Module pinkfish.benchmark
Benchmark for comparision to a strategy.
Expand source code
"""
Benchmark for comparision to a strategy.
"""
import pinkfish.portfolio as portfolio
import pinkfish.pfstatistics as pfstatistics
import pinkfish.trade as trade
import pinkfish.utility as utility
class Benchmark:
"""
Portfolio Benchmark for comparison to a strategy.
"""
def __init__(self, symbols, capital, start, end,
dir_name='data',
use_adj=False,
use_continuous_calendar=False,
force_stock_market_calendar=False):
"""
Initialize instance variables.
Parameters
----------
symbols : str or list of str
The symbol(s) to use in the benchmark.
capital : int
The amount of money available for trading.
start : datetime.datetime
The desired start date for the benchmark.
end : datetime.datetime
The desired end date for the benchmark.
dir_name : str, optional
The leaf data dir name (default is 'data').
use_adj : bool, optional
True to adjust prices for dividends and splits
(default is False).
use_continuous_calendar: bool, optional
True if your timeseries has data for all seven days a week,
and you want to backtest trading every day, including weekends.
If this value is True, then `force_stock_market_calendar`
is set to False (default is False).
force_stock_market_calendar : bool, optional
True forces use of stock market calendar on timeseries.
Normally, you don't need to do this. This setting is intended
to transform a continuous timeseries into a weekday timeseries.
If this value is True, then `use_continuous_calendar` is set
to False.
Attributes
----------
symbols : list of str
The symbols to use in the benchmark.
capital : int
The amount of money available for trading.
start : datetime.datetime
The desired start date for the benchmark.
end : datetime.datetime
The desired end date for the benchmark.
dir_name : str, optional
The leaf data dir name (default is 'data').
use_adj : bool, optional
True to adjust prices for dividends and splits.
use_continuous_calendar: bool, optional
True if your timeseries has data for all seven days a week,
and you want to backtest trading every day, including weekends.
If this value is True, then `force_stock_market_calendar`
is set to False (default is False).
force_stock_market_calendar : bool, optional
True forces use of stock market calendar on timeseries.
Normally, you don't need to do this. This setting is intended
to transform a continuous timeseries into a weekday timeseries.
If this value is True, then `use_continuous_calendar` is set
to False.
ts : pd.DataFrame
The timeseries of the symbol used in backtest.
rlog : pd.DataFrame
The raw trade log.
tlog : pd.DataFrame
The trade log.
dbal : pd.DataFrame
The daily balance.
stats : pd.Series
The statistics for the benchmark.
portfolio : pf.Portfolio
The portfolio.
"""
# If symbols is not a list, cast it to a list.
if not isinstance(symbols, list):
symbols = [symbols]
symbols = list(set(symbols))
self.symbols = symbols
self.capital = capital
self.start = start
self.end = end
self.dir_name = dir_name
self.use_adj = use_adj
self.use_continuous_calendar = use_continuous_calendar
self.force_stock_market_calendar = force_stock_market_calendar
self.ts = None
self.rlog = None
self.tlog = None
self.dbal = None
self.stats = None
self.portfolio = None
def _algo(self):
"""
This is a general asset allocation stategy.
Invest an equal percent in each investment option and
rebalance every year.
"""
trade.TradeLog.cash = self.capital
trade.TradeLog.margin = trade.Margin.CASH
# These dicts are used to track close and weights for
# each symbol in portfolio
weights = {}
weight = 1 / len(self.portfolio.symbols)
weights = {symbol:weight for symbol in self.portfolio.symbols}
# Trading algorithm
for i, row in enumerate(self.ts.itertuples()):
end_flag = utility.is_last_row(self.ts, i)
start_flag = (i==0)
# Buy on first trading day
# Rebalance on the first trading day of each year
# Close all positions on last trading day
if row.first_doty or end_flag or start_flag:
# If last row, then zero out all weights.
if end_flag:
weights = utility.set_dict_values(weights, 0)
# Adjust weights of all symbols in portfolio
self.portfolio.adjust_percents(row, weights, field='close')
# Record daily balance.
self.portfolio.record_daily_balance(row)
def run(self):
"""
Run the strategy.
