How to use index_strategy method in pandera

Best Python code snippet using pandera_python

Replicate_Figure1.py

Source:Replicate_Figure1.py

1import numpy as np2import pandas as pd3import matplotlib.pyplot as plt4import strategymodels5from Functions.set_Beta_prior import set_priors6from Functions.update_strategy_posterior_probability import update_strategy_posterior_probability7from Functions.Summaries_of_Beta_distribution import summaries_of_Beta_Distribution8from Functions.plotSessionStructure import plotSessionStructure9from Functions.interpolate_null_trials import interpolate_null_trials10# initiate TestData variable so that rat 2 testdata can be loaded11TestData = pd.read_csv('data.csv')12#  choose strategies to evaluate: subset shown in Figure 113strategies = ['go_left','go_right','go_cued','win_stay_spatial','lose_shift_cued','lose_shift_spatial']14# set prior15prior_type = 'Uniform' #set prior type16decay_rate = 0.917[alpha0, beta0] = set_priors(prior_type)  # define priors18#%% initialise storage19Output_collection = {} # empty dict in which to store dataframes20event_totals = {}   # empty dict to store totals of events for each strategy21# initialise dataframes22for index_strategy in range(len(strategies)):23    Output_collection[strategies[index_strategy]] =  pd.DataFrame(columns = ['Alpha', 'Beta','MAPprobability', 'Precision','Alpha_interpolated', 'Beta_interpolated','MAPprobability_interpolated', 'Precision_interpolated'])  # empty Dataframe to input data into24    event_totals[strategies[index_strategy]] = {}; # create empty dict for this strategy25    event_totals[strategies[index_strategy]]['success_total'] = 0;26    event_totals[strategies[index_strategy]]['failure_total'] = 0;27#%%28for trial in range(len(TestData)):29    30    rows_of_data = TestData.iloc[0:trial+1]     # select all rows of data up to the curren trial; is trial+1 as dataframe includes colu   31     32   #%% 33    for index_strategy in range(len(strategies)):34       # run current strategy model on data up to current trial 35       strategy_fcn = getattr(strategymodels,strategies[index_strategy])  # dynamically assign string as function to be called36       trial_type = strategy_fcn(rows_of_data)   #call currently assigned function37     38       # update probability of strategy39       [event_totals[strategies[index_strategy]]['success_total'], event_totals[strategies[index_strategy]]['failure_total'], Alpha, Beta] \40           = update_strategy_posterior_probability(trial_type, decay_rate,event_totals[strategies[index_strategy]]['success_total'], event_totals[strategies[index_strategy]]['failure_total'],alpha0, beta0)41               42       MAPprobability = summaries_of_Beta_Distribution(Alpha, Beta, 'MAP')43       precision = summaries_of_Beta_Distribution(Alpha, Beta, 'precision')44      45              46       #%% interpolate null trials47       this_trials_data= {'Alpha':Alpha, 'Beta':Beta, 'MAPprobability':MAPprobability, 'Precision':precision}     # create dict of current data to pass48       if trial > 0:49           previous_trials_data = Output_collection[strategies[index_strategy]].iloc[trial-1] 50       else:51           previous_trials_data = Output_collection[strategies[index_strategy]]  # pass empty dataframe52           53       new_row_of_data = interpolate_null_trials(this_trials_data,previous_trials_data,alpha0,beta0)54       55       # store results  - dynamically-defined dataframe...56       new_df= pd.DataFrame([new_row_of_data])   # have to convert to dataframe to use concat!!57       Output_collection[strategies[index_strategy]] = pd.concat([Output_collection[strategies[index_strategy]], new_df], ignore_index=True)       # add new row to dataframe58      59      60#%% plot results61no_Trials = np.size(TestData.TrialIndex)62# plotting time series of MAPprobability for Rule Strategies63plt.figure(figsize=(10, 5))64plt.plot(Output_collection['go_left'].MAPprobability, linewidth=0.75)  # plots the time series65plt.plot(Output_collection['go_right'].MAPprobability, linewidth=0.75, color=(0.4, 0.8, 0.5))  # plots the time series66plt.plot(Output_collection['go_cued'].MAPprobability, linewidth=0.75, color=(0.8,0.6,0.5))  # plots the time series67plt.axis([0, no_Trials, 0, 1.25])  # establishes axis limits 68plt.xlabel('Trials'), plt.ylabel('P(Strategy)')  # labelling the axis69plt.axhline(y=0.5, color='darkgrey', linewidth=0.75, label="Chance")  # shows the line at which Chance is exceeded70plotSessionStructure(TestData)71plt.show()       72# plotting Precision for the same three strategies (precision identical for go_left and go_right)73plt.figure(figsize=(10, 5))74plt.plot(Output_collection['go_left'].Precision, linewidth=0.75)  # plots the time series75plt.plot(Output_collection['go_cued'].Precision, linewidth=0.75, color=(0.8,0.6,0.5))  # plots the time series76plt.xlabel('Trials'), plt.ylabel('Precision')  # labelling the axis77plt.show()      78       79# plotting MAP probability for some exploratory strategies - use interpolated values80plt.figure(figsize=(10, 5))81plt.plot(Output_collection['lose_shift_cued'].MAPprobability_interpolated, linewidth=0.75, color=(1, 0.1, 0.6))  # plots the time series82plt.plot(Output_collection['lose_shift_spatial'].MAPprobability_interpolated, linewidth=0.75, color=(0.8, 0.6, 0.5))  # plots the time series83plt.plot(Output_collection['win_stay_spatial'].MAPprobability_interpolated, linewidth=0.75, color=(0.4,0.8,0.5))  # plots the time series84plt.axis([0, no_Trials, 0, 1.25])  # establishes axis limits 85plt.xlabel('Trials'), plt.ylabel('P(Strategy)')  # labelling the axis86plt.axhline(y=0.5, color='darkgrey', linewidth=0.75, label="Chance")  # shows the line at which Chance is exceeded87plotSessionStructure(TestData)...

