def backtest(
strategy,
data, # Treated as csv path is str, and dataframe of pd.DataFrame
commission=COMMISSION_PER_TRANSACTION,
init_cash=INIT_CASH,
plot=True,
verbose=True,
sort_by="rnorm",
sentiments=None,
strats=None, # Only used when strategy = "multi"
data_format=None, # No longer needed but will leave for now to warn removal in a coming release
return_history=False,
channel=None,
symbol=None,
allow_short=False,
short_max=1.5,
**kwargs):
"""Backtest financial data with a specified trading strategy
Parameters
----------------
strategy : str or an instance of `fastquant.strategies.base.BaseStrategy`
see list of accepted strategy keys below
data : pandas.DataFrame
dataframe with at least close price indexed with time
commission : float
commission per transaction [0, 1]
init_cash : float
initial cash (currency implied from `data`)
plot : bool
show plot backtrader (disabled if `strategy`=="multi")
sort_by : str
sort result by given metric (default='rnorm')
sentiments : pandas.DataFrame
df of sentiment [0, 1] indexed by time (applicable if `strategy`=='senti')
strats : dict
dictionary of strategy parameters (applicable if `strategy`=='multi')
return_history : bool
return history of transactions (i.e. buy and sell timestamps) (default=False)
channel : str
Channel to be used for last day notification - e.g. "slack" (default=None)
verbose : int
Verbose can take values: [-1, 0, 1, 2], with increasing levels of verbosity (default=0).
symbol : str
Symbol to be referenced in the channel notification if not None (default=None)
{0}
"""
if data_format:
errmsg = "Warning: data_format argument is no longer needed since formatting is now purely automated based on column names!"
errmsg += "\nWe will be removing this argument in a coming release!"
warnings.warn(errmsg, DeprecationWarning)
print(errmsg)
# Setting inital support for 1 cpu
# Return the full strategy object to get all run information
cerebro = bt.Cerebro(stdstats=False, maxcpus=1, optreturn=False)
cerebro.addobserver(bt.observers.Broker)
cerebro.addobserver(bt.observers.Trades)
cerebro.addobserver(bt.observers.BuySell)
# Convert all non iterables and strings into lists
kwargs = {
k: v if isinstance(v, Iterable) and not isinstance(v, str) else [v]
for k, v in kwargs.items()
}
strat_names = []
strat_name = None
if strategy == "multi" and strats is not None:
for strat, params in strats.items():
cerebro.optstrategy(STRATEGY_MAPPING[strat],
init_cash=[init_cash],
transaction_logging=[verbose],
commission=commission,
channel=None,
symbol=None,
allow_short=allow_short,
short_max=short_max,
**params)
strat_names.append(strat)
else:
# Allow instance of BaseStrategy or from the predefined mapping
if not isinstance(strategy, str) and issubclass(strategy, bt.Strategy):
strat_name = str(strategy)
else:
strat_name = strategy
strategy = STRATEGY_MAPPING[strategy]
cerebro.optstrategy(strategy,
init_cash=[init_cash],
transaction_logging=[verbose],
commission=commission,
channel=None,
symbol=None,
allow_short=allow_short,
short_max=short_max,
**kwargs)
strat_names.append(strat_name)
# Apply Total, Average, Compound and Annualized Returns calculated using a logarithmic approach
cerebro.addanalyzer(btanalyzers.Returns, _name="returns")
cerebro.addanalyzer(btanalyzers.SharpeRatio, _name="mysharpe")
cerebro.addanalyzer(btanalyzers.DrawDown, _name="drawdown")
cerebro.addanalyzer(btanalyzers.TimeDrawDown, _name="timedraw")
cerebro.broker.setcommission(commission=commission)
# Treat `data` as a path if it's a string; otherwise, it's treated as a pandas dataframe
if isinstance(data, str):
if verbose:
print("Reading path as pandas dataframe ...")