"""
self.portfolio = portfolio.Portfolio()
self.ts = self.portfolio.fetch_timeseries(
self.symbols, self.start, self.end,
fields=['close'], dir_name=self.dir_name, use_adj=self.use_adj,
use_continuous_calendar=self.use_continuous_calendar,
force_stock_market_calendar=self.force_stock_market_calendar)
# Add calendar columns
self.ts = self.portfolio.calendar(self.ts)
self.ts, self.start = self.portfolio.finalize_timeseries(self.ts, self.start)
self.portfolio.init_trade_logs(self.ts)
self._algo()
self._get_logs()
self._get_stats()
def _get_logs(self):
"""
Get the logs.
"""
self.rlog, self.tlog, self.dbal = self.portfolio.get_logs()
def _get_stats(self):
"""
Get the stats.
"""
self.stats = pfstatistics.stats(self.ts, self.tlog, self.dbal, self.capital)
Strategy = Benchmark
"""
class : Strategy is a class reference to Benchmark, i.e. an alias.
"""Classes
class Benchmark (symbols, capital, start, end, dir_name='data', use_adj=False, use_continuous_calendar=False, force_stock_market_calendar=False)-
Portfolio Benchmark for comparison to a strategy.
Initialize instance variables.
Parameters
symbols:strorlistofstr- The symbol(s) to use in the benchmark.
capital:int- The amount of money available for trading.
start:datetime.datetime- The desired start date for the benchmark.
end:datetime.datetime- The desired end date for the benchmark.
dir_name:str, optional- The leaf data dir name (default is 'data').
use_adj:bool, optional- True to adjust prices for dividends and splits (default is False).
use_continuous_calendar:bool, optional- True if your timeseries has data for all seven days a week,
and you want to backtest trading every day, including weekends.
If this value is True, then
force_stock_market_calendaris set to False (default is False). force_stock_market_calendar:bool, optional- True forces use of stock market calendar on timeseries.
Normally, you don't need to do this.
This setting is intended
to transform a continuous timeseries into a weekday timeseries.
If this value is True, then
use_continuous_calendaris set to False.
Attributes
symbols:listofstr- The symbols to use in the benchmark.
capital:int- The amount of money available for trading.
start:datetime.datetime- The desired start date for the benchmark.
end:datetime.datetime- The desired end date for the benchmark.
dir_name:str, optional- The leaf data dir name (default is 'data').
use_adj:bool, optional- True to adjust prices for dividends and splits.
use_continuous_calendar:bool, optional- True if your timeseries has data for all seven days a week,
and you want to backtest trading every day, including weekends.
If this value is True, then
force_stock_market_calendaris set to False (default is False). force_stock_market_calendar:bool, optional- True forces use of stock market calendar on timeseries.
Normally, you don't need to do this.
This setting is intended
to transform a continuous timeseries into a weekday timeseries.
If this value is True, then
use_continuous_calendaris set to False. ts:pd.DataFrame- The timeseries of the symbol used in backtest.
rlog:pd.DataFrame- The raw trade log.
tlog:pd.DataFrame- The trade log.
dbal:pd.DataFrame- The daily balance.
stats:pd.Series- The statistics for the benchmark.
portfolio:pf.Portfolio- The portfolio.
