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PerformanceAnalytics_Class.py

Source:PerformanceAnalytics_Class.py

1#Python Script---------------------------------------------------------------------------------------------------------------------------2#Title: Performance Analytics3# coding: utf-84#---5import pandas as pd6import datetime 7import time8import math9import csv10import numpy as np11import scipy12from scipy.stats import trim_mean, kurtosis13from scipy.stats.mstats import mode, gmean, hmean14from scipy.stats import norm15from pandas.tseries.offsets import BDay16import ta as ta17class Performance():18    def __init__(self, Reuters):19        self.reuters  = Reuters20        21        #actual price data22        url_csv = "http://matterhorn-lab.herokuapp.com/download/"+ str(self.reuters)23        prices_data  = pd.read_csv(url_csv, sep = ",") 24        start, stop, step = 0, -14, 125        prices_data["Date"] = prices_data["Date"].str.slice(start, stop, step)26        27        prices_data = prices_data[::-1].reset_index()28        prices_data = prices_data.drop(['index'], axis=1)29        prices_data = prices_data.sort_values(["Date"], ascending = [1]).reset_index()30        prices_data = prices_data.drop(['index'], axis=1)31        32        33        #static34        stock_info = pd.read_csv('data/DB_Stock_Info.csv', sep=';')35        key_ratios = pd.read_csv('data/DB_Stock_Key_Ratios.csv', sep=';')36    37        # get the number of business days38        c_size = len(prices_data.columns)39        r_size = prices_data.shape[0]40         41        date_data = prices_data.iloc[:,0]42        today = date_data.iloc[r_size-1:r_size]43        today = pd.to_datetime(today)44        today = datetime.date(int(today.dt.year),int(today.dt.month),int(today.dt.day))45        46        # calculate days yesterday47        yesterday =  today -BDay(1)48        # calculate days last week49        lastweek = today -BDay(5)50        # calculate days  since start month51        startmonth = datetime.date(int(today.strftime('%Y')),int(today.strftime('%m')),1)52        days_start_month = np.busday_count(startmonth,today)53        # calculate days  last month54        lastmonth = datetime.date(int(today.strftime('%Y')),int(today.strftime('%m'))-1,int(today.strftime('%d')))55        days_last_month = np.busday_count(lastmonth,today)56        # calculate days since start year57        yearstart = datetime.date(int(today.strftime('%Y')),1,1)58        days_start_year = np.busday_count(yearstart,today)59        # calculate days one year60        lastyear = datetime.date(int(today.strftime('%Y'))-1,int(today.strftime('%m')),int(today.strftime('%d')))61        days_last_year = np.busday_count(lastyear,today)62        # calculate days three years63        last3years = datetime.date(int(today.strftime('%Y'))-3,int(today.strftime('%m')),int(today.strftime('%d')))64        days_last_3years = np.busday_count(last3years,today)65        # calculate days five years66        last5years = datetime.date(int(today.strftime('%Y'))-5,int(today.strftime('%m')),int(today.strftime('%d')))67        days_last_5years = np.busday_count(last5years,today)68        # calculate days ten years69        last10years = datetime.date(int(today.strftime('%Y'))-10,int(today.strftime('%m')),int(today.strftime('%d')))70        days_last_10years = np.busday_count(last10years,today)71        72        # calculate returns        73        prices = prices_data.iloc[:,1:c_size]74        #returns = math.log(prices/prices.shift(1))75        #prices_year = prices.iloc[r_size-days_year:r_size]76        price_change = pd.DataFrame(prices.values[r_size-1] - prices)77        price_change.columns = [ Reuters]78        returns = prices.pct_change(1)79        80        # calculate price and return today81        returns_today = returns.iloc[r_size-1:r_size]82        prices_today = prices.iloc[r_size-1:r_size]83        price_change_today = price_change.iloc[r_size-1:r_size]84        85        # calculate price and return yesterday86        returns_yesterday = returns.iloc[r_size-2:r_size]87        prices_yesterday = prices.iloc[r_size-2:r_size]88        cum_return_yesterday = prices_yesterday.loc[r_size-1] / prices_yesterday.loc[r_size-2] -189        average_return_yesterday = np.mean(returns_yesterday)90        price_change_yesterday = price_change.iloc[r_size-2:r_size-1]91        92        # calculate price and return last week93        returns_last_week = returns.iloc[r_size-5:r_size]94        prices_last_week = prices.iloc[r_size-5:r_size]95        cum_return_last_week = prices_last_week.loc[r_size-1] / prices_last_week.loc[r_size-5] -196        average_return_last_week = np.mean(returns_last_week)97        price_change_last_week = price_change.iloc[r_size-5:r_size]98        vola_last_week = np.std(returns_last_week)99        sharpe_ratio_last_week = average_return_last_week /vola_last_week100        101        # calculate price and return since start month102        returns_start_month = returns.iloc[r_size-days_start_month:r_size]103        prices_start_month = prices.iloc[r_size-days_start_month:r_size]104        cum_return_start_month = prices_start_month.loc[r_size-1] / prices_start_month.loc[r_size-days_start_month] -1105        average_return_start_month = np.mean(returns_start_month)106        price_change_start_month = price_change.iloc[r_size-days_start_month:r_size]107        vola_start_month = np.std(returns_start_month)108        sharpe_ratio_start_month = average_return_start_month /vola_start_month109        110        # calculate price and return last month111        returns_last_month = returns.iloc[r_size-days_last_month:r_size]112        prices_last_month = prices.iloc[r_size-days_last_month:r_size]113        cum_return_last_month = prices_last_month.loc[r_size-1] / prices_last_month.loc[r_size-days_last_month] -1114        average_return_last_month = np.mean(returns_last_month)115        price_change_last_month = price_change.iloc[r_size-days_last_month:r_size]116        117        # calculate price and return since start year118        returns_start_year = returns.iloc[r_size-days_start_year:r_size]119        prices_start_year = prices.iloc[r_size-days_start_year:r_size]120        cum_return_start_year = prices_start_year.loc[r_size-1] / prices_start_year.loc[r_size-days_start_year] -1121        average_return_start_year = np.mean(returns_start_year)122        price_change_start_year = price_change.iloc[r_size-days_start_year:r_size]123        vola_start_year = np.std(returns_start_year)124        sharpe_ratio_start_year = average_return_start_year /vola_start_year125        126        # calculate price and return one year127        returns_last_year = returns.iloc[r_size-days_last_year:r_size]128        prices_last_year = prices.iloc[r_size-days_last_year:r_size]129        cum_return_last_year = prices_last_year.loc[r_size-1] / prices_last_year.loc[r_size-days_last_year] -1130        average_return_last_year = np.mean(returns_last_year)131        price_change_last_year = price_change.iloc[r_size-days_last_year:r_size]132        vola_last_year = np.std(returns_last_year)133        sharpe_ratio_last_year = average_return_last_year /vola_last_year134        135        # calculate price and return three years136        returns_last_3years = returns.iloc[r_size-days_last_3years:r_size]137        prices_last_3years = prices.iloc[r_size-days_last_3years:r_size]138        cum_return_last_3years = prices_last_3years.loc[r_size-1] / prices_last_3years.loc[r_size-days_last_3years] -1139        average_return_last_3years = np.mean(returns_last_3years)140        price_change_last_3years = price_change.iloc[r_size-days_last_3years:r_size]141        vola_last_3years = np.std(returns_last_3years)142        sharpe_ratio_last_3years = average_return_last_3years /vola_last_3years143        144        # calculate price and return five years145        returns_last_5years = returns.iloc[r_size-days_last_5years:r_size]146        prices_last_5years = prices.iloc[r_size-days_last_5years:r_size]147        cum_return_last_5years = prices_last_5years.loc[r_size-1] / prices_last_5years.loc[r_size-days_last_5years] -1148        average_return_last_5years = np.mean(returns_last_5years)149        price_change_last_5years = price_change.iloc[r_size-days_last_5years:r_size]150        vola_last_5years = np.std(returns_last_5years)151        sharpe_ratio_last_5years = average_return_last_5years /vola_last_5years152        153        # calculate price and return ten years154        returns_last_10years = returns.iloc[r_size-days_last_10years:r_size]155        prices_last_10years = prices.iloc[r_size-days_last_10years:r_size]156        cum_return_last_10years = prices_last_10years.loc[r_size-1] / prices_last_10years.loc[r_size-days_last_10years] -1157        average_return_last_10years = np.mean(returns_last_10years)158        price_change_last_10years = price_change.iloc[r_size-days_last_10years:r_size]159        vola_last_10years = np.std(returns_last_10years)160        sharpe_ratio_last_10years = average_return_last_10years /vola_last_10years161        162        # all time163        cum_return_all = prices.loc[r_size-1] / prices.loc[3] -1164        average_return_all = np.mean(returns)165        vola_all = np.std(returns)166        sharpe_ratio_all = average_return_all /vola_all167        # year high, low and range168        year_high = prices_last_year.max()169        year_low = prices_last_year.min()170        range_low_high = year_high - year_low171        range_percent = range_low_high / year_high172        173        # investment of 10000 CHF174        help_investment = returns175        help_investment = help_investment.drop(help_investment.index[0:2])176        help_invest = [0] * (c_size-1)177        help_investment.iloc[0] = help_invest178        investment = (1+help_investment).cumprod() *10000179        180        # describtive statistics181        mean = np.mean(returns_last_year)182        std = np.std(returns_last_year)183        Z_99 = norm.ppf([0.01])184        185        186        # Value at risk187        Covar_Var = -(mean-Z_99*std)188        n_sims = 1000000189        SimVar =[]190        for i in range(c_size-1):191            np.random.seed(i)192            random_numbers = np.random.normal(0, 1, n_sims)193            sim_returns=mean[i]+std[i]*random_numbers194            SimVar = (np.percentile(sim_returns, 1))195        196        HistVar=[]197        for i in range(0,r_size-days_last_year):198            help_VaR = returns.iloc[r_size-days_last_year-i:r_size-i]199            HistVar.append(np.percentile(help_VaR, 1))200         201        df_HistVar = {}202        df_HistVar= {"Name": HistVar}203        HistVar = pd.DataFrame(HistVar)204        205        # Expected Shortfall206        cutoff = int(round(days_last_year * 0.01,0))207                208        ES = []209        for i in range(0,r_size-days_last_year):210            help_ES = returns.Price.iloc[r_size-days_last_year-i:r_size-i]211            losses = help_ES.sort_values()212            expectedloss = np.mean(losses.iloc[0:cutoff])213            ES.append(expectedloss)214         215        data_ES = {}216        data_ES = {"Name": ES}217        ES = pd.DataFrame(ES)218        219        # Drawdown220        Roll_Max = prices.cummax()221        Daily_Drawdown = (prices/Roll_Max - 1.0)222        Max_Daily_Drawdown = Daily_Drawdown.cummin()223        224        Daily_Drawdown = abs(Daily_Drawdown)225        Max_Daily_Drawdown = abs(Max_Daily_Drawdown)226                          227        228        #Key Ratios229        key_ratios.columns= ["Name", "ABBN.S", "ADEN.S", "ALCC.S", "CSGN.S", "GEBN.S", "GIVN.S", 230                             "LHN.S", "LONN.S", "NESN.S", "NOVN.S", "CFR.S", "ROG.S", "SGSN.S",231                             "SIKA.S", "UHR.S", "SLHN.S", "SRENH.S", "SCMN.S", "UBSG.S", "ZURN.S"]232        233        key_ratios_clean = key_ratios["NESN.S"]234        price_earnings_ratio = key_ratios_clean.iloc[4]235        236        # price/book ratio237        price_book_ratio = key_ratios_clean.iloc[5]238        239        # return on equity ratio240        return_on_equity_ratio = key_ratios_clean.iloc[12]241        242        # Dividend yield - indicated annual dividend divided by closing price243        dividend_yield_ratio = key_ratios_clean.iloc[8]244        245        # debt to ratio246        debt_equity_ratio = key_ratios_clean.iloc[20]247        248    249        # =============================================================================250        #Sort all analysis from above to get dataframes which are used on the webpage for the tables and figures251        252        #Overview: Data for Figure Annual Performance253        Data = {'Date':  [lastmonth,lastyear, last3years, last5years, last10years],254                'Price': [cum_return_last_month.Price, cum_return_last_year.Price, cum_return_last_3years.Price, cum_return_last_5years.Price, cum_return_last_10years.Price],255                }256        257        self.df_annual_perf = pd.DataFrame(Data, columns = ['Date','Price'])258        259        #Table Price Performance260        Data_Price_Performance = {261            'Name':  ["Placeholder"],262            '1 month':  [cum_return_last_month.Price],263            '1 Years': [cum_return_last_year.Price],264            '3 Years': [cum_return_last_3years.Price],265            '5 Years': [cum_return_last_5years.Price],266            '10 Years': [cum_return_last_10years.Price],267            'Since Inception': [cum_return_all.Price],268            }269    270        self.df_Price_Performance = pd.DataFrame(Data_Price_Performance, columns = ['1 month','1 Years', '3 Years', '5 Years', '10 Years', 'Since Inception'])271        272        #Overview: Hypothetical Growth   273        V2007 = investment.iloc[r_size-3-days_last_year*12].Price274        V2008 = investment.iloc[r_size-3-days_last_year*11].Price275        V2009 = investment.iloc[r_size-3-days_last_year*10].Price276        V2010 = investment.iloc[r_size-3-days_last_year*9].Price277        V2011 = investment.iloc[r_size-3-days_last_year*8].Price278        V2012 = investment.iloc[r_size-3-days_last_year*7].Price279        V2013 = investment.iloc[r_size-3-days_last_year*6].Price280        V2014 = investment.iloc[r_size-3-days_last_year*5].Price281        V2015 = investment.iloc[r_size-3-days_last_year*4].Price282        V2016 = investment.iloc[r_size-3-days_last_year*3].Price283        V2017 = investment.iloc[r_size-3-days_last_year*2].Price284        V2018 = investment.iloc[r_size-3-days_last_year].Price285        V2019 = investment.iloc[r_size-3].Price286        287        hypothetical_growth = {'Date':  ['2006','2007','2008','2009','2010','2011','2012','2013','2014','2015','2016','2017','2018','2019'],288                'Value': [10000,V2007, V2008,V2009,V2010,V2011,V2012,V2013,V2014,V2015,V2016,V2017,V2018,V2019]289                }290        291        self.df_hypothetical_growth = pd.DataFrame(hypothetical_growth, columns = ['Date','Value'])292        293        #Overview: Figure Average Annual Performance294        annual_perf_average = {'Date':  [lastmonth,lastyear, last3years, last5years, last10years],295                       'Price': [average_return_last_month.Price*252, average_return_last_year.Price*252, average_return_last_3years.Price*252, average_return_last_5years.Price*252, average_return_last_10years.Price*252],296                       }297        self.df_annual_perf_average = pd.DataFrame(annual_perf_average, columns = ['Date','Price'])298        299        300        #Overview: igure Risk Potential301        #Define quantiles for Graph302        q0=-1.854444201294828303        q1=-0.8269888130616426304        q2=0.22536003249425604305        q3=0.6619326773878177306        q4=1.1356494832642325307        SR = sharpe_ratio_last_year.Price * math.sqrt(252)308        309        #Define Values for Figure310        if (SR< q1):311            self.SR_q = 0.09312        elif (SR >= q1 and SR< q2):313            self.SR_q = 0.29314        elif (SR >= q2 and SR < q3):315            self.SR_q = 0.49316        elif (SR >= q3 and SR < q4):317            self.SR_q = 0.69318        elif (SR >= q4):319            self.SR_q = 0.89320         321        322        Data_Current_Statistic = {"Name": ["Price change yesterday",  "Average Annual Return", "Average daily Volatility","1 Year Volatilty", "1 Year Sharpe Ratio"],323           "Numbers": [round(float(price_change_yesterday.values[0]),2),  round(average_return_all.Price*252* 100, 2).astype(str) + '%', round(vola_last_year.Price,3), round(vola_last_year.Price * math.sqrt(252),3), round(sharpe_ratio_last_year.Price * math.sqrt(252),3)]324             }325        326        self.df_Current_Statistic = pd.DataFrame(Data_Current_Statistic, columns = ["Name", "Numbers"])327        328        329        #Price Performance: Table Historic Prices330        Avg_price = pd.DataFrame.mean(prices)331        Data_Historic_Prices = {"Name": ["Current Price", "Price Last Year", "Average Price","Year High", "Year Low"],332               "Numbers": [prices_today.Price.iloc[0],prices_last_year.Price.iloc[0], Avg_price.Price, year_high.Price, year_low.Price]333                 }334        335        self.df_Historic_Prices = pd.DataFrame(Data_Historic_Prices, columns = ["Name", "Numbers"])        336        self.df_Historic_Prices.Numbers = round(self.df_Historic_Prices.Numbers , 2)337    338    339        #Price Performance: Figure Price Development340        date_data_clean = pd.DataFrame(date_data)341        date_data_clean["Prices"] = prices342        343        self.df_Performance_Graph = date_data_clean344        345        #Price Performance: Figure returns 346        date_data_clean2 = pd.DataFrame(date_data)347        date_data_clean2["Returns"] = returns348        349        self.df_Return_Graph = date_data_clean2350        351                    352        #Price Performance: Key Ratios353        Data_Key_Ratios = {"Name": ["Price Earnings Ratio", "Price Book Ratio", "Return on Equity","Debt Equity Ratio", "Dividend Yield Ratio",],354        "Numbers": [round(price_earnings_ratio,2), round(price_book_ratio,2), round(return_on_equity_ratio,2), round(debt_equity_ratio* 100, 