# Rename dt to datetime
data = pd.read_csv(data, header=0, parse_dates=["dt"])
if strat_name == "sentiment":
# initialize series for sentiments
senti_series = pd.Series(sentiments,
name="sentiment_score",
dtype=float)
# join and reset the index for dt to become the first column
data = data.merge(senti_series,
left_index=True,
right_index=True,
how="left")
data = data.reset_index()
# If a `close` column exists but an `open` column doesn't, create a new `open` column with the same values as the `close` column
# This is for easier handling of next day trades (w/ the assumption that next day open is equal to current day close)
if "close" in data.columns and "open" not in data.columns:
data["open"] = data.close.shift().values
# If data has `dt` as the index and `dt` or `datetime` are not already columns, set `dt` as the first column
# This means `backtest` supports the dataframe whether `dt` is the index or a column
if len(set(["dt", "datetime"]).intersection(data.columns)) == 0:
if data.index.name == "dt":
data = data.reset_index()
# If the index is a datetime index, set this as the datetime column
elif isinstance(data.index, pd.DatetimeIndex):
data.index.name = "dt"
data = data.reset_index()
# Rename "dt" column to "datetime" to match the formal alias
data = data.rename(columns={"dt": "datetime"})
data["datetime"] = pd.to_datetime(data.datetime)
numeric_cols = [col for col in data.columns if is_numeric_dtype(data[col])]
params_tuple = tuple([(col, i) for i, col in enumerate(data.columns)
if col in numeric_cols + ["datetime"]])
default_cols = [c for c, _ in DEFAULT_PANDAS]
non_default_numeric_cols = tuple(
[col for col, _ in params_tuple if col not in default_cols])
class CustomData(bt.feeds.PandasData):
"""
Data feed that includes all the columns in the input dataframe
"""
# Need to make sure that the new lines don't overlap w/ the default lines already in PandasData
lines = non_default_numeric_cols
# automatically handle parameter with -1
# add the parameter to the parameters inherited from the base class
params = params_tuple
# extend the dataframe with sentiment score
if strat_name == "sentiment":
data_format_dict = tuple_to_dict(params_tuple)
# create CustomData which inherits from PandasData
pd_data = CustomData(dataname=data, **data_format_dict)
else:
data_format_dict = tuple_to_dict(params_tuple)
pd_data = CustomData(dataname=data, **data_format_dict)
cerebro.adddata(pd_data)
cerebro.broker.setcash(init_cash)
# Allows us to set buy price based on next day closing
# (technically impossible, but reasonable assuming you use all your money to buy market at the end of the next day)
cerebro.broker.set_coc(True)
if verbose:
print("Starting Portfolio Value: %.2f" % cerebro.broker.getvalue())
# clock the start of the process
tstart = time.time()
stratruns = cerebro.run()
# clock the end of the process
tend = time.time()
params = []
metrics = []
if verbose:
print("==================================================")
print("Number of strat runs:", len(stratruns))
print("Number of strats per run:", len(stratruns[0]))
print("Strat names:", strat_names)
order_history_dfs = []
periodic_history_dfs = []
indicator_history_dfs = []
for strat_idx, stratrun in enumerate(stratruns):
strats_params = {}
if verbose:
print("**************************************************")
for i, strat in enumerate(stratrun):
# Get indicator history
indicators = strat.getindicators()
indicators_dict = {
ind.plotlabel() if hasattr(ind, "plotlabel") else
"indicator{}".format(i): ind.lines[0].array
for i, ind in enumerate(indicators)
}
indicators_df = pd.DataFrame(indicators_dict)
indicators_df.insert(0, "dt", data["datetime"].values)
strat_name = strat_names[i]
p_raw = strat.p._getkwargs()
p, selected_p = {}, {}
for k, v in p_raw.items():
if k not in ["periodic_logging", "transaction_logging"]:
# Make sure the parameters are mapped to the corresponding strategy
if strategy == "multi":
key = ("{}.{}".format(strat_name, k)
if k not in GLOBAL_PARAMS else k)
# make key with format: e.g. smac.slow_period40_fast_period10
if k in strats[strat_name]:
selected_p[k] = v
pkeys = "_".join(
["{}{}".format(*i) for i in selected_p.items()])
history_key = "{}.{}".format(strat_name, pkeys)
else:
key = k
# make key with format: e.g. slow_period40_fast_period10
if k in kwargs.keys():
selected_p[k] = v
history_key = "_".join(
["{}{}".format(*i) for i in selected_p.items()])
p[key] = v
strats_params = {**strats_params, **p}
if return_history:
# columns are decided in log method of BaseStrategy class in base.py
order_history_df = strat.order_history_df
order_history_df["dt"] = pd.to_datetime(order_history_df.dt)
# combine rows with identical index
# history_df = order_history_df.set_index('dt').dropna(how='all')
# history_dfs[history_key] = order_history_df.stack().unstack().astype(float)
order_history_df.insert(0, "strat_name", history_key)
order_history_df.insert(0, "strat_id", strat_idx)
order_history_dfs.append(order_history_df)
periodic_history_df = strat.periodic_history_df
periodic_history_df["dt"] = pd.to_datetime(
periodic_history_df.dt)
periodic_history_df.insert(0, "strat_name", history_key)
periodic_history_df.insert(0, "strat_id", strat_idx)
periodic_history_df[
"return"] = periodic_history_df.portfolio_value.pct_change(
)
periodic_history_dfs.append(periodic_history_df)
indicators_df.insert(0, "strat_name", history_key)
indicators_df.insert(0, "strat_id", strat_idx)
indicator_history_dfs.append(indicators_df)
# We run metrics on the last strat since all the metrics will be the same for all strats
returns = strat.analyzers.returns.get_analysis()
sharpe = strat.analyzers.mysharpe.get_analysis()
drawdown = strat.analyzers.drawdown.get_analysis()
timedraw = strat.analyzers.timedraw.get_analysis()
# Combine dicts for returns and sharpe
m = {
**returns,
**drawdown,
**timedraw,
**sharpe,
"pnl": strat.pnl,
"final_value": strat.final_value,
}
params.append(strats_params)
metrics.append(m)
if verbose:
print("--------------------------------------------------")
print(strats_params)
print(returns)
print(sharpe)
print(drawdown)
print(timedraw)
params_df = pd.DataFrame(params)
# Set the index as a separate strat id column, so that we retain the information after sorting
strat_ids = pd.DataFrame({"strat_id": params_df.index.values})
metrics_df = pd.DataFrame(metrics)
# Get indices based on `sort_by` metric
optim_idxs = np.argsort(metrics_df[sort_by].values)[::-1]
sorted_params_df = params_df.iloc[optim_idxs].reset_index(drop=True)
sorted_metrics_df = metrics_df.iloc[optim_idxs].reset_index(drop=True)
sorted_strat_ids = strat_ids.iloc[optim_idxs].reset_index(drop=True)
sorted_combined_df = pd.concat(
[sorted_strat_ids, sorted_params_df, sorted_metrics_df], axis=1)
# print out the result
print("Time used (seconds):", str(tend - tstart))
# Save optimal parameters as dictionary
optim_params = sorted_params_df.iloc[0].to_dict()
optim_metrics = sorted_metrics_df.iloc[0].to_dict()
print("Optimal parameters:", optim_params)
print("Optimal metrics:", optim_metrics)
if plot and strategy != "multi":
has_volume = (data_format_dict["volume"] is not None
if "volume" in data_format_dict.keys() else False)
# Plot only with the optimal parameters when multiple strategy runs are required
if params_df.shape[0] == 1:
# This handles the Colab Plotting
# Simple Check if we are in Colab
try:
from google.colab import drive
iplot = False
except Exception:
iplot = True
cerebro.plot(volume=has_volume, figsize=(30, 15), iplot=iplot)
else:
print("=============================================")
print("Plotting backtest for optimal parameters ...")
