def run(config, testing, tickers, _filename):
# Backtest information
title = ['Buy and Hold Example on %s' % tickers[0]]
initial_equity = 500000.0
start_date = datetime.datetime(2020, 1, 1)
todays_month = int(datetime.datetime.today().strftime("%m"))
todays_day = int(datetime.datetime.today().strftime("%d"))
end_date = datetime.datetime(2020, todays_month, todays_day)
# Use the Buy and Hold Strategy
events_queue = queue.Queue()
strategy = BuyAndHoldStrategy(tickers[0], events_queue)
# Set up the backtest
backtest = TradingSession(config,
strategy,
tickers,
initial_equity,
start_date,
end_date,
events_queue,
title=title)
results = backtest.start_trading(testing=testing, filename=_filename)
return results
def run(config, testing, tickers, filename):
# Backtest information
title = ['Moving Average Crossover Example on AAPL: 100x300']
initial_equity = 10000.0
start_date = datetime.datetime(2000, 1, 1)
end_date = datetime.datetime(2014, 1, 1)
# Use the MAC Strategy
events_queue = queue.Queue()
strategy = MovingAverageCrossStrategy(tickers[0],
events_queue,
short_window=50,
long_window=100)
# Set up the backtest
backtest = TradingSession(
config,
strategy,
tickers,
initial_equity,
start_date,
end_date,
events_queue,
title=title,
benchmark=tickers[1],
)
results = backtest.start_trading(testing=testing)
# results = backtest.start_trading(testing=Fa)
return results
def run(config, testing, tickers, filename):
# Backtest information
title = ['Accelerating momentum for SPY: 1m, 3m, 6m > 0']
initial_equity = 10000.0
start_date = datetime.datetime(2000, 1, 1)
end_date = datetime.datetime(2014, 1, 1)
# Use the MAC Strategy
events_queue = queue.Queue()
strategy = AcceleratingMomentumStrategy(tickers[0], events_queue)
# Set up the backtest
backtest = TradingSession(
config,
strategy,
tickers,
initial_equity,
start_date,
end_date,
events_queue,
title=title,
benchmark=tickers[1],
)
results = backtest.start_trading(testing=testing)
# results = backtest.start_trading(testing=Fa)
return results
def run(config, testing, tickers, _filename, initial_equity):
# Backtest information
title = ['Buy and Hold Example on %s' % tickers[0]]
start_date = datetime.datetime(2014, 1, 1)
todays_month = int(datetime.datetime.today().strftime("%m"))
todays_day = int(datetime.datetime.today().strftime("%d"))
end_date = datetime.datetime(2019, todays_month, todays_day)
# Use the Buy and Hold Strategy
events_queue = queue.Queue()
strategy = BuyAndHoldStrategy(tickers[0], events_queue)
ticker_weights = {"AZN.L": 1}
position_sizer = LiquidateRebalancePositionSizer(ticker_weights)
# Set up the backtest
backtest = TradingSession(config,
strategy,
tickers,
initial_equity,
start_date,
end_date,
events_queue,
title=title,
adjusted_or_close='adj_close',
position_sizer=position_sizer)
results = backtest.start_trading(testing=testing, filename=_filename)
return results
def run(config, testing, tickers, safe_asset, filename):
# Backtest information
title = ['Accelerating momentum for SPX, BONDS, MSCI: 1m, 3m, 6m > 0']
initial_equity = 10000.0
start_date = datetime.datetime(2000, 1, 1)
end_date = datetime.datetime(2019, 7, 1)
# Use the MAC Strategy
events_queue = queue.Queue()
strategy = AcceleratingMomentumStrategy(tickers, safe_asset, events_queue)
# Use the liquidate and rebalance position sizer
# with prespecified ticker weights
ticker_weights = {"SPY": 1, "VUSTX": 1, "VINEX": 1}
position_sizer = LiquidateRebalancePositionSizer(ticker_weights)
# Set up the backtest
backtest = TradingSession(
config,
strategy,
tickers,
initial_equity,
start_date,
end_date,
events_queue,
position_sizer=position_sizer,
title=title,
benchmark=tickers[1],
)
results = backtest.start_trading(testing=testing)
# results = backtest.start_trading(testing=Fa)
return results
def run(config, testing, tickers, _filename, initial_equity):
# Backtest information
title = ['Buy and Hold monthly rebalance Example on %s' % tickers[0]]
year_start = 2016
year_end = 2019
start_date = datetime.datetime(year_start, 12, 28)
todays_month = int(datetime.datetime.today().strftime("%m"))
todays_day = int(datetime.datetime.today().strftime("%d"))
end_date = datetime.datetime(year_end, todays_month, todays_day)
years = list(range(year_start, year_end + 1))
calendars = get_dict_of_trading_calendars(years, cal='LSE')
# Use the Buy and Hold Strategy
events_queue = queue.Queue()
strategy = BuyAndHoldStrategy(tickers[0], events_queue, calendars)
ticker_weights = {"TSLA": 1}
position_sizer = LiquidateRebalancePositionSizer(ticker_weights)
# Set up the backtest
backtest = TradingSession(config,
strategy,
tickers,
initial_equity,
start_date,
end_date,
events_queue,
title=title,
adjusted_or_close='adj_close',
position_sizer=position_sizer)
results = backtest.start_trading(testing=testing, filename=_filename)
return results
def run(config, testing, tickers, _filename, initial_equity):
# Backtest information
title = ['Exponential momentum on basket %s' % len(tickers), "tickers"]
year_start = 2012
year_end = 2019
start_date = datetime.datetime(year_start, 12, 28)
todays_month = int(datetime.datetime.today().strftime("%m"))
todays_day = int(datetime.datetime.today().strftime("%d"))
end_date = datetime.datetime(year_end, todays_month, todays_day)
csv_dir = config.CSV_DATA_DIR
adjusted_or_close = 'adj_close'
events_queue = queue.Queue()
price_handler = YahooDailyCsvBarPriceHandler(csv_dir,
events_queue,
tickers,
start_date=start_date,
end_date=end_date,
calc_adj_returns=True)
"""
we need to pass the first date dictionary ino the strategy, the np of -1s needs t obe the length
on a given day how many -1s are we expecting.
"""
first_date_dict = {}
# last_date_dict = {}
for ticker in price_handler.tickers:
first_date, last_date = price_handler.tickers[ticker][
'timestamp'], price_handler.tickers_data[ticker].index[-1]
first_date_dict[ticker] = (first_date, last_date)
# last_date_dict[ticker] = price_handler.tickers_data[ticker].index[-1]
first_date_df = pd.DataFrame(list(first_date_dict.items()),
columns=['ticker', 'Dates'])
first_date_df[['start_date',
'end_date']] = pd.DataFrame(first_date_df['Dates'].tolist(),
index=first_date_df.index)
first_date_df = first_date_df[['ticker', 'start_date', 'end_date']]
print(first_date_df)
# save the dataframe
first_date_df.to_pickle('tickers_date_range.pkl')
years = list(range(year_start, year_end + 1))
calendars = get_dict_of_trading_calendars(years, cal='LSE')
# Use the Buy and Hold Strategy
strategy = ExponentialMomentum(tickers, events_queue, calendars,
first_date_df)
risk_per_stock = 0.002
ticker_weights = {}
for ticker in tickers:
ticker_weights[ticker] = risk_per_stock
# ticker_weights = {
# "BP.L": 1,
# "GSK.L": 1,
# "ITV.L": 1,
# "NG.L": 1
# }
position_sizer = RiskParityATRPositionSizer(ticker_weights)
# risk_manager = ExampleRiskManager()
risk_manager = ExpoMomoRiskManager(PriceParser.parse(initial_equity))
portfolio_handler = PortfolioHandler(PriceParser.parse(initial_equity),
events_queue, price_handler,
position_sizer, risk_manager,
adjusted_or_close)
# Set up the backtest
backtest = TradingSession(config,
strategy,
tickers,
initial_equity,
start_date,
end_date,
events_queue,
title=title,
adjusted_or_close=adjusted_or_close,
position_sizer=position_sizer,
portfolio_handler=portfolio_handler,
price_handler=price_handler)
results = backtest.start_trading(testing=testing, filename=_filename)
return results
def run(config, testing, tickers, filename):
"""
http://www.lazyportfolioetf.com/allocation/ray-dalio-all-weather/
All weather portfolio,
We show the regular weightings
And use 2x leveraged ETFs to show the leveraged version.
"""
# Backtest information
title = [
'quarterly Liquidate/Rebalance on All-Weather\n30%/15%/40%/7.5%/7.5% SPY/IEF/TLT/GLD/DBC Portfolio'
]
# title = [
# 'Monthly Liquidate/Rebalance on All-Weather2x\n30%/15%/40%/7.5%/7.5% FLGE/UST/UBT/DGP/DBC Portfolio'
# ]
initial_equity = 100000.0
start_date = datetime.datetime(2011, 1, 1)
end_date = datetime.datetime(2020, 12, 27)
# Use the Monthly Liquidate And Rebalance strategy
events_queue = queue.Queue()
strategy = MonthlyLiquidateRebalanceStrategy(tickers, events_queue)
# Use the liquidate and rebalance position sizer
# with prespecified ticker weights
ticker_weights = {
"SPY": 0.3,
"IEF": 0.15,
"TLT": 0.4,
"GLD": 0.075,
"DBC": 0.075
}
# ticker_weights = {
# "SSO":0.29,
# "FLGE": 0.29,
# "UST": 0.15,
# "UBT": 0.4,
# "DGP": 0.075,
# "DBC": 0.075,
# "SPY": 0.01
# }
position_sizer = LiquidateRebalancePositionSizer(ticker_weights)
# Set up the backtest
backtest = TradingSession(
config,
strategy,
tickers,
initial_equity,
start_date,
end_date,
events_queue,
position_sizer=position_sizer,
adjusted_or_close='adj_close',
title=title,
benchmark=tickers[0],
)
results = backtest.start_trading(testing=testing, filename=filename)
return results