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back_testing.py
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back_testing.py
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#from datetime import datetime
import datetime
import financial as f
import pandas as pd
from abc import abstractmethod, ABC
import matplotlib as plt
import indicators as ind
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.dates as mdates
import csv
#calculate buy and sell info, returns
class BackTesting(ABC):
def __init__(self, tker, feed, buy, sell, position, cash=10000,debug=0):
self.__tker = tker
self.__feed = feed
self.method_buy = buy
self.method_sell = sell
self.position = position
self.debug = debug
self.__buy = []
self.__sell = []
self.buy_index = []
self.sell_index = []
self.__result = []
self.__cash = cash
self.__cash_ini = cash
self.__portfolio = 0
self.__share = 0
self.__transaction = pd.DataFrame()
self.__bars = []
self.__daily_profit = None
self.__profit = None
self.stop_loss = 0
self.log= pd.DataFrame(columns=['Time', 'transaction', 'share', 'price', 'cash', 'holdings'])
def run(self):
stock_n = None
row_list = []
index_list = []
i = 0
for index, row in self.__feed.iterrows():
row_list.append(list(row))
index_list.append(index)
stock_n = pd.DataFrame(row_list,columns=self.__feed.columns, index=index_list)
position = self.position(stock_n)
if i > 5:
if position == 1: #long postion
if not self.method_buy(stock_n) is None:
self.__buy.append(index)
self.buy_index.append(i)
result = self.method_buy(stock_n)
self.stop_loss = result[1]
share = result[2]
self.market_buy(index,share,row['close'])
if not self.method_sell(stock_n) is None:
self.__sell.append(index)
self.sell_index.append(i)
_, _, share, _ = self.method_sell(stock_n)
self.market_sell(index,share,row['close'])
elif position == -1: #long or short
if not self.method_sell(stock_n) is None:
self.__sell.append(index)
self.sell_index.append(i)
_, _, share, _ = self.method_sell(stock_n)
self.market_sell(index,share,row['close'])
i +=1
#execute orders based on time marks
#self.get_returns()
#create transaction log
self.log_transaction()
#calulate profit
#self.get_returns_trend()
def get_buy(self):
return self.__buy
def get_sell(self):
return self.__sell
def get_buy_index(self):
return self.buy_index
def get_sell_index(self):
return self.sell_index
def get_returns(self):
return round(self.__share*self.__feed['close'].iloc[-1] + self.__cash - self.__cash_ini, 2)
def get_portfolio(self):
self.__portfolio = self.__share*self.__feed['close'].iloc[-1] + self.__cash
print(f'[Info] Total portfolio: {self.__portfolio:.2f}')
print(f'[Info] Total return: {(self.__portfolio - self.__cash_ini)/self.__cash_ini*100:.2f}')
return (self.__portfolio - self.__cash_ini)/self.__cash_ini*100
def market_buy(self, time, share, price):
#share = int(self.__cash*0.1/price)
assert (price < self.__cash), "You are broken, Game over!"