backtest(
strategy,
data, # Treated as csv path is str, and dataframe of pd.DataFrame
plot=plot,
verbose=verbose,
sort_by=sort_by,
**optim_params)
# drop extra columns #248
if (len(
set(["channel"
"symbol"
]).intersection(sorted_combined_df.columns.values)) == 2):
sorted_combined_df.drop(["channel", "symbol"], axis=1, inplace=True)
if return_history:
order_history = pd.concat(order_history_dfs)
periodic_history = pd.concat(periodic_history_dfs)
indicator_history = pd.concat(indicator_history_dfs)
history_dict = dict(
orders=order_history,
periodic=periodic_history,
indicators=indicator_history,
)
return sorted_combined_df, history_dict
else:
return sorted_combined_df
from mean_reversion import MeanReversion
import datetime as dt
import pandas as pd
import backtrader as bt
import backtrader.feeds as feeds
import backtrader.indicators as btind
import backtrader.analyzers as btanalyzers
import matplotlib
if __name__ == '__main__':
startTime = dt.datetime.now()
strategy = MeanReversion
startcash = 10 #BTC
cerebro = bt.Cerebro(runonce=False, optreturn=False)
cerebro.addstrategy(strategy)
# Used when you don't have the csv data and need to pull from the internet
# one_year = dt.timedelta(days=365)
# days_100 = dt.timedelta(days=100)
# days_150 = dt.timedelta(days=150)
# days_30 = dt.timedelta(days=30)
# month_6 = dt.timedelta(days=180)
# start = dt.datetime.now() - days_100
# pipeline = CoinbasePipeline('BTC-USD',start=start, granularity=3600)
# dataframe = pipeline.get_data()
#
dataframe = pd.read_csv("./hist-data/ETH-BTC-100d-1hr-12-16.csv",
index_col="datetime",
parse_dates=['datetime'])
def backtest_strategy(df: pd.DataFrame,
strategy: Type[BaseStrategy],
date_from: datetime = None,
date_to: datetime = None,
perc_size=10,
commission=0.00075,
start_cash=1000.0,
returns_analyzer=False,
use_replay=False,
df_resample=None,
timeframe=bt.TimeFrame.Minutes,
compression=60,
output=None,
*args,
**kwargs) -> Tuple[bt.Cerebro, Any]:
cerebro = bt.Cerebro()
if date_from is None:
date_from = df.index.min().to_pydatetime()
if date_to is None:
date_to = df.index.max().to_pydatetime()
# data
data = backtrader_data(dataname=df, fromdate=date_from, todate=date_to)
if use_replay:
cerebro.replaydata(data, timeframe=timeframe, compression=compression)
else:
cerebro.adddata(data)
if df_resample is not None:
data_resample = BacktestData(dataname=df_resample)
cerebro.resampledata(data_resample,
timeframe=timeframe,
compression=compression)
# strategy
cerebro.addstrategy(strategy, *args, **kwargs)
# analysers
cerebro.addanalyzer(bt.analyzers.SharpeRatio,
_name='sharpe_ratio',
timeframe=bt.TimeFrame.Days)
if returns_analyzer:
cerebro.addanalyzer(bt.analyzers.LogReturnsRolling,
_name='log_returns_rolling')
cerebro.addanalyzer(bt.analyzers.Returns, _name='returns')
cerebro.addanalyzer(bt.analyzers.DrawDown, _name='draw_down')
cerebro.addanalyzer(bt.analyzers.SQN, _name='sqn')
cerebro.addanalyzer(bt.analyzers.VWR, _name='vwr')
cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name='trade')
cerebro.addanalyzer(bt.analyzers.GrossLeverage, _name='gross_leverage')
# cerebro.addanalyzer(bt.analyzers.Calmar, _name='calmar')
# params
cerebro.broker.setcash(start_cash)
cerebro.broker.setcommission(commission=commission)
cerebro.addsizer(bt.sizers.PercentSizer, percents=perc_size)
cerebro.signal_accumulate(False)
cerebro.signal_concurrent(False)