today_3strategy_signal.py

Source:today_3strategy_signal.py

1#-*-coding:UTF-8-*-2import datetime3from Models.Closed_Price_Prediction_model import arbitrary_prediction4from Strategys.INDEX_STRATEGY.CYB import CYB5from Strategys.INDEX_STRATEGY.Gold_ETF import GOLD_ETF6from Strategys.INDEX_STRATEGY.NSDK import NSDK7from Strategys.INDEX_STRATEGY.hs_300_strategy import hs_300_strategy8from Strategys.INDEX_STRATEGY.petro_ETF import PETRO_ETF9from pitcher import get_his_data10today = datetime.datetime.today()-datetime.timedelta(1)11yesterday = str(today)[0:10]12def tree_predict(df):13    xx = arbitrary_prediction(df)14    return [xx.one_day_gradient_change()]15start = '2015-10-10'16gold_index = get_his_data('159934',start=start,ma=[4,5,12,13,16,18,20,30,60,120,14],index=True)17gold_index = tree_predict(gold_index)18petro_index = get_his_data('162411',ma=[4,5,12,13,16,18,20,30,60,120,14],start=start,index=True)19petro_index = tree_predict(petro_index)20hs300_index = get_his_data('000300',ma = [5,12,13,16,18,20,30,60,120,14],start=start,index=True)21hs300_index = tree_predict(hs300_index)22cyb = get_his_data('399006',ma = [4,5,12,13,16,18,20,30,60,120,14,10],start=start,index=True)23cyb = tree_predict(cyb)24NSD_ETF = get_his_data('513100',ma=[3,60,120,250],start=start,index=True)25NSD_ETF = tree_predict(NSD_ETF)26open('todays_results.csv','w')27print('Processing Gold_Index')28open('todays_results.csv', 'a').write('GOLD_Prediction')29for i in gold_index:30    gold = GOLD_ETF(i)31    gold = gold.today_signal()32    gold.to_csv('todays_results.csv',mode='a')33    open('todays_results.csv', 'a').write('\n')34open('todays_results.csv','a').write('\n')35print('Processing Petro_index')36open('todays_results.csv', 'a').write('OIL_Prediction')37for i in petro_index:38    gold = PETRO_ETF(i)39    gold = gold.today_signal()40    gold.to_csv('todays_results.csv',mode='a')41    open('todays_results.csv', 'a').write('\n')42open('todays_results.csv','a').write('\n')43print('Processing hs300_index')44open('todays_results.csv', 'a').write('hs300_Prediction')45for i in hs300_index:46    gold = hs_300_strategy(i)47    gold = gold.today_signal()48    gold.to_csv('todays_results.csv',mode='a')49    open('todays_results.csv', 'a').write('\n')50open('todays_results.csv','a').write('\n')51print('Processing cyb')52open('todays_results.csv', 'a').write('CYB_prediction')53for i in cyb:54    gold = CYB(i)55    gold = gold.today_signal()56    gold.to_csv('todays_results.csv',mode='a')57    open('todays_results.csv', 'a').write('\n')58open('todays_results.csv','a').write('\n')59print('Processing NSD_index')60open('todays_results.csv', 'a').write('NASDAQ_Prediction')61for i in NSD_ETF:62    gold = NSDK(i)63    gold = gold.today_signal()64    gold.to_csv('todays_results.csv',mode='a')65    open('todays_results.csv', 'a').write('\n')66open('todays_results.csv','a').write('\n')67#print('é»éä»æ¥ä¹°åä¿¡å·: ')68#gold.today_signal()69#print('ç³æ²¹ä»æ¥ä¹°åä¿¡å·: ')70#petro.today_signal()71#print('æ²ªæ·±300ä»æ¥ä¹°åä¿¡å·: ')72#hs300#.today_signal()73#print('åä¸æ¿ä»æ¥ä¹°åä¿¡å·: ')...

test_concept_index.py

Source:test_concept_index.py

1import unittest2import datetime3import unittest4from strategy.strategy_index_calculator import IndexCalculatorStategy5from jqdemo.offline_stock_action import OfflineStockAction6from jqdemo.stock_action import StockAction7from config.config import *8class TestConceptIndex(unittest.TestCase):9    def __init__(self, method_name):10        unittest.TestCase.__init__(self, method_name)11        self.index_strategy = IndexCalculatorStategy()12        self.offline_stock_action = OfflineStockAction()13    def test_concept_index(self):14        self.index_strategy.calculate_concept_index('GN086')15        pass16if __name__ == '__main__':...

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