def backtest_obv_strategy(args):
# If no symbols were passed in
# Retrieve top active stocks from Yahoo Finance
try:
symbols = args[1].split(',')
except IndexError:
print('No symbol(s) provided. Retrieving top 10 from Yahoo Finance...')
symbols = get_10_best_active_stocks()
portfolio_amount = portfolio_input('Enter cash amount to give each stock: ')
try:
splices = args[2].split(',')
except IndexError:
splices = ['year1month1']
interval = '5min'
results = {}
print('\nBACKTESTING CONFIRMATION:')
print('----------------------------------')
print(f'Cash: $%.2f' % float(portfolio_amount))
print(f"Stocks: {', '.join(symbols)}")
print(f"Splices: {', '.join(splices)}")
print(f'Interval: {interval}')
print('----------------------------------')
confirmation = input('Continue? (Y/n): ')
if confirmation.lower() != 'y':
return
for splice in splices:
results[splice] = {}
total_cash = 0
for symbol in symbols:
print('===================================')
historic_data = get_stock_data(symbol, interval, splice)
if type(historic_data) is not pd.DataFrame:
print(f'Error: {symbol} not found.\n')
continue
print(f'Trading {symbol} using {interval} intervals...')
cash = float(portfolio_amount)
results[splice][symbol] = {}
Transactions = {}
transaction_holder = {}
position = None
stop_loss_price = None
initial_loss_percentage = 0.98
trailing_loss_percentage = 0.96
for i in range(0, len(historic_data.index)):
datetime = historic_data.index[i]
interval_date, interval_time = str(datetime).split(' ')
if interval_date not in Transactions.keys():
Transactions[interval_date] = []
if is_market_open(historic_data.index[i]):
close_price = historic_data['close'][datetime]
obv = historic_data['OBV'][datetime]
obv_ema = historic_data['OBV_EMA'][datetime]
if not position and obv > obv_ema:
shares = math.floor(cash / close_price)
transaction_holder = { 'Buy': close_price, 'Buy Time': interval_time, 'Quantity': shares }
position = close_price
stop_loss_price = close_price * initial_loss_percentage
# print(f'Buy triggered by: {obv} > {obv_ema}')
# print(f'Buy {shares} shares on {interval_date} at {interval_time}: $%.2f' % close_price)
# print('Set stop loss to $%.2f' % stop_loss_price)
# If at least 2 indicators from the last 3 intervals tell it to sell
elif position and obv < obv_ema:
transaction_holder['Sell'] = close_price
transaction_holder['Sell Time'] = interval_time
Transactions[interval_date].append(transaction_holder)
# print(f'Sell triggered by: {obv} < {obv_ema}')
# print(f'Sell on {interval_date} at {interval_time}: $%.2f' % close_price)
net_profit = (close_price * transaction_holder['Quantity']) - (transaction_holder['Buy'] * transaction_holder['Quantity'])
percentage = ((close_price - transaction_holder['Buy']) / transaction_holder['Buy']) * 100
# print('Profit/Loss: ${0} ({1}%)\n'.format(('%.2f' % net_profit), ('%.2f' % percentage)))
position = None
transaction_holder = {}
# Sell if its holding and the price has dropped to our stop loss
elif position and close_price <= stop_loss_price:
transaction_holder['Sell'] = close_price
transaction_holder['Sell Time'] = interval_time
Transactions[interval_date].append(transaction_holder)
# print('Sell triggered by stop loss $%.2f' % stop_loss_price)
# print(f'SL Sell on {interval_date} at {interval_time}: $%.2f' % close_price)
net_profit = (close_price * transaction_holder['Quantity']) - (transaction_holder['Buy'] * transaction_holder['Quantity'])
percentage = ((close_price - transaction_holder['Buy']) / transaction_holder['Buy']) * 100
# print('Profit/Loss: ${0} ({1}%)\n'.format(('%.2f' % net_profit), ('%.2f' % percentage)))
position = None
transaction_holder = {}
# Update trailing stop loss if price is rising
elif position and close_price > position and (close_price * trailing_loss_percentage) > stop_loss_price:
position = close_price
stop_loss_price = close_price * trailing_loss_percentage
# print('Updated trailing stop loss to $%.2f' % stop_loss_price)
# If the market's about to close, sell remaining positions
if position and interval_time == '15:59:00':
transaction_holder['Sell'] = close_price
transaction_holder['Sell Time'] = interval_time
Transactions[interval_date].append(transaction_holder)
# print('Sell triggered by market closing')
# print(f'MC Sell on {interval_date} at {interval_time}: $%.2f' % close_price)
net_profit = (close_price * transaction_holder['Quantity']) - (transaction_holder['Buy'] * transaction_holder['Quantity'])
percentage = ((close_price - transaction_holder['Buy']) / transaction_holder['Buy']) * 100
# print('Profit/Loss: ${0} ({1}%)\n'.format(('%.2f' % net_profit), ('%.2f' % percentage)))
position = None
transaction_holder = {}
# Check for end of day
elif interval_time == '16:00:00':
# Calculate total results for the day
day_profits = 0
if len(Transactions[interval_date]) > 0:
for transaction in Transactions[interval_date]:
num_shares = transaction['Quantity']
buy = transaction['Buy']
sell = transaction['Sell']
day_profits += (sell * num_shares) - (buy * num_shares)
updated_cash = cash + day_profits
profit_percent = ((updated_cash - cash) / cash) * 100
results[splice][symbol][interval_date] = profit_percent
cash = updated_cash
# Display day result
print('{0}: {1}% (${2}), balance: ${3}'.format(interval_date, ('%.2f' % profit_percent), ('%.2f' % day_profits), '%.2f' % cash))
total_cash += cash
print()
# plot.figure(figsize=(15, 6))
# plot.bar(results[splice][symbol].keys(), results[splice][symbol].values())
# plot.title(f'{symbol} Results')
# plot.xlabel('Date')
# plot.xticks(rotation = 60)
# plot.ylabel('Net % Gain')
# plot.show()
# input('hit Enter to test next symbol')
# END SYMBOLS LOOP
# Calculate total percentages for all days in splice
cash = float(portfolio_amount)
splice_percents = []
for symbol in results[splice].keys():
splice_percent = 0
for date in results[splice][symbol].keys():
splice_percent += results[splice][symbol][date]
cash *= 1 + (splice_percent / 100)
splice_percents.append(splice_percent)
# plot.figure(figsize=(15, 6))
# plot.bar(results[splice].keys(), splice_percents)
# plot.title(f'Backtesting Results')
# plot.xlabel('Symbol')
# plot.xticks(rotation = 45)
# plot.ylabel('Net % Gain')
# plot.show()
print(f'{splice} SUMMARY:')
original_cash = float(portfolio_amount) * len(results[splice].keys())
print('${0} --> ${1}'.format('%.2f' % original_cash, '%.2f' % total_cash))
print('{}% return'.format('%.2f' % ((total_cash - original_cash) / original_cash * 100)))
for symbol_string in symbol_strings:
batch_api_url = f"https://sandbox.iexapis.com/stable/stock/market/batch/?symbols={symbol_string}&types=quote&token={IEX_CLOUD_API_TOKEN}"
data = requests.get(batch_api_url).json()
for symbol in symbol_string.split(','):
final_dataframe = final_dataframe.append(pd.Series([
symbol, data[symbol]['quote']['latestPrice'],
data[symbol]['quote']['peRatio'], 'N/A'
],
index=columns),
ignore_index=True)
# Select 50 best value stocks
final_dataframe.sort_values('Price-to-Earnings Ratio', inplace=True)
final_dataframe = final_dataframe[
final_dataframe['Price-to-Earnings Ratio'] >= 0]
final_dataframe = final_dataframe[:50]
final_dataframe.reset_index(drop=True, inplace=True)
########################################################################
################# Calculate the amount of shares to buy ################
########################################################################
portfolio_size = portfolio_input()
position_size = float(portfolio_size) / len(final_dataframe.index)
for i in final_dataframe.index:
final_dataframe.loc[i, 'Number of Shares to Buy'] = math.floor(
position_size / final_dataframe.loc[i, 'Price'])
print(final_dataframe)
holdings[symbol] = transaction
results[symbol] = total_profits
# END SYMBOL LOOP
# Display results
print(f'\nTOTAL GAINS ON {trading_day}:')
updated_portfolio = portfolio
for symbol in results.keys():
updated_portfolio += results[symbol]
print(f'{symbol}: $%.2f' % results[symbol])
print()
print('On {0}, I turned ${1} into ${2}'.format(
trading_day, ('%.2f' % portfolio), ('%.2f' % updated_portfolio)))
if len(holdings.keys()) > 0:
print("I'm still holding:")
for symbol in holdings.keys():
print(f'{symbol}: $%.2f' %
(holdings[symbol]['Quantity'] * holdings[symbol]['Buy']))
plot.figure(figsize=(15, 6))
plot.bar(results.keys(), results.values())
plot.title(f'Results from {trading_day}')
plot.xlabel('Symbols')
plot.ylabel('Net Gain/Loss ($)')
plot.show()
obv_strategy(sys.argv, portfolio_input())