if output is None:
if 'verbose' in kwargs and kwargs['verbose']:
import sys
output = sys.stdout
else:
import os
output = open(os.devnull, "w")
cerebro.addwriter(bt.WriterFile, csv=True, out=output)
# run
thestrats = cerebro.run(runonce=False)
return cerebro, thestrats
def testStrat(self):
startcash = 100000
cerebro = bt.Cerebro()
strat_params = [
('AGG', 10),
('IEMG', 10),
('IAU', 10),
('MBB', 10),
('IXUS', 10),
('IJH', 10),
('IJR', 10),
('IVW', 10),
('IUSV', 10),
('IGV', 10),
]
symbol_list = [x[0] for x in strat_params]
dont_plot = []
# Add our strategy
cerebro.addstrategy(RebalanceStrategy, assets=strat_params)
is_first=True
for tick in symbol_list:
data = bt.feeds.YahooFinanceData(dataname=tick,
fromdate=datetime(2017, 1, 1),
todate=datetime(2020, 9, 1))
if tick not in dont_plot:
if is_first:
data.plotinfo.plotlinelabels = False
data.plotinfo.plotylimited = False
data.plotinfo.sameaxis = True
data_main_plot = data
is_first = False
else:
#data.compensate(last_data)
data.plotinfo.plotmaster = data_main_plot
data.plotinfo.plotlinelabels = False
data.plotinfo.plotylimited = False
data.plotinfo.sameaxis = True
else:
data.plotinfo.plot = False
cerebro.adddata(data)
#last_data = copy.deepcopy(data)
# Set our desired cash start and run settings
cerebro.broker.setcash(startcash)
cerebro.addanalyzer(bta.SharpeRatio, _name='mysharpe')
cerebro.addanalyzer(bta.DrawDown, _name='drawdown')
cerebro.addanalyzer(bta.TimeDrawDown, _name='drawdowntime')
cerebro.addanalyzer(bta.PositionsValue, _name='positionsValue', cash=True) ##
cerebro.addanalyzer(bta.Returns, _name='returns')
cerebro.addanalyzer(bta.Transactions, _name='trxns')
cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name="ta")
cerebro.broker.set_checksubmit(False)
cerebro.addobserver(AcctValue)
cerebro.addobservermulti(bto.BuySell, plotlinelabels=False, plotname=False)
# Run the backtest
results = cerebro.run(stdstats=False)
cerebro.plot(iplot=True, volume=False)
# Get final portfolio Value
portfolio_value = cerebro.broker.getvalue()
pnl = portfolio_value - startcash
#self.assertEquals(pnl, 78239.86999999997)
# Analyze Results
analyzers = results[0].analyzers
trxns = analyzers.trxns.get_analysis()
trades = analyzers.ta.get_analysis()
tot_returns = analyzers.returns.get_analysis()
sharpe = analyzers.mysharpe.get_analysis()
positionvals = analyzers.positionsValue.get_analysis()
position_hist = pd.DataFrame(positionvals).T
position_hist.columns = symbol_list + ["Cash"]
portfoliovals = position_hist.sum(axis=1)
returns = np.log(portfoliovals / portfoliovals.shift(1))[1:]
max_drawdown = analyzers.drawdown.get_analysis()
# Get SPY returns to compare
spy_st = pd.Series(returns.cumsum()).index[0]
spy_end = pd.Series(returns.cumsum()).index[-1]
SPY_df = web.DataReader('SPY', 'yahoo', spy_st, spy_end)
SPY_returns = pd.Series(np.log(SPY_df.Close / SPY_df.Close.shift(1)).cumsum())
# Get SPY $ Portfolio Value
SPY_portfolio_values = (startcash / SPY_df.Close[0]) * SPY_df.Close
# Plot Returns Comparison
plt.clf()
pd.Series(returns.cumsum()).plot(label="Portfolio Returns")
pd.Series(SPY_returns).plot(label="SPY Returns")
plt.title("Portfolio Returns")
plt.ylabel("Log Returns")
plt.legend()
plt.show()
# Plot Returns Comparison
plt.clf()
pd.Series(portfoliovals).plot(label="Portfolio $ Value")
pd.Series(SPY_portfolio_values).plot(label="SPY Portfolio $ Value")