if share > 0:
self.__share += share
self.__cash -= price*share
bar = [time, 'Buy', share, price, self.__cash, self.__share,
self.__share*price + self.__cash]
self.__bars.append(bar)
def market_sell(self, time, share, price):
#share = int(self.__share*0.2)
if share > 0: # and share < self.__share:
self.__share -= share
self.__cash += price*share
bar = [time, 'Sell', share, price, self.__cash, self.__share,
self.__share*price + self.__cash]
self.__bars.append(bar)
#log table
#time transaction share price cash holding_shares
def log_transaction(self):
columns = ['Time', 'transaction', 'share', 'price', 'cash', 'holdings','total invest']
self.__transaction = pd.DataFrame(self.__bars, columns=columns)
def get_transaction_log(self):
#print('Info:Transaction Log')
#print(self.__transaction)
return self.__transaction
def get_bid_result(self,period):
result = []
df = self.__transaction.copy()
stock = self.__feed.copy()
date = list(stock.index)
for d in df['Time']:
index = date.index(d)
if index + period >= len(date):
result.append('n/a')
else:
current = stock['close'].iloc[index]
future = stock['close'].iloc[index + period]
if df[df['Time']==d]['transaction'].iloc[0] == 'Buy':
if future > current:
result.append('WIN')
else:
result.append('LOSE')
elif df[df['Time']==d]['transaction'].iloc[0] == 'Sell':
if future < current:
result.append('WIN')
else:
result.append('LOSE')
self.__transaction['result'] = result
print(self.__transaction)
count = self.__transaction['result'].str.count('WIN').sum()
print('win ', count, 'out of ', len(self.__transaction), 'win chance is ', round(count/len(self.__transaction)*100,2),'%')
#calculate daily
"""def get_returns_trend(self):
dates = np.array(self.__feed[self.__timeFrame:].index)
price = self.__feed['close']
cash = np.zeros(len(dates), dtype= float)
cash.fill(self.__cash_ini)
stock = np.zeros(len(dates), dtype= float)
values = np.zeros(len(dates), dtype= float)
t = self.__transaction.copy()
start = 0
for i in range(len(dates)):
if dates[i] in list(t['Time']):
if (t.loc[t['Time'] == dates[i]]['transaction'] == 'Buy').bool() == True:
index = t[t['Time'] ==dates[i]].index[0] #row number
cash[i:] = t['cash'].iloc[index]
stock[i:] = t['holdings'].iloc[index]
elif (t.loc[t['Time'] == dates[i]]['transaction'] == 'Sell').bool() == True:
index = t[t['Time'] ==dates[i]].index[0] #row number
cash[i:]= t['cash'].iloc[index]
stock[i:] = t['holdings'].iloc[index]
values = cash + stock*price
# calculate daily returns
dif = np.diff(values)
dif = np.insert(dif, 0, 0., axis=0) # insert 0 at the top
profit = np.round(dif/values*100,2)
self.__daily_profit = profit
#calculate accumulative returns
profit = np.round((values - self.__cash_ini)/self.__cash_ini*100,2)
trend = list(zip(dates,price, cash, stock, values, profit))
returns = pd.DataFrame(trend, columns = ['date', 'price','cash','holding','values','profit'])
#print(returns)
self.__profit = profit"""
def plot_profit(self):
p = plotter()
p.plot_profit(self.__profit)
def get_portfolio_ref(self):
print('[Info] Stock Price change until today: ',
(self.__feed['close'].iloc[-1] - self.__feed['close'].iloc[0])/self.__feed['close'].iloc[0]*100)
#calculate buy and sell info, returns
class ForwardTesting(ABC):
def __init__(self, tker, feed, buy, sell, cash=10000,debug=0):
self.__tker = tker
self.__feed = feed
self.method_buy = buy
self.method_sell = sell
self.debug = debug
self.__buy = []
self.__sell = []
self.__result = []
self.__cash = cash
self.cash_ini = cash
self.__portfolio = 0
self.__share = 0
self.__transaction = pd.DataFrame()
self.long = True
def run(self):
self.update_position()
stock = self.__feed
if self.long is True:
if not self.method_buy(stock) is None: #long position
result = self.method_buy(stock)
self.long = result[0]
price = result[1]
share = result[2]
index = result[3]
self.market_buy(index,share,price)
elif self.long is False:
if not self.method_sell(stock) is None: #short position
result = self.method_sell(stock)
self.long = result[0]
price = result[1]
share = result[2]
index = result[3]
self.market_sell(index,share,price)
self.get_returns()
print('[Info]long', self.long)
def update_position(self):
transaction = pd.read_csv('log/forward_test.csv')
if len(transaction) > 0:
if transaction['transaction'].iloc[-1] == 'Buy':
self.long = False
elif transaction['transaction'].iloc[-1] == 'Sell':
self.long = True
def get_returns(self):
transaction = pd.read_csv('log/forward_test.csv')
if len(transaction) == 0:
total = self.cash_ini
else:
share = transaction['share'].iloc[-1]
cash = transaction['cash'].iloc[-1]
price = self.__feed['close'].iloc[-1]
total = cash + price*share
print('[Info] Transaction Log')
print(transaction)
print('[Info]Total', total)
return total
def market_buy(self, time, share, price):
#share = int(self.__cash*0.1/price)
assert (price < self.__cash), "You are broken, Game over!"
if share > 0:
self.__share += share
self.__cash -= price*share
bar = [time, 'Buy', share, price, self.__cash, self.__share,
self.__share*price + self.__cash]
self.log_transaction(bar)
def market_sell(self, time, share, price):
#share = int(self.__share*0.2)
if share > 0: # and share < self.__share:
self.__share -= share
self.__cash += price*share
bar = [time, 'Sell', share, price, self.__cash, self.__share,
self.__share*price + self.__cash]
self.log_transaction(bar)
#log table
#time transaction share price cash holding_shares
def log_transaction(self,bar):
with open('log/forward_test.csv', 'a') as f:
write = csv.writer(f)
write.writerow(bar)
#draw price trend and mark buy and sell points
class plotter():
def __init__(self):
super().__init__()
self.years = mdates.YearLocator() # every year
self.months = mdates.MonthLocator() # every month
self.days = mdates.DayLocator() # every day
self.hours = mdates.HourLocator() # every hour
self.autoLocator = mdates.AutoDateLocator()
self.minutes = mdates.MinuteLocator() # every minute
self.years_fmt = mdates.DateFormatter('%Y')
self.months_fmt = mdates.DateFormatter('%m-%d')
def plot_price(self,x,y):
plt.plot(x,y)
plt.show()
def plot_day(self, df, record, label):
datetimes = [datetime.datetime.strptime(d, '%Y-%m-%d') for d in df.index]
fig, (ax1, ax2) = plt.subplots(2, figsize=(12,4))
ax1.xaxis.set_major_locator(self.months)
ax1.xaxis.set_major_formatter(self.months_fmt)
ax1.xaxis.set_minor_locator(self.days)
ax1.grid(True)
ax1.set_ylabel(label)
ax1.plot( datetimes, df['boll_band'])
ax2.grid(True)
ax2.set_ylabel('Price')
ax2.plot(datetimes, df['close'], label='price')
ax2.plot(datetimes, df['boll_ub'], label='boll_ub')
ax2.plot(datetimes, df['boll'], label='boll')
ax2.plot(datetimes, df['boll_lb'], label='boll_lb')
plt.legend()
fig.autofmt_xdate()
fig.suptitle(label)
x = record['Time'].loc[record['transaction'] == 'Buy']
y = df['boll_band'].loc[x]
ax1.scatter(x, y, label = 'test', marker = '^', color = 'green')
x = record['Time'].loc[record['transaction'] == 'Sell']
y = df['boll_band'].loc[x]
ax1.scatter(x, y, label = 'sell', marker = 'v', color='red')
plt.show()
def plot_min(self, df, record, label):
fig, (ax1, ax2) = plt.subplots(2, figsize=(12,4))
ax1.plot(df['close'])
ax2.plot(df['boll_band'])
x = record['Time'].loc[record['transaction'] == 'Buy']
y = df['close'].loc[x]
ax1.scatter(x, y, label = 'buy', marker = '^', color = 'green')
x = record['Time'].loc[record['transaction'] == 'Sell']
y = df['close'].loc[x]
ax1.scatter(x, y, label = 'sell', marker = 'v', color='red')
fig.suptitle(label)
plt.legend()
plt.show()
def plot_result(self, df, record):
fig, ax = plt.subplots(figsize=(12,4))
ax.plot(df['close'])
x = record['Time'].loc[record['transaction'] == 'Buy']
y = df['close'].loc[x]
ax.scatter(x, y, label = 'buy', marker = '^', color = 'green')
x = record['Time'].loc[record['transaction'] == 'Sell']
y = df['close'].loc[x]
ax.scatter(x, y, label = 'sell', marker = 'v', color='red')
plt.legend()
plt.show()
def buy(self):
pass
def sell(self):
pass