<reponame>vrishank97/AlgoTrading
import pandas as pd

class Backtest:
    def __init__(self, agent, cash, stock=0, logging=False, ticker=None):
        self.agent = agent
        self.cash = cash
        self.stock = stock
        self.logging = logging
        self.ticker = ticker
        if logging:
            if ticker is None:
                raise Exception("Ticker not defined. Unable to log records")
            from arctic import Arctic

            self.store = Arctic('localhost')
            self.store.initialize_library('TransactionLogs')
            self.logstore = self.store['TransactionLogs']

    def getPortfolioVal(self, price):
        return int((self.stock*price)+self.cash)

    def buy_all(self, price):

        if(self.cash>price):
            # print("Buying %d stocks for %f each" % ((self.cash)//price, price))
            #print("buy at ", price)
            self.stock += (self.cash)//price
            self.cash -= ((self.cash)//price)*price

    def sell_all(self, price):
        if(self.stock>0):
            #print("sell at ", price)
            #print("Selling %d stocks for %f each" % (self.stock, price))
            self.cash += self.stock*price
            self.stock = 0

    def run(self, data, dates=None):
        results = []
        sell_count = 0
        buy_val = 0
        win_count = 0

        for i in range(len(data)):
            action = None
            if dates is not None:
                action = self.agent.step(data[i], dates[i])
            else:
                action = self.agent.step(data[i])

            if action == 1:
                # Buy
                buy_val = data[i]
                self.buy_all(data[i])

            elif action == -1:
                # Sell
                sell_count += 1
                self.sell_all(data[i])
                if data[i] > buy_val:
                        win_count += 1

            val = self.getPortfolioVal(data[i])

            if self.logging:
                action_type = None
                if action == 0:
                    action_type = "Hold"
                if action == 1:
                    action_type = "Buy"
                if action == -1:
                    action_type = "Sell"

                d = {'Timestep': i, 'Portfolio Value': val, 'Action': action_type}
                df = pd.DataFrame(data = d,index = [i])
                self.logstore.append(self.ticker , df, upsert=True)
            
            results.append(val)
            #print("Portfolio valuation : %f" % (val))

        if self.stock > 0:
            sell_count += 1
            if buy_val > 0:
                if data[len(data)-1] > buy_val:
                    win_count += 1
        '''
        if sell_count == 0:
            print("ZERO SALES")
        else:
            print("Sell count is ", sell_count, " and win rate is", (100*win_count)/sell_count)
        ''' 
        return results
import pandas as pd

class Backtest:
    def __init__(self, agent, cash, stock=0, logging=False, ticker=None):
        self.agent = agent
        self.cash = cash
        self.stock = stock
        self.logging = logging
        self.ticker = ticker
        if logging:
            if ticker is None:
                raise Exception("Ticker not defined. Unable to log records")
            from arctic import Arctic

            self.store = Arctic('localhost')
            self.store.initialize_library('TransactionLogs')
            self.logstore = self.store['TransactionLogs']

    def getPortfolioVal(self, price):
        return int((self.stock*price)+self.cash)

    def buy_all(self, price):

        if(self.cash>price):
            # print("Buying %d stocks for %f each" % ((self.cash)//price, price))
            #print("buy at ", price)
            self.stock += (self.cash)//price
            self.cash -= ((self.cash)//price)*price

    def sell_all(self, price):
        if(self.stock>0):
            #print("sell at ", price)
            #print("Selling %d stocks for %f each" % (self.stock, price))
            self.cash += self.stock*price
            self.stock = 0

    def run(self, data, dates=None):
        results = []
        sell_count = 0
        buy_val = 0
        win_count = 0

        for i in range(len(data)):
            action = None
            if dates is not None:
                action = self.agent.step(data[i], dates[i])
            else:
                action = self.agent.step(data[i])

            if action == 1:
                # Buy
                buy_val = data[i]
                self.buy_all(data[i])

            elif action == -1:
                # Sell
                sell_count += 1
                self.sell_all(data[i])
                if data[i] > buy_val:
                        win_count += 1

            val = self.getPortfolioVal(data[i])

            if self.logging:
                action_type = None
                if action == 0:
                    action_type = "Hold"
                if action == 1:
                    action_type = "Buy"
                if action == -1:
                    action_type = "Sell"

                d = {'Timestep': i, 'Portfolio Value': val, 'Action': action_type}
                df = pd.DataFrame(data = d,index = [i])
                self.logstore.append(self.ticker , df, upsert=True)
            
            results.append(val)
            #print("Portfolio valuation : %f" % (val))

        if self.stock > 0:
            sell_count += 1
            if buy_val > 0:
                if data[len(data)-1] > buy_val:
                    win_count += 1
        '''
        if sell_count == 0:
            print("ZERO SALES")
        else:
            print("Sell count is ", sell_count, " and win rate is", (100*win_count)/sell_count)
        ''' 
        return results
# print("Buying %d stocks for %f each" % ((self.cash)//price, price)) #print("buy at ", price) #print("sell at ", price) #print("Selling %d stocks for %f each" % (self.stock, price)) # Buy # Sell #print("Portfolio valuation : %f" % (val)) if sell_count == 0:             print("ZERO SALES")         else:             print("Sell count is ", sell_count, " and win rate is", (100*win_count)/sell_count)