plt.title("Portfolio Value")
plt.ylabel("Dollars")
plt.legend()
plt.show()
# Print Out the Backtest Analysis
# for x in results[0].analyzers:
# x.print()
print('Final Portfolio Value: ${}'.format(portfolio_value))
print('Portfololio PnL: ${}'.format(pnl))
print("Portfolio Total Log Returns: {}".format(tot_returns["rtot"]))
print("Portfolio Sharpe Ratio: {}".format(sharpe['sharperatio']))
print(
"Portfolio Max Drawdown: ${}, Time: {} days".format(max_drawdown.max["moneydown"], max_drawdown.max["len"]))
print("---Compare to SPY---")
print("SPY Portfolio Value: ${}".format(SPY_portfolio_values[-1]))
print("SPY Portfolio Pnl: ${}".format(SPY_portfolio_values[-1] - startcash))
print("SPY Total Log Returns: {}".format(SPY_returns[-1]))
# Get Round Trip Trade Info
printTradeAnalysis(trades)
print("Portfolio History:")
print(position_hist)
def runstrat(args=None):
args = parse_args(args)
cerebro = bt.Cerebro()
cerebro.broker.set_cash(args.cash)
comminfo = bt.commissions.CommInfo_Stocks_Perc(commission=args.commperc,
percabs=True)
cerebro.broker.addcommissioninfo(comminfo)
#cerebro.broker.setcommission(commission=0.0)
dkwargs = dict()
if args.fromdate is not None:
fromdate = datetime.datetime.strptime(args.fromdate, '%Y-%m-%d')
dkwargs['fromdate'] = fromdate
if args.todate is not None:
todate = datetime.datetime.strptime(args.todate, '%Y-%m-%d')
dkwargs['todate'] = todate
# if dataset is None, args.data has been given
dataname = DATASETS.get(args.dataset, args.data)
if args.datafile:
dataname = args.datafile
data0 = bt.feeds.GenericCSVData(dataname=dataname,
dtformat=('%Y-%m-%d'),
date=0,
open=1,
high=2,
low=3,
close=4,
volume=5,
openinterest=-1,
**dkwargs)
cerebro.adddata(data0)
cerebro.addstrategy(TheRSIStrategy)
#cerebro.addsizer(FixedPerc, perc=0.96)
cerebro.addsizer(LongOnly)
cerebro.broker.setcash(50000.0)
# Print out the starting conditions
print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
results = cerebro.run()
st0 = results[0]
# Print out the final result
print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
if args.plot:
pkwargs = dict(style='bar')
pkwargs = dict()
if args.plot is not True: # evals to True but is not True
npkwargs = eval('dict(' + args.plot + ')') # args were passed
pkwargs.update(npkwargs)
cerebro.plot(**pkwargs)
def BackTesting(fromdate=None):
# Datafeed from file
class MyCSVData(bt.feeds.GenericCSVData):
params = (
('nullvalue', float('NaN')),
('dtformat', '%Y-%m-%d'),
('datetime', 0),
('time', -1),
('open', 2),
('high', -1),
('low', -1),
('close', 1),
('volume', -1),
('openinterest', 3),
)
# Create a Stratey
class TestStrategy(bt.Strategy):
def __init__(self):
# Keep a reference to the "close" line in the data[0] dataseries
self.dataclose = self.datas[0].close
self.dataopen = self.datas[0].open
self.openinterest = self.datas[0].openinterest
# To keep track of pending orders
self.order = None
self.target = 0
def notify_order(self, order):
if order.status in [order.Submitted, order.Accepted]:
# Buy/Sell order submitted/accepted to/by broker - Nothing to do
return
# Check if an order has been completed
# Attention: broker could reject order if not enough cash
if order.status in [order.Completed]:
if order.isbuy():
#self.log('BUY EXECUTED, %.2f' % order.executed.price)
self.ExecText = 'BUY EXECUT, '
elif order.issell():
#self.log('SELL EXECUTED, %.2f' % order.executed.price)