Expand source code
class Benchmark: """ Portfolio Benchmark for comparison to a strategy. """ def __init__(self, symbols, capital, start, end, dir_name='data', use_adj=False, use_continuous_calendar=False, force_stock_market_calendar=False): """ Initialize instance variables. Parameters ---------- symbols : str or list of str The symbol(s) to use in the benchmark. capital : int The amount of money available for trading. start : datetime.datetime The desired start date for the benchmark. end : datetime.datetime The desired end date for the benchmark. dir_name : str, optional The leaf data dir name (default is 'data'). use_adj : bool, optional True to adjust prices for dividends and splits (default is False). use_continuous_calendar: bool, optional True if your timeseries has data for all seven days a week, and you want to backtest trading every day, including weekends. If this value is True, then `force_stock_market_calendar` is set to False (default is False). force_stock_market_calendar : bool, optional True forces use of stock market calendar on timeseries. Normally, you don't need to do this. This setting is intended to transform a continuous timeseries into a weekday timeseries. If this value is True, then `use_continuous_calendar` is set to False. Attributes ---------- symbols : list of str The symbols to use in the benchmark. capital : int The amount of money available for trading. start : datetime.datetime The desired start date for the benchmark. end : datetime.datetime The desired end date for the benchmark. dir_name : str, optional The leaf data dir name (default is 'data'). use_adj : bool, optional True to adjust prices for dividends and splits. use_continuous_calendar: bool, optional True if your timeseries has data for all seven days a week, and you want to backtest trading every day, including weekends. If this value is True, then `force_stock_market_calendar` is set to False (default is False). force_stock_market_calendar : bool, optional True forces use of stock market calendar on timeseries. Normally, you don't need to do this. This setting is intended to transform a continuous timeseries into a weekday timeseries. If this value is True, then `use_continuous_calendar` is set to False. ts : pd.DataFrame The timeseries of the symbol used in backtest. rlog : pd.DataFrame The raw trade log. tlog : pd.DataFrame The trade log. dbal : pd.DataFrame The daily balance. stats : pd.Series The statistics for the benchmark. portfolio : pf.Portfolio The portfolio. """ # If symbols is not a list, cast it to a list. if not isinstance(symbols, list): symbols = [symbols] symbols = list(set(symbols)) self.symbols = symbols self.capital = capital self.start = start self.end = end self.dir_name = dir_name self.use_adj = use_adj self.use_continuous_calendar = use_continuous_calendar self.force_stock_market_calendar = force_stock_market_calendar self.ts = None self.rlog = None self.tlog = None self.dbal = None self.stats = None self.portfolio = None def _algo(self): """ This is a general asset allocation stategy. Invest an equal percent in each investment option and rebalance every year. """ trade.TradeLog.cash = self.capital trade.TradeLog.margin = trade.Margin.CASH # These dicts are used to track close and weights for # each symbol in portfolio weights = {} weight = 1 / len(self.portfolio.symbols) weights = {symbol:weight for symbol in self.portfolio.symbols} # Trading algorithm for i, row in enumerate(self.ts.itertuples()): end_flag = utility.is_last_row(self.ts, i) start_flag = (i==0) # Buy on first trading day # Rebalance on the first trading day of each year # Close all positions on last trading day if row.first_doty or end_flag or start_flag: # If last row, then zero out all weights. if end_flag: weights = utility.set_dict_values(weights, 0) # Adjust weights of all symbols in portfolio self.portfolio.adjust_percents(row, weights, field='close') # Record daily balance. self.portfolio.record_daily_balance(row) def run(self): """ Run the strategy. """ self.portfolio = portfolio.Portfolio() self.ts = self.portfolio.fetch_timeseries( self.symbols, self.start, self.end, fields=['close'], dir_name=self.dir_name, use_adj=self.use_adj, use_continuous_calendar=self.use_continuous_calendar, force_stock_market_calendar=self.force_stock_market_calendar) # Add calendar columns self.ts = self.portfolio.calendar(self.ts) self.ts, self.start = self.portfolio.finalize_timeseries(self.ts, self.start) self.portfolio.init_trade_logs(self.ts) self._algo() self._get_logs() self._get_stats() def _get_logs(self): """ Get the logs. """ self.rlog, self.tlog, self.dbal = self.portfolio.get_logs() def _get_stats(self): """ Get the stats. """ self.stats = pfstatistics.stats(self.ts, self.tlog, self.dbal, self.capital)Methods
def run(self)-
Run the strategy.
Expand source code
def run(self): """ Run the strategy. """ self.portfolio = portfolio.Portfolio() self.ts = self.portfolio.fetch_timeseries( self.symbols, self.start, self.end, fields=['close'], dir_name=self.dir_name, use_adj=self.use_adj, use_continuous_calendar=self.use_continuous_calendar, force_stock_market_calendar=self.force_stock_market_calendar) # Add calendar columns self.ts = self.portfolio.calendar(self.ts) self.ts, self.start = self.portfolio.finalize_timeseries(self.ts, self.start) self.portfolio.init_trade_logs(self.ts) self._algo() self._get_logs() self._get_stats()
class Strategy (symbols, capital, start, end, dir_name='data', use_adj=False, use_continuous_calendar=False, force_stock_market_calendar=False)-
Portfolio Benchmark for comparison to a strategy.
Initialize instance variables.
Parameters
symbols:strorlistofstr- The symbol(s) to use in the benchmark.
capital:int- The amount of money available for trading.
start:datetime.datetime- The desired start date for the benchmark.
end:datetime.datetime- The desired end date for the benchmark.
dir_name:str, optional- The leaf data dir name (default is 'data').
use_adj:bool, optional- True to adjust prices for dividends and splits (default is False).
use_continuous_calendar:bool, optional- True if your timeseries has data for all seven days a week,
and you want to backtest trading every day, including weekends.
If this value is True, then
force_stock_market_calendaris set to False (default is False). force_stock_market_calendar:bool, optional- True forces use of stock market calendar on timeseries.
Normally, you don't need to do this.
This setting is intended
to transform a continuous timeseries into a weekday timeseries.
If this value is True, then
use_continuous_calendaris set to False.
Attributes
symbols:listofstr- The symbols to use in the benchmark.
capital:int- The amount of money available for trading.
start:datetime.datetime- The desired start date for the benchmark.
end:datetime.datetime- The desired end date for the benchmark.
dir_name:str, optional- The leaf data dir name (default is 'data').
use_adj:bool, optional- True to adjust prices for dividends and splits.
use_continuous_calendar:bool, optional- True if your timeseries has data for all seven days a week,
and you want to backtest trading every day, including weekends.
If this value is True, then
force_stock_market_calendaris set to False (default is False). force_stock_market_calendar:bool, optional- True forces use of stock market calendar on timeseries.
Normally, you don't need to do this.
This setting is intended
to transform a continuous timeseries into a weekday timeseries.
If this value is True, then
use_continuous_calendaris set to False. ts:pd.DataFrame- The timeseries of the symbol used in backtest.
rlog:pd.DataFrame- The raw trade log.
tlog:pd.DataFrame- The trade log.
dbal:pd.DataFrame- The daily balance.
stats:pd.Series- The statistics for the benchmark.
portfolio:pf.Portfolio- The portfolio.
Expand source code
class Benchmark: """ Portfolio Benchmark for comparison to a strategy. """ def __init__(self, symbols, capital, start, end, dir_name='data', use_adj=False, use_continuous_calendar=False, force_stock_market_calendar=False): """ Initialize instance variables. Parameters ---------- symbols : str or list of str The symbol(s) to use in the benchmark. capital : int The amount of money available for trading. start : datetime.datetime The desired start date for the benchmark. end : datetime.datetime The desired end date for the benchmark. dir_name : str, optional The leaf data dir name (default is 'data'). use_adj : bool, optional True to adjust prices for dividends and splits (default is False). use_continuous_calendar: bool, optional True if your timeseries has data for all seven days a week, and you want to backtest trading every day, including weekends. If this value is True, then `force_stock_market_calendar` is set to False (default is False). force_stock_market_calendar : bool, optional True forces use of stock market calendar on timeseries. Normally, you don't need to do this. This setting is intended to transform a continuous timeseries into a weekday timeseries. If this value is True, then `use_continuous_calendar` is set to False. Attributes ---------- symbols : list of str The symbols to use in the benchmark. capital : int The amount of money available for trading. start : datetime.datetime The desired start date for the benchmark. end : datetime.datetime The desired end date for the benchmark. dir_name : str, optional The leaf data dir name (default is 'data'). use_adj : bool, optional True to adjust prices for dividends and splits. use_continuous_calendar: bool, optional True if your timeseries has data for all seven days a week, and you want to backtest trading every day, including weekends. If this value is True, then `force_stock_market_calendar` is set to False (default is False). force_stock_market_calendar : bool, optional True forces use of stock market calendar on timeseries. Normally, you don't need to do this. This setting is intended to transform a continuous timeseries into a weekday timeseries. If this value is True, then `use_continuous_calendar` is set to False. ts : pd.DataFrame The timeseries of the symbol used in backtest. rlog : pd.DataFrame The raw trade log. tlog : pd.DataFrame The trade log. dbal : pd.DataFrame The daily balance. stats : pd.Series The statistics for the benchmark. portfolio : pf.Portfolio The portfolio. """ # If symbols is not a list, cast it to a list. if not isinstance(symbols, list): symbols = [symbols] symbols = list(set(symbols)) self.symbols = symbols self.capital = capital self.start = start self.end = end self.dir_name = dir_name self.use_adj = use_adj self.use_continuous_calendar = use_continuous_calendar self.force_stock_market_calendar = force_stock_market_calendar self.ts = None self.rlog = None self.tlog = None self.dbal = None self.stats = None self.portfolio = None def _algo(self): """ This is a general asset allocation stategy. Invest an equal percent in each investment option and rebalance every year. """ trade.TradeLog.cash = self.capital trade.TradeLog.margin = trade.Margin.CASH # These dicts are used to track close and weights for # each symbol in portfolio weights = {} weight = 1 / len(self.portfolio.symbols) weights = {symbol:weight for symbol in self.portfolio.symbols} # Trading algorithm for i, row in enumerate(self.ts.itertuples()): end_flag = utility.is_last_row(self.ts, i) start_flag = (i==0) # Buy on first trading day # Rebalance on the first trading day of each year # Close all positions on last trading day if row.first_doty or end_flag or start_flag: # If last row, then zero out all weights. if end_flag: weights = utility.set_dict_values(weights, 0) # Adjust weights of all symbols in portfolio self.portfolio.adjust_percents(row, weights, field='close') # Record daily balance. self.portfolio.record_daily_balance(row) def run(self): """ Run the strategy. """ self.portfolio = portfolio.Portfolio() self.ts = self.portfolio.fetch_timeseries( self.symbols, self.start, self.end, fields=['close'], dir_name=self.dir_name, use_adj=self.use_adj, use_continuous_calendar=self.use_continuous_calendar, force_stock_market_calendar=self.force_stock_market_calendar) # Add calendar columns self.ts = self.portfolio.calendar(self.ts) self.ts, self.start = self.portfolio.finalize_timeseries(self.ts, self.start) self.portfolio.init_trade_logs(self.ts) self._algo() self._get_logs() self._get_stats() def _get_logs(self): """ Get the logs. """ self.rlog, self.tlog, self.dbal = self.portfolio.get_logs() def _get_stats(self): """ Get the stats. """ self.stats = pfstatistics.stats(self.ts, self.tlog, self.dbal, self.capital)Methods
def run(self)-
Run the strategy.
Expand source code
def run(self): """ Run the strategy. """ self.portfolio = portfolio.Portfolio() self.ts = self.portfolio.fetch_timeseries( self.symbols, self.start, self.end, fields=['close'], dir_name=self.dir_name, use_adj=self.use_adj, use_continuous_calendar=self.use_continuous_calendar, force_stock_market_calendar=self.force_stock_market_calendar) # Add calendar columns self.ts = self.portfolio.calendar(self.ts) self.ts, self.start = self.portfolio.finalize_timeseries(self.ts, self.start) self.portfolio.init_trade_logs(self.ts) self._algo() self._get_logs() self._get_stats()