2).astype(str) + '%', round(dividend_yield_ratio* 100, 2).astype(str) + '%']355         }356        self.df_Key_Ratios = pd.DataFrame(Data_Key_Ratios, columns = ["Name", "Numbers"])357        358        #Risk Measures: Table 1359        Data_Risk_Measures1 = {"Name": [ "Sharpe Ratio last year", "Sharpe Ratio Total", "Daily Drawdown", "Max Daily Drawdown"],360                               "Numbers": [round(sharpe_ratio_last_year.Price * math.sqrt(252),2), round(sharpe_ratio_all.Price *math.sqrt(r_size),2), round(Daily_Drawdown.Price.iloc[-1]* 100, 2).astype(str) + '%',  round(Max_Daily_Drawdown.Price.iloc[-1]* 100, 2).astype(str) + '%']361                               }362        self.df_Risk_Measure1 = pd.DataFrame(Data_Risk_Measures1, columns = ["Name", "Numbers"])363        364        #Risk Measures: Table 2365        Data_Risk_Measures2 = {"Name": [ "Historic Value at Risk","Simulated Value at Risk", "Parametic Value at Risk","Expected Shortfall"],366                               "Numbers": [ round(float(HistVar.values[0]),4), round(SimVar,4), round(Covar_Var.Price,4) , round(float(ES.values[0]),4)]367         }368        self.df_Risk_Measure2 = pd.DataFrame(Data_Risk_Measures2, columns = ["Name", "Numbers"])369        370        371        #Risk Measures: Value at Risk        372        data_VaR = pd.DataFrame(df_HistVar, columns = ["Name"])373        data_VaR = data_VaR[::-1].reset_index()374        data_VaR = data_VaR.drop(['index'], axis=1)375        Date_VaR = pd.DataFrame(date_data.iloc[days_last_year:r_size]).reset_index()376        Date_VaR = Date_VaR.drop(['index'], axis=1)377        Date_VaR["Price"] = data_VaR378        self.df_VaR = Date_VaR379        380        #Risk Measures: Expected Shortfall381        Data_ES = pd.DataFrame(data_ES, columns = ["Name"])382        Data_ES = Data_ES[::-1].reset_index()383        Data_ES = Data_ES.drop(['index'], axis=1)384        Date_ES = pd.DataFrame(date_data.iloc[days_last_year:r_size]).reset_index()385        Date_ES = Date_ES.drop(['index'], axis=1)386        Date_ES["Price"] = Data_ES387        self.df_ES = Date_ES388                        389        390         #Risk Measures: Drawdown391        date_data_clean1 = pd.DataFrame(date_data)392        date_data_clean1["Max_DD"] = Max_Daily_Drawdown393        date_data_clean1["DD"] = Daily_Drawdown394        date_data_clean1["Roll_Max"] = Roll_Max395        self.df_Max_Daily_Drawdown = date_data_clean1396        397        398        #Technical399        b = prices.Price400        bollinger_mavg = ta.bollinger_mavg(b)401        bollinger_hband = ta.bollinger_hband(b)402        bollinger_lband = ta.bollinger_lband(b)403        bollinger_hband_indicator = ta.bollinger_hband_indicator(b)404        bollinger_lband_indicator=ta.bollinger_lband_indicator(b)405        406        rsi = ta.rsi(b)407        aroon_up = ta.aroon_up(b)408        aroon_down = ta.aroon_down(b)409        410        #Technical Analysis: Table Technical Analysis411        aroon_up_today = aroon_up.values[r_size-2]412        aroon_down_today = aroon_down.values[r_size-2]413        if (aroon_up_today  > aroon_down_today):414            if (aroon_up_today > 50):415                aroon_text = 'The Aroon Indicator detects a current strong upwards trend'416            else:417                aroon_text = 'The Aroon Indicator detects a current weak upwards trend'418        else:419            if (aroon_down_today > 50):420                aroon_text = 'The Aroon Indicator detects a current strong downwards trend'421            else:422                aroon_text = 'The Aroon Indicator detects a current weak downwards trend'423        424        425        rsi_today = rsi.values[r_size-2]426        if (rsi_today  > 70):427            rsi_text = 'The Relative Strength Index detects a current overvaluation of the stock'428        elif(rsi_today > 30 and rsi_today <70):429            rsi_text = 'The Relative Strength Index detects no current overvaluation or undervaluation of the stock'    430        else:431            rsi_text = 'The Relative Strength Index detects a current undervaluation of the stock'432      433        bollinger_hband_indicator_today = bollinger_hband_indicator.values[r_size-2]434        bollinger_lband_indicator_today = bollinger_lband_indicator.values[r_size-2]435        if (bollinger_hband_indicator_today  > bollinger_lband_indicator_today):436            bollinger_text = 'The Bollinger Band Oscillator detects that the current price is higher than the higher Bollinger Band and therefore recommends a buy of the stock'437        elif(bollinger_lband_indicator_today > 0):438            bollinger_text = 'The Bollinger Band Oscillator detects that the current price is lower than the lower Bollinger Band and therefore recommends  a selling of the stock'439        else:440            bollinger_text = 'The Bollinger Band Oscillator detects that the current price is between the lower and higher Bollinger Band and therefore recommends no trading activities in the stock'441        TechnicalAnalysis = {"Name": [ "Boolinger Band:", "Relative Strength Index:", "Aroon Indicator:"],442       "Implications": [bollinger_text, rsi_text, aroon_text]443         }444        self.df_TechnicalAnalysis= pd.DataFrame(TechnicalAnalysis, columns = ["Name", "Implications"])445        446        #Technical Analyis: Figure Bollinger447        Date_Bollinger = pd.DataFrame(date_data)448        Date_Bollinger["mavg"] = bollinger_mavg449        Date_Bollinger["hband"] = bollinger_hband450        Date_Bollinger["lband"] = bollinger_lband451        self.df_BollingerBands = Date_Bollinger452        453        #Technical Analyis: Figure RSI454        Date_RSI = pd.DataFrame(date_data)455        Date_RSI["RSI"] = rsi456        457        df_RSI = Date_RSI.drop(Date_RSI.index[0:14]).reset_index()458        self.df_RSI = df_RSI.drop(['index'], axis=1)459        460        #Technical Analyis: Figure Aroon461        Date_aroon = pd.DataFrame(date_data)462        Date_aroon["aroon_up"] = aroon_up463        Date_aroon["aroon_down"] = aroon_down464        self.df_AroonIndicator = Date_aroon...

0003_auto_20170616_1404.py

Source:0003_auto_20170616_1404.py

1# -*- coding: utf-8 -*-2# Generated by Django 1.11.1 on 2017-06-16 14:043from __future__ import unicode_literals4from django.db import migrations, models5class Migration(migrations.Migration):6    dependencies = [7        ('raw', '0002_viewingtimes'),8    ]9    operations = [10        migrations.AddField(11            model_name='address',12            name='last_updated',13            field=models.DateTimeField(auto_now=True),14        ),15        migrations.AddField(16            model_name='agency',17            name='last_updated',18            field=models.DateTimeField(auto_now=True),19        ),20        migrations.AddField(21            model_name='agent',22            name='last_updated',23            field=models.DateTimeField(auto_now=True),24        ),25        migrations.AddField(26            model_name='attribute',27            name='last_updated',28            field=models.DateTimeField(auto_now=True),29        ),30        migrations.AddField(31            model_name='attributeoption',32            name='last_updated',33            field=models.DateTimeField(auto_now=True),34        ),35        migrations.AddField(36            model_name='attributerange',37            name='last_updated',38            field=models.DateTimeField(auto_now=True),39        ),40        migrations.AddField(41            model_name='bid',42            name='last_updated',43            field=models.DateTimeField(auto_now=True),44        ),45        migrations.AddField(46            model_name='bidcollection',47            name='last_updated',48            field=models.DateTimeField(auto_now=True),49        ),50        migrations.AddField(51            model_name='branding',52            name='last_updated',53            field=models.DateTimeField(auto_now=True),54        ),55        migrations.AddField(56            model_name='broadbandtechnology',57            name='last_updated',58            field=models.DateTimeField(auto_now=True),59        ),60        migrations.AddField(61            model_name='category',62            name='last_updated',63            field=models.DateTimeField(auto_now=True),64        ),65        migrations.AddField(66            model_name='charity',67            name='last_updated',68            field=models.DateTimeField(auto_now=True),69        ),70        migrations.AddField(71            model_name='contactdetails',72            name='last_updated',73            field=models.DateTimeField(auto_now=True),74        ),75        migrations.AddField(76            model_name='currentshippingpromotion',77            name='last_updated',78            field=models.DateTimeField(auto_now=True),79        ),80        migrations.AddField(81            model_name='dealer',82            name='last_updated',83            field=models.DateTimeField(auto_now=True),84        ),85        migrations.AddField(86            model_name='dealership',87            name='last_updated',88            field=models.DateTimeField(auto_now=True),89        ),90        migrations.AddField(91            model_name='dealershiplistingcounts',92            name='last_updated',93            field=models.DateTimeField(auto_now=True),94        ),95        migrations.AddField(96            model_name='dealershipphonenumbers',97            name='last_updated',98            field=models.DateTimeField(auto_now=True),99        ),100        migrations.AddField(101            model_name='dealershowroom',102            name='last_updated',103            field=models.DateTimeField(auto_now=True),104        ),105        migrations.AddField(106            model_name='district',107            name='last_updated',108            field=models.DateTimeField(auto_now=True),109        ),110        migrations.AddField(111            model_name='embeddedcontent',112            name='last_updated',113            field=models.DateTimeField(auto_now=True),114        ),115        migrations.AddField(116            model_name='fixedpriceofferdetails',117            name='last_updated',118            field=models.DateTimeField(auto_now=True),119        ),120        migrations.AddField(121            model_name='fixedpriceofferrecipient',122            name='last_updated',123            field=models.DateTimeField(auto_now=True),124        ),125        migrations.AddField(126            model_name='flatmate',127            name='last_updated',128            field=models.DateTimeField(auto_now=True),129        ),130        migrations.AddField(131            model_name='flatmateadjacentsuburbids',132            name='last_updated',133            field=models.DateTimeField(auto_now=True),134        ),135        migrations.AddField(136            model_name='flatmateadjacentsuburbnames',137            name='last_updated',138            field=models.DateTimeField(auto_now=True),139        ),140        migrations.AddField(141            model_name='flatmatephotourls',142            name='last_updated',143            field=models.DateTimeField(auto_now=True),144        ),145        migrations.AddField(146            model_name='flatmates',147            name='last_updated',148            field=models.DateTimeField(auto_now=True),149        ),150        migrations.AddField(151            model_name='foundcategory',152            name='last_updated',153            field=models.DateTimeField(auto_now=True),154        ),155        migrations.AddField(156            model_name='geographiclocation',157            name='last_updated',158            field=models.DateTimeField(auto_now=True),159        ),160        migrations.AddField(161            model_name='largebannerimage',162            name='last_updated',163            field=models.DateTimeField(auto_now=True),164        ),165        migrations.AddField(166            model_name='listeditemdetail',167            name='last_updated',168            field=models.DateTimeField(auto_now=True),169        ),170        migrations.AddField(171            model_name='locality',172            name='last_updated',173            field=models.DateTimeField(auto_now=True),174        ),175        migrations.AddField(176            model_name='member',177            name='last_updated',178            field=models.DateTimeField(auto_now=True),179        ),180        migrations.AddField(181            model_name='memberprofile',182            name='last_updated',183            field=models.DateTimeField(auto_now=True),184        ),185        migrations.AddField(186            model_name='memberrequestinformation',187            name='last_updated',188            field=models.DateTimeField(auto_now=True),189        ),190        migrations.AddField(191            model_name='membershipdistrict',192            name='last_updated',193            field=models.DateTimeField(auto_now=True),194        ),195        migrations.AddField(196            model_name='membershiplocality',197            name='last_updated',198            field=models.DateTimeField(auto_now=True),199        ),200        migrations.AddField(201            model_name='motorwebbasicreport',202            name='last_updated',203            field=models.DateTimeField(auto_now=True),204        ),205        migrations.AddField(206            model_name='openhome',207            name='last_updated',208            field=models.DateTimeField(auto_now=True),209        ),210        migrations.AddField(211            model_name='option',212            name='last_updated',213            field=models.DateTimeField(auto_now=True),214        ),215        migrations.AddField(216            model_name='optionset',217            name='last_updated',218            field=models.DateTimeField(auto_now=True),219        ),220        migrations.AddField(221            model_name='optionsetvalues',222            name='last_updated',223            field=models.DateTimeField(auto_now=True),224        ),225        migrations.AddField(226            model_name='photo',227            name='last_updated',228            field=models.DateTimeField(auto_now=True),229        ),230        migrations.AddField(231            model_name='photourl',232            name='last_updated',233            field=models.DateTimeField(auto_now=True),234        ),235        migrations.AddField(236            model_name='properties',237            name='last_updated',238            field=models.DateTimeField(auto_now=True),239        ),240        migrations.AddField(241            model_name='property',242            name='last_updated',243            field=models.DateTimeField(auto_now=True),244        ),245        migrations.AddField(246            model_name='propertyadjacentsuburbids',247            name='last_updated',248            field=models.DateTimeField(auto_now=True),249        ),250        migrations.AddField(251            model_name='propertyadjacentsuburbnames',252            name='last_updated',253            field=models.DateTimeField(auto_now=True),254        ),255        migrations.AddField(256            model_name='propertyphotourls',257            name='last_updated',258            field=models.DateTimeField(auto_now=True),259        ),260        migrations.AddField(261            model_name='question',262            name='last_updated',263            field=models.DateTimeField(auto_now=True),264        ),265        migrations.AddField(266            model_name='questions',267            name='last_updated',268            field=models.DateTimeField(auto_now=True),269        ),270        migrations.AddField(271            model_name='refunddetails',272            name='last_updated',273            field=models.DateTimeField(auto_now=True),274        ),275        migrations.AddField(276            model_name='sale',277            name='last_updated',278            field=models.DateTimeField(auto_now=True),279        ),280        migrations.AddField(281            model_name='searchparameter',282            name='last_updated',283            field=models.DateTimeField(auto_now=True),284        ),285        migrations.AddField(286            model_name='shippingoption',287            name='last_updated',288            field=models.DateTimeField(auto_now=True),289        ),290        migrations.AddField(291            model_name='simplememberprofile',292            name='last_updated',293            field=models.DateTimeField(auto_now=True),294        ),295        migrations.AddField(296            model_name='sponsorlink',297            name='last_updated',298            field=models.DateTimeField(auto_now=True),299        ),300        migrations.AddField(301            model_name='store',302            name='last_updated',303            field=models.DateTimeField(auto_now=True),304        ),305        migrations.AddField(306            model_name='storepromotion',307            name='last_updated',308            field=models.DateTimeField(auto_now=True),309        ),310        migrations.AddField(311            model_name='suburb',312            name='last_updated',313            field=models.DateTimeField(auto_now=True),314        ),315        migrations.AddField(316            model_name='suburbadjacentsuburbs',317            name='last_updated',318            field=models.DateTimeField(auto_now=True),319        ),320        migrations.AddField(321            model_name='variant',322            name='last_updated',323            field=models.DateTimeField(auto_now=True),324        ),325        migrations.AddField(326            model_name='variantdefinition',327            name='last_updated',328            field=models.DateTimeField(auto_now=True),329        ),330        migrations.AddField(331            model_name='variantdefinitionsummary',332            name='last_updated',333            field=models.DateTimeField(auto_now=True),334        ),335        migrations.AddField(336            model_name='viewingtime',337            name='last_updated',338            field=models.DateTimeField(auto_now=True),339        ),340        migrations.AddField(341            model_name='viewingtimes',342            name='last_updated',343            field=models.DateTimeField(auto_now=True),344        ),...

expressions.py

Source:expressions.py

1"""This module contains the expressions applicable for CronTrigger's fields."""2from calendar import monthrange3import re4from apscheduler.util import asint5__all__ = ('AllExpression', 'RangeExpression', 'WeekdayRangeExpression',6           'WeekdayPositionExpression', 'LastDayOfMonthExpression')7WEEKDAYS = ['mon', 'tue', 'wed', 'thu', 'fri', 'sat', 'sun']8MONTHS = ['jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug', 'sep', 'oct', 'nov', 'dec']9class AllExpression(object):10    value_re = re.compile(r'\*(?:/(?P<step>\d+))?$')11    def __init__(self, step=None):12        self.step = asint(step)13        if self.step == 0:14            raise ValueError('Increment must be higher than 0')15    def validate_range(self, field_name):16        from apscheduler.triggers.cron.fields import MIN_VALUES, MAX_VALUES17        value_range = MAX_VALUES[field_name] - MIN_VALUES[field_name]18        if self.step and self.step > value_range:19            raise ValueError('the step value ({}) is higher than the total range of the '20                             'expression ({})'.format(self.step, value_range))21    def get_next_value(self, date, field):22        start = field.get_value(date)23        minval = field.get_min(date)24        maxval = field.get_max(date)25        start = max(start, minval)26        if not self.step:27            next = start28        else:29            distance_to_next = (self.step - (start - minval)) % self.step30            next = start + distance_to_next31        if next <= maxval:32            return next33    def __eq__(self, other):34        return isinstance(other, self.__class__) and self.step == other.step35    def __str__(self):36        if self.step:37            return '*/%d' % self.step38        return '*'39    def __repr__(self):40        return "%s(%s)" % (self.__class__.__name__, self.step)41class RangeExpression(AllExpression):42    value_re = re.compile(43        r'(?P<first>\d+)(?:-(?P<last>\d+))?(?:/(?P<step>\d+))?$')44    def __init__(self, first, last=None, step=None):45        super(RangeExpression, self).__init__(step)46        first = asint(first)47        last = asint(last)48        if last is None and step is None:49            last = first50        if last is not None and first > last:51            raise ValueError('The minimum value in a range must not be higher than the maximum')52        self.first = first53        self.last = last54    def validate_range(self, field_name):55        from apscheduler.triggers.cron.fields import MIN_VALUES, MAX_VALUES56        super(RangeExpression, self).validate_range(field_name)57        if self.first < MIN_VALUES[field_name]:58            raise ValueError('the first value ({}) is lower than the minimum value ({})'59                             .format(self.first, MIN_VALUES[field_name]))60        if self.last is not None and self.last > MAX_VALUES[field_name]:61            raise ValueError('the last value ({}) is higher than the maximum value ({})'62                             .format(self.last, MAX_VALUES[field_name]))63        value_range = (self.last or MAX_VALUES[field_name]) - self.first64        if self.step and self.step > value_range:65            raise ValueError('the step value ({}) is higher than the total range of the '66                             'expression ({})'.format(self.step, value_range))67    def get_next_value(self, date, field):68        startval = field.get_value(date)69        minval = field.get_min(date)70        maxval = field.get_max(date)71        # Apply range limits72        minval = max(minval, self.first)73        maxval = min(maxval, self.last) if self.last is not None else maxval74        nextval = max(minval, startval)75        # Apply the step if defined76        if self.step:77            distance_to_next = (self.step - (nextval - minval)) % self.step78            nextval += distance_to_next79        return nextval if nextval <= maxval else None80    def __eq__(self, other):81        return (isinstance(other, self.__class__) and self.first == other.first and82                self.last == other.last)83    def __str__(self):84        if self.last != self.first and self.last is not None:85            range = '%d-%d' % (self.first, self.last)86        else:87            range = str(self.first)88        if self.step:89            return '%s/%d' % (range, self.step)90        return range91    def __repr__(self):92        args = [str(self.first)]93        if self.last != self.first and self.last is not None or self.step:94            args.append(str(self.last))95        if self.step:96            args.append(str(self.step))97        return "%s(%s)" % (self.__class__.__name__, ', '.join(args))98class MonthRangeExpression(RangeExpression):99    value_re = re.compile(r'(?P<first>[a-z]+)(?:-(?P<last>[a-z]+))?', re.IGNORECASE)100    def __init__(self, first, last=None):101        try:102            first_num = MONTHS.index(first.lower()) + 1103        except ValueError:104            raise ValueError('Invalid month name "%s"' % first)105        if last:106            try:107                last_num = MONTHS.index(last.lower()) + 1108            except ValueError:109                raise ValueError('Invalid month name "%s"' % last)110        else:111            last_num = None112        super(MonthRangeExpression, self).__init__(first_num, last_num)113    def __str__(self):114        if self.last != self.first and self.last is not None:115            return '%s-%s' % (MONTHS[self.first - 1], MONTHS[self.last - 1])116        return MONTHS[self.first - 1]117    def __repr__(self):118        args = ["'%s'" % MONTHS[self.first]]119        if self.last != self.first and self.last is not None:120            args.append("'%s'" % MONTHS[self.last - 1])121        return "%s(%s)" % (self.__class__.__name__, ', '.join(args))122class WeekdayRangeExpression(RangeExpression):123    value_re = re.compile(r'(?P<first>[a-z]+)(?:-(?P<last>[a-z]+))?', re.IGNORECASE)124    def __init__(self, first, last=None):125        try:126            first_num = WEEKDAYS.index(first.lower())127        except ValueError:128            raise ValueError('Invalid weekday name "%s"' % first)129        if last:130            try:131                last_num = WEEKDAYS.index(last.lower())132            except ValueError:133                raise ValueError('Invalid weekday name "%s"' % last)134        else:135            last_num = None136        super(WeekdayRangeExpression, self).__init__(first_num, last_num)137    def __str__(self):138        if self.last != self.first and self.last is not None:139            return '%s-%s' % (WEEKDAYS[self.first], WEEKDAYS[self.last])140        return WEEKDAYS[self.first]141    def __repr__(self):142        args = ["'%s'" % WEEKDAYS[self.first]]143        if self.last != self.first and self.last is not None:144            args.append("'%s'" % WEEKDAYS[self.last])145        return "%s(%s)" % (self.__class__.__name__, ', '.join(args))146class WeekdayPositionExpression(AllExpression):147    options = ['1st', '2nd', '3rd', '4th', '5th', 'last']148    value_re = re.compile(r'(?P<option_name>%s) +(?P<weekday_name>(?:\d+|\w+))' %149                          '|'.join(options), re.IGNORECASE)150    def __init__(self, option_name, weekday_name):151        super(WeekdayPositionExpression, self).__init__(None)152        try:153            self.option_num = self.options.index(option_name.lower())154        except ValueError:155            raise ValueError('Invalid weekday position "%s"' % option_name)156        try:157            self.weekday = WEEKDAYS.index(weekday_name.lower())158        except ValueError:159            raise ValueError('Invalid weekday name "%s"' % weekday_name)160    def get_next_value(self, date, field):161        # Figure out the weekday of the month's first day and the number of days in that month162        first_day_wday, last_day = monthrange(date.year, date.month)163        # Calculate which day of the month is the first of the target weekdays164        first_hit_day = self.weekday - first_day_wday + 1165        if first_hit_day <= 0:166            first_hit_day += 7167        # Calculate what day of the month the target weekday would be168        if self.option_num < 5:169            target_day = first_hit_day + self.option_num * 7170        else:171            target_day = first_hit_day + ((last_day - first_hit_day) // 7) * 7172        if target_day <= last_day and target_day >= date.day:173            return target_day174    def __eq__(self, other):175        return (super(WeekdayPositionExpression, self).__eq__(other) and176                self.option_num == other.option_num and self.weekday == other.weekday)177    def __str__(self):178        return '%s %s' % (self.options[self.option_num], WEEKDAYS[self.weekday])179    def __repr__(self):180        return "%s('%s', '%s')" % (self.__class__.__name__, self.options[self.option_num],181                                   WEEKDAYS[self.weekday])182class LastDayOfMonthExpression(AllExpression):183    value_re = re.compile(r'last', re.IGNORECASE)184    def __init__(self):185        super(LastDayOfMonthExpression, self).__init__(None)186    def get_next_value(self, date, field):187        return monthrange(date.year, date.month)[1]188    def __str__(self):189        return 'last'190    def __repr__(self):...

single_strategy.py

Source:single_strategy.py

1# encoding: utf-82# author:  gao-ming3# time:  2019/7/14--22:024# desc:5def stragtegy_MACD(df):6    macd_df = df[['DIF', 'DEA', 'MACD']]7    macd_list = df['MACD']8    # var_macd_list=[]9    # i=110    # while 1:11    #     var_macd=macd_list[-i]-macd_list[-1-1]12    #     var_macd_list.append(var_macd)13    last_macd = macd_df.iloc[-1, :]14    last_DIF = last_macd['DIF']15    last_DEA = last_macd['DEA']16    last_MACD = last_macd['MACD']17    up_trend = False18    if last_DIF > last_DEA and last_MACD > 0:19        up_trend = 'ä¸åè¶å¿'20    else:21        up_trend = 'éè¡æä¸éè¶å¿'22    var_macd_mark = []23    for i in range(1, len(macd_list)):24        var_macd = macd_list[-i] - macd_list[-i - 1]25        if i == 1:26            try:27                first_mark = var_macd / abs(var_macd)28            except:29                first_mark = 030        if var_macd > 0:31            var_macd_mark.append(1)32        elif var_macd < 0:33            var_macd_mark.append(-1)34        else:35            var_macd_mark.append(0)36        i += 137    if first_mark == 1:38        trend_add = 'å¢å¼º'39    else:40        trend_add = 'åå¼±'41    res = {42        'trend': up_trend + '--' + trend_add,43    }44    return res45def strategy_BOLL(df, n: int = 20):46    """47    å¤æä»·æ ¼è¾¹ç ä»·æ ¼è¿å¨ æä¸å®çè¾¹ç48    :param df:49    :param n:  ä½¿ç¨çæ°æ®é¿åº¦  é»è®¤2050    :return:   ä¸ä¸è½¨ä»·æ ¼  ä»¥å å¨ä¸è½¨æä¸è½¨éè¿ï¼ç»åºæä½çä»·æ ¼åºé´  å¶å®ä¸º051    """52    boll_df = df.iloc[-n:, :]53    mid_s = boll_df['boll_mid']54    last_mid = mid_s[-1]55    # è®¡ç®ä¸ä¸å¨æçbollè½¨é56    next_mid = mid_s[-1] * 2 - mid_s[-2]57    next_up = boll_df['boll_up'][-1] * 2 - boll_df['boll_up'][-2]58    next_dn = boll_df['boll_dn'][-1] * 2 - boll_df['boll_dn'][-2]59    last_close = df['close'][-1]60    risk_rate_income, section = 0, 061    # bollçº¿ï¼ä¸è½¨éè¿ï¼ååº  ç»åºä»·æ ¼åºé´62    if abs(last_close - next_up) < last_mid * 0.025:63        section = (next_up - 0.025 * last_mid, next_up + 0.025 * last_mid)64        # ååº  æ¶çé£é©æ¯å¾å°65        risk_rate_income = 0.0166    # bollçº¿ä¸è½¨ ä¹°å¥ ç»åºä¹°å¥åºé´67    if abs(last_close - next_dn) < last_mid * 0.025:68        section = (next_dn - 0.025 * last_mid, next_dn + 0.025 * last_mid)69        try:70            risk_rate_income = (next_mid - last_close) / (last_close - next_dn) - 171        except:72            risk_rate_income = 673        if risk_rate_income > 5 or risk_rate_income < -1:74            risk_rate_income = 575    res = {76        'next_up': next_up,77        'next_dn': next_dn,78        # é£é©åæ¥æ¯79        'risk_income': '%.2f' % risk_rate_income,80        # ä»·æ ¼åèåºé´81        'section': section,82    }83    return res84def strategy_KDJ(df):85    """86    å¤ææ¦çè¾¹ç  è¶ä¹° è·çæ¦çå¤§    è¶å  æ¶¨çæ¦çå¤§87    :param df:88    :return:89    """90    kdj_df = df[['kdj_K', 'kdj_D']]91    kdj_df['K-D'] = kdj_df['kdj_K'] - kdj_df['kdj_D']92    last_kdj = kdj_df.iloc[-1, :]93    kdj_K = last_kdj['kdj_K']94    kdj_D = last_kdj['kdj_D']95    too_much = False96    if kdj_K > 80 or kdj_D > 80:97        too_much = 'è¿å¥è¶ä¹°åºé´'98    if kdj_K < 20 or kdj_D < 20:99        too_much = 'è¿å¥è¶ååºé´'100    res = {101        'kdj_K': kdj_K,102        'kdj_D': kdj_D,103    }104    if too_much:105        res['kdj_res'] = too_much106    return res107def strategy_RSI(df):108    rsi_df = df[['RSI_6', 'RSI_12', 'RSI_24']]109    pass110def strategy_MA(df, n: int = 20):111    """112    åçº¿çç¥ï¼å¤æè¶å¿113    :param df:114    :return:115    """116    try:117        ma_df = df[f'close_MA_{n}']118    except Exception as e:119        raise Exception('åçº¿å¨æææ°æ®æè¯¯ï¼')120    # ä¸åè¶å¿ï¼å¹³çï¼ä¸éè¶å¿ï¼æåè¿åçè¶å¿ç»æ121    # mark_up,mark_line,mark_dn,trend=0,0,0,0122    # print('0',mark_up)123    # bollä¸çº¿çæ³¢å¨å¼è¾¹çï¼å¨è¿ä¸ªå¼åæ³¢å¨ï¼è®¤ä¸ºæ¯åççæ³¢å¨124    stand_value = ma_df[-1] * 0.007125    var_mid_list = []126    var_mid_mark = []127    for i in range(2, n):128        # è®¡ç®bollä¸çº¿ååå·®å¼129        var_mid = ma_df[-i] - ma_df[1 - i]130        var_mid_list.append(var_mid)131        # å¤æè¶å¿132        if abs(var_mid) < stand_value:133            mark = 0  # å¹³ç134        elif var_mid > stand_value:135            mark = 1  # ä¸åè¶å¿136        else:137            mark = -1  # ä¸éè¶å¿138        var_mid_mark.append(mark)139    last_mark = var_mid_mark[0]140    # ä¿åè¶å¿ï¼ä»¥åå å¼ºè¿æ¯åå¼±141    trend_res = [last_mark, -1]142    if var_mid_mark[0] * var_mid_mark[1] > 0:143        if abs(var_mid_list[0]) > abs(var_mid_list[1]):144            trend_res[1] = 1  # è¶å¿å å¼º145    # å¤æè¶å¿å»¶ç»çå¨æ146    trend_num = 0147    for i in range(1, n):148        trend_num += 1149        if var_mid_list[i] * var_mid_list[i - 1] < 0:150            break151    suggest = 'æ¸ä»' if last_mark == -1 else 'æä»ææ³¢æ®µ'152    trend_dict = {153        '1': 'ä¸å',154        '0': 'å¹³ç',155        '-1': 'ä¸é',156    }157    trend_add = {158        '-1': 'åå¼±',159        '1': 'å å¼º',160    }161    trend_judge = trend_dict[trend_res[0]] + '  ' + trend_add[trend_res[1]]162    res = {163        'suggest': suggest,164        # è¶å¿165        'trend': trend_judge,166        # å¨ææç»çé¿åº¦167        'trend_num': trend_num,168    }169    return res170def strategy_VOL(df):171    """172    æ ¹æ®æäº¤éæ¥å¤æ173    :param df:å«ææäº¤é åæäº¤éåå¼çdataframe174    :return:  å¤æç»æ:175        'abnormal':éè½æ¯å¦å¼å¸¸,176        'vol_status':éè½å¢åç¶æ,177        'period':æç»æ¶é´,178    """179    vol_df = df[['vol', 'vol_MA_10']]180    # å¤æéè½å¼å¸¸181    vol_abnormal = 0182    last_vol = vol_df[-1, :]183    if last_vol['vol'] / last_vol['vol_MA_10'] > 2:184        vol_abnormal = 'éè½å¼å¸¸'185    # è®°å½æäº¤éçå¢åç¶æ186    var_vol_MA_sign = []187    for i in range(1, 50):188        last_vol = vol_df[-i, :]189        if last_vol['vol'] - last_vol['vol_MA_10'] >= 0:190            var_vol_MA_sign.append(1)191        else:192            var_vol_MA_sign.append(-1)193    if var_vol_MA_sign[0] == 1:194        vol_status = 'éè½å¢å '195    else:196        vol_status = 'éè½åå°'197    # ä¿åååçèç¹index198    change_index = []199    #200    for i in range(len(var_vol_MA_sign)):201        if var_vol_MA_sign[i] * var_vol_MA_sign[i + 1] < 0:202            change_index.append(i)203    # éè½äº¤æ¿ä¸è½è¶è¿3ä¸ªå¨æï¼å¦åæç»ç¶æç»æ204    i = 0205    while 1:206        if change_index[i + 1] - change_index[i] > 3:207            vol_period = i208            break209        i += 2210    vol_period = change_index[vol_period]211    res = {212        'abnormal': vol_abnormal,213        'vol_status': vol_status,214        'period': vol_period,215    }...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.