self.ExecText = 'SEL EXECUT, '
self.bar_executed = len(self)
elif order.status in [
order.Canceled, order.Margin, order.Rejected
]:
#self.log('Order Canceled/Margin/Rejected')
self.ExecText = 'ORD CANCAL, '
# Write down: no pending order
self.order = None
def next(self):
# Simply log the closing price of the series from the reference
PriceText = ', Open: %.2f, Close: %.2f, Signal: %.0f => ' % (
self.dataopen[0], self.dataclose[0], self.openinterest[0])
OperText = ' '
CashValue = 'Cash: %.2f, ' % cerebro.broker.getcash()
PortoValue = 'Porto: %.2f' % cerebro.broker.getvalue()
dt = self.datas[0].datetime.date(0)
# Check if an order is pending ... if yes, we cannot send a 2nd one
if self.order:
return
# Check if we are in the market
if self.openinterest[0] == 1:
if self.position.size <= 0:
OperText = 'BUY CREATE, '
# Keep track of the created order to avoid a 2nd order
self.target = cerebro.broker.getvalue(
) * 0.95 / self.dataopen
self.order = self.order_target_size(target=self.target)
if self.openinterest[0] == -1:
if self.position.size >= 0:
OperText = 'SEL CREATE, '
# Keep track of the created order to avoid a 2nd order
self.target = -cerebro.broker.getvalue(
) * 0.95 / self.dataopen
self.order = self.order_target_size(target=self.target)
if self.openinterest[0] == 0:
if self.position.size != 0:
OperText = 'EMPTY CREATE, '
# Keep track of the created order to avoid a 2nd order
self.target = 0
self.order = self.order_target_size(target=self.target)
# Print the line
LineText = str(dt.isoformat()) + str(PriceText) + str(
OperText) + str(CashValue) + str(PortoValue)
PrintLog(logfile, 'a', LineText)
#log('%s, %s' % (self.dt.isoformat(), LineText))
# Create a cerebro entity
cerebro = bt.Cerebro()
# Add a strategy
cerebro.addstrategy(TestStrategy)
# Datas are in a subfolder of the samples. Need to find where the script is
# because it could have been called from anywhere
modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
datapath = os.path.join(modpath, sigfile)
# Create a Data Feed
data = MyCSVData(dataname=datapath, fromdate=fromdate)
# Add the Data Feed to Cerebro
cerebro.adddata(data)
# Set our desired cash start
cerebro.broker.setcash(10000.0)
# Set the commission - 0.1% ... divide by 100 to remove the %
cerebro.broker.setcommission(commission=0.001)
# Print out the starting conditions
LineText = ('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
PrintLog(logfile, 'w', LineText)
print(LineText)
# Run over everything
cerebro.run()
# Print out the final result
FinalValue = cerebro.broker.getvalue()
LineText = ('Final Portfolio Value: %.2f' % FinalValue)
PrintLog(logfile, 'a', LineText)
print(LineText)
# Plot the result
figure = cerebro.plot(volume=False)[0][0]
figure.savefig(figfile)
return (FinalValue)
def main():
##################################################################################################
# Setup logger and output dir #
##################################################################################################
output_dir = config.output_dir
if output_dir is None:
output_dir = './jupyter_py/output/backtest-{}'.format(
get_current_time())
if not os.path.exists(output_dir):
pathlib.Path(output_dir).mkdir(parents=True, exist_ok=True)
# Setup logger
LogHelper.setup(log_path='{}/backtesting.log'.format(output_dir),
log_level=logging.INFO)
_logger = logging.getLogger(__name__)
