candle[990:1000],\n",
" 'indicatorData' :{'ind_BB': df_candle[:1000]}\n",
" } \n",
"}\n",
"\n",
"\n",
"sigAgrRetroTemplate = {\n",
" 'sig_BB':{\n",
" 'signalData': None,\n",
" 'indicatorData' :{'ind_BB': None}\n",
" },\n",
" 'sig_BB_2':{\n",
" 'signalData': None,\n",
" 'indicatorData' :{'ind_BB': None}\n",
" } \n",
"} \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" '''\n",
" \n",
" \n",
" \n",
" \n",
" def __init__(self,name, sigDict: dict):\n",
" self.RM = riskManager()\n",
" self.DM = DealManager()\n",
" self.TV = trandeVoter(name)\n",
" self.SA = signalsAgrigator(sigDict)\n",
" self.sigDict = sigDict\n",
" \n",
" \n",
" def getOnlineAns(self, signalsAns: dict, price: float) -> dict:\n",
" probabilityDecsion = self.TV.getDecisionBySignals(self.getSignalsAns(signalsAns))\n",
" RMD = self.RM.getDecision(probabilityDecision=probabilityDecsion, price=price, deals = self.DM.deals)\n",
" return RMD\n",
" \n",
" def getSignalsAns(self, signalsDataDict: dict) -> dict:\n",
" return self.SA.getAns(signalsDataDict)\n",
" \n",
" def getRightAns(self,value_1, value_2):\n",
" \n",
" ans=''\n",
" \n",
" if value_1 > value_2:\n",
" ans = 'down'\n",
" elif value_1 < value_2:\n",
" ans = 'up'\n",
" else:\n",
" ans = 'none'\n",
" \n",
" return ans\n",
" \n",
" def getRetroTrendAns(self, retroTemplateDict: dict, data: pd.DataFrame(), window: int) -> list:\n",
" \n",
" reqSig={} \n",
" ans = {\n",
" 'signalsAns':[],\n",
" 'rightAns':[]\n",
" \n",
" }\n",
" target = ''\n",
" \n",
" \n",
" for k in tqdm(range(data.shape[0]-window-1)):\n",
" for i in retroTemplateDict.keys():\n",
" reqSig[i] = {'signalData': data[k:k+window], 'indicatorData':{}}\n",
" target = self.SA.signals[i].params['target']\n",
" for j in retroTemplateDict[i]['indicatorData'].keys():\n",
" reqSig[i]['indicatorData'][j] = data[k:k+window]\n",
" \n",
" sigAns = self.getSignalsAns(reqSig)\n",
" rightAns = self.getRightAns(data[target][k], data[target][k+1])\n",
"\n",
" ans['signalsAns'].append(sigAns)\n",
" ans['rightAns'].append(rightAns)\n",
"\n",
" return ans\n",
" \n",
"\n",
" def generateMatrixProbabilityFromDict(self, dictSignals: dict) -> dict:\n",
" self.TV.createMatrixAmounts(dictSignals['signalsAns'][0].keys())\n",
" for i in range(len(dictSignals['signalsAns'])):\n",
" self.TV.setDecisionBySignals(signalDecisions = dictSignals['signalsAns'][i],\n",
" trande = dictSignals['rightAns'][i])\n",
" self.TV.generateMatrixProbability()\n",
" \n",
" def createDump(self,postfix='') -> str:\n",
" dataDict = {\n",
" 'RM':self.RM,\n",
" 'DM':self.DM,\n",
" 'TV':self.TV,\n",
" 'SA':self.SA,\n",
" 'sigDict':self.sigDict\n",
" }\n",
" fileName='data_'+postfix+'.pickle'\n",
" with open(fileName, 'wb') as f:\n",
" pickle.dump(dataDict, f)\n",
" \n",
" return os.path.abspath(fileName)\n",
" \n",
" def loadDump(self,path: str) -> None:\n",
" \n",
" with open(path, 'rb') as f:\n",
" dataDict = pickle.load(f)\n",
" \n",
" self.RM = dataDict['RM']\n",
" self.DM = dataDict['DM']\n",
" self.TV = dataDict['TV']\n",
" self.SA = dataDict['SA']\n",
" self.sigDict = dataDict['sigDict']"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "1721f22c",
"metadata": {},
"outputs": [],
"source": [
"sigAgrReq = {\n",
" 'sig_BB':{\n",
" 'className':sig_BB,\n",
" 'params':{'source':'close','target':'close'},\n",
" 'indicators':{\n",
" 'ind_BB':{\n",
" 'className':ind_BB,\n",
" 'params':{'MeanType':'SMA','window':30,'valueType':'close','kDev':2.5}\n",
" } \n",
" }\n",
" },\n",
" 'sig_BB_2':{\n",
" 'className':sig_BB,\n",
" 'params':{'source':'close','target':'close'},\n",
" 'indicators':{\n",
" 'ind_BB':{\n",
" 'className':ind_BB,\n",
" 'params':{'MeanType':'SMA','window':30,'valueType':'close','kDev':2}\n",
" } \n",
" }\n",
" }\n",
"}\n",
"\n",
"sigAgrData = {\n",
" 'sig_BB':{\n",
" 'signalData': df_candle[990:1000],\n",
" 'indicatorData' :{'ind_BB': df_candle[:1000]}\n",
" },\n",
" 'sig_BB_2':{\n",
" 'signalData': df_candle[990:1000],\n",
" 'indicatorData' :{'ind_BB': df_candle[:1000]}\n",
" } \n",
"}\n",
"\n",
"\n",
"sigAgrRetroTemplate = {\n",
" 'sig_BB':{\n",
" 'signalData': None,\n",
" 'indicatorData' :{'ind_BB': None}\n",
" },\n",
" 'sig_BB_2':{\n",
" 'signalData': None,\n",
" 'indicatorData' :{'ind_BB': None}\n",
" } \n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "355d8c01",
"metadata": {},
"outputs": [],
"source": [
"test = decsionManager('Pipa', sigAgrReq)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "d42a723a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'RM': <riskManager.riskManager at 0x7f8e4947f390>,\n",
" 'DM': <dealManager.DealManager at 0x7f8e76bda610>,\n",
" 'TV': <trandeVoter.trandeVoter at 0x7f8e973b3ed0>,\n",
" 'SA': <signals_v2.signalsAgrigator at 0x7f8e4948d410>,\n",
" 'sigDict': {'sig_BB': {'className': signals_v2.sig_BB,\n",
" 'params': {'source': 'close', 'target': 'close'},\n",
" 'indicators': {'ind_BB': {'className': indicators_v2.ind_BB,\n",
" 'params': {'MeanType': 'SMA',\n",
" 'window': 30,\n",
" 'valueType': 'close',\n",
" 'kDev': 2.5}}}},\n",
" 'sig_BB_2': {'className': signals_v2.sig_BB,\n",
" 'params': {'source': 'close', 'target': 'close'},\n",
" 'indicators': {'ind_BB': {'className': indicators_v2.ind_BB,\n",
" 'params': {'MeanType': 'SMA',\n",
" 'window': 30,\n",
" 'valueType': 'close',\n",
" 'kDev': 2}}}}}}"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test.__dict__"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "7524d678",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'name': 'Pipa',\n",
" 'trandeValuesList': ['up', 'none', 'down'],\n",
" 'matrixAmounts': None,\n",
" 'keysMatrixAmounts': None,\n",
" 'matrixProbability': None}"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test.TV.__dict__"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "b0c1db05",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'close'"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test.SA.signals['sig_BB'].params['target']"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "6bf1aaa4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'sig_BB': 'none', 'sig_BB_2': 'down'}"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test.getSignalsAns(sigAgrData)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "e9452eca",
"metadata": {},
"outputs": [],
"source": [
"#test.loadDump('C:\\\\Users\\\\Redsandy\\\\PyProj\\\\Trade\\\\MVP\\\\data_pupa.pickle')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "89931e60",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4959/4959 [00:14<00:00, 339.27it/s]\n"
]
},
{
"data": {
"text/plain": [
"{'signalsAns': [{'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'down'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'down'},\n",
" {'sig_BB': 'down', 'sig_BB_2': 'down'},\n",
" {'sig_BB': 'down', 'sig_BB_2': 'down'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'down'},\n",
" {'sig_BB': 'none', 'sig_BB_2': 'none'},\n",
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]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"uuu = test.getRetroTrendAns(sigAgrRetroTemplate,df_candle[:5000],40)\n",
"uuu"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "d281ff5b",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/parf/projects/marketTrade/MVP/trandeVoter.py:48: RuntimeWarning: invalid value encountered in scalar divide\n",
" self.matrixProbability.iloc[i]['up'] = (self.matrixAmounts.iloc[i]['up'] / rowSum)\n",
"/home/parf/projects/marketTrade/MVP/trandeVoter.py:49: RuntimeWarning: invalid value encountered in scalar divide\n",
" self.matrixProbability.iloc[i]['none'] = self.matrixAmounts.iloc[i]['none'] / rowSum\n",
"/home/parf/projects/marketTrade/MVP/trandeVoter.py:50: RuntimeWarning: invalid value encountered in scalar divide\n",
" self.matrixProbability.iloc[i]['down'] = self.matrixAmounts.iloc[i]['down'] / rowSum\n"
]
}
],
"source": [
"test.generateMatrixProbabilityFromDict(uuu)"
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},
{
"cell_type": "code",
"execution_count": 18,
"id": "59dcd288",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'name': 'Pipa',\n",
" 'trandeValuesList': ['up', 'none', 'down'],\n",
" 'matrixAmounts': up none down\n",
" sig_BB sig_BB_2 \n",
" up up 31 35 32\n",
" none 0 0 0\n",
" down 0 0 0\n",
" none up 61 51 60\n",
" none 1504 1333 1529\n",
" down 67 57 85\n",
" down up 0 0 0\n",
" none 0 0 0\n",
" down 35 41 38,\n",
" 'keysMatrixAmounts': ['sig_BB', 'sig_BB_2'],\n",
" 'matrixProbability': up none down\n",
" sig_BB sig_BB_2 \n",
" up up 0.316327 0.357143 0.326531\n",
" none NaN NaN NaN\n",
" down NaN NaN NaN\n",
" none up 0.354651 0.296512 0.348837\n",
" none 0.344480 0.305314 0.350206\n",
" down 0.320574 0.272727 0.406699\n",
" down up NaN NaN NaN\n",
" none NaN NaN NaN\n",
" down 0.307018 0.359649 0.333333}"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
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{
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"execution_count": 19,
"id": "8a241b2d",
"metadata": {},
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{
"data": {
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"{'decision': 'none'}"
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"execution_count": 19,
"metadata": {},
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}
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"source": [
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{
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"execution_count": 20,
"id": "12753f77",
"metadata": {},
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{
"data": {
"text/plain": [
"(0, 3)"
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"execution_count": 20,
"metadata": {},
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"source": [
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{
"cell_type": "code",
"execution_count": 21,
"id": "836cd460",
"metadata": {},
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{
"data": {
"text/plain": [
"'/home/parf/projects/marketTrade/MVP/data_pupa.pickle'"
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},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test.createDump('pupa')"
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{
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"execution_count": null,
"id": "83d850ac",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 22,
"id": "8457a1c2",
"metadata": {},
"outputs": [
{
"ename": "FileNotFoundError",
"evalue": "[Errno 2] No such file or directory: 'C:\\\\Users\\\\Redsandy\\\\PyProj\\\\Trade\\\\MVP\\\\data_pupa.pickle'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[22], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mC:\u001b[39;49m\u001b[38;5;130;43;01m\\\\\u001b[39;49;00m\u001b[38;5;124;43mUsers\u001b[39;49m\u001b[38;5;130;43;01m\\\\\u001b[39;49;00m\u001b[38;5;124;43mRedsandy\u001b[39;49m\u001b[38;5;130;43;01m\\\\\u001b[39;49;00m\u001b[38;5;124;43mPyProj\u001b[39;49m\u001b[38;5;130;43;01m\\\\\u001b[39;49;00m\u001b[38;5;124;43mTrade\u001b[39;49m\u001b[38;5;130;43;01m\\\\\u001b[39;49;00m\u001b[38;5;124;43mMVP\u001b[39;49m\u001b[38;5;130;43;01m\\\\\u001b[39;49;00m\u001b[38;5;124;43mdata_pupa.pickle\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m 2\u001b[0m data_new \u001b[38;5;241m=\u001b[39m pickle\u001b[38;5;241m.\u001b[39mload(f)\n\u001b[1;32m 3\u001b[0m data_new\n",
"File \u001b[0;32m~/projects/marketTrade/.venv/lib/python3.11/site-packages/IPython/core/interactiveshell.py:310\u001b[0m, in \u001b[0;36m_modified_open\u001b[0;34m(file, *args, **kwargs)\u001b[0m\n\u001b[1;32m 303\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m file \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m}:\n\u001b[1;32m 304\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 305\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIPython won\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt let you open fd=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfile\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m by default \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 306\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mas it is likely to crash IPython. If you know what you are doing, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 307\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myou can use builtins\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m open.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 308\u001b[0m )\n\u001b[0;32m--> 310\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mio_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'C:\\\\Users\\\\Redsandy\\\\PyProj\\\\Trade\\\\MVP\\\\data_pupa.pickle'"
]
}
],
"source": [
"with open('C:\\\\Users\\\\Redsandy\\\\PyProj\\\\Trade\\\\MVP\\\\data_pupa.pickle', 'rb') as f:\n",
" data_new = pickle.load(f)\n",
"data_new"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9ece2f89",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "b062fc41",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "1d7dbc1e",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "a67424f2",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "24abc829",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "44e7b018",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "84d97b40",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "0acf1850",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "e893cf88",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "cca0dc8e",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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