# Log all paremeters
_logger.info("Backtest parameters: {}".format(vars(config)))
# load data
data = GSTools.load_csv_files(config.data_path)
stk0, stk1 = config.stk0, config.stk1
# check existence of stocks
if (stk0 not in data):
_logger.error("Stock symbol {} does not exist!".format(stk0))
return
if (stk1 not in data):
_logger.error("Stock symbol {} does not exist!".format(stk1))
return
# size requirements
pre_backtest_size = None
if config.strategy_type == "cointegration" or config.strategy_type == "distance":
pre_backtest_size = config.lookback
elif config.strategy_type == "kalman":
pre_backtest_size = config.kalman_estimation_length
# select segment of data that we want
data0, data1 = data[stk0].set_index("date"), data[stk1].set_index("date")
start_date_dt = datetime.strptime(config.backtest_start, "%Y-%m-%d").date()
end_date_dt = datetime.strptime(config.backtest_end, "%Y-%m-%d").date()
data0 = data0[:start_date_dt].tail(pre_backtest_size).append(
data0[start_date_dt:end_date_dt])
data1 = data1[:start_date_dt].tail(pre_backtest_size).append(
data1[start_date_dt:end_date_dt])
data0 = data0.reset_index()
data1 = data1.reset_index()
# initialize cerebro
cerebro = bt.Cerebro()
# Create data feeds
data0 = bt.feeds.PandasData(dataname=data0,
timeframe=(bt.TimeFrame.Days),
datetime=0,
open=1,
close=4)
data1 = bt.feeds.PandasData(dataname=data1,
timeframe=(bt.TimeFrame.Days),
datetime=0,
open=1,
close=4)
# add data feeds to cerebro
cerebro.adddata(data0)
cerebro.adddata(data1)
# Add the strategy
if config.strategy_type == "distance":
cerebro.addstrategy(DistStrategy,
stk0_symbol=stk0,
stk1_symbol=stk1,
lookback=config.lookback,
max_lookback=pre_backtest_size,
enter_threshold_size=config.enter_threshold,
exit_threshold_size=config.exit_threshold,
loss_limit=config.loss_limit,
consider_borrow_cost=True,
consider_commission=False,
print_msg=True,
print_transaction=True)
elif config.strategy_type == "cointegration":
cerebro.addstrategy(CointStrategy,
stk0_symbol=stk0,
stk1_symbol=stk1,
lookback=config.lookback,
max_lookback=pre_backtest_size,
enter_threshold_size=config.enter_threshold,
exit_threshold_size=config.exit_threshold,
loss_limit=config.loss_limit,
consider_borrow_cost=True,
consider_commission=True,
print_msg=True,
print_transaction=True)
elif config.strategy_type == "kalman":
cerebro.addstrategy(CointKalmanStrategy,
stk0_symbol=stk0,
stk1_symbol=stk1,
max_lookback=pre_backtest_size,
enter_threshold_size=config.enter_threshold,
exit_threshold_size=config.exit_threshold,
loss_limit=config.loss_limit,
consider_borrow_cost=True,
consider_commission=True,
print_msg=True,
print_transaction=True)
# Add analyzers
cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name='mysharpe')
cerebro.addanalyzer(Metrics, lookback=pre_backtest_size, _name='metrics')
# Add the commission - only stocks like a for each operation
cerebro.broker.setcash(1000000)
# And run it
strat = cerebro.run()
# get MICRO metrics
results_dict = {}
results_dict["pair"] = stk0 + "-" + stk1
results_dict["sharperatio"] = strat[0].analyzers.mysharpe.get_analysis(
)['sharperatio']
results_dict["returnstd"] = strat[0].analyzers.metrics.returns_std()
results_dict["avg_holding_period"] = strat[
0].analyzers.metrics.avg_holding_period
results_dict["n_trades"] = strat[0].analyzers.metrics.n_trades
results_dict["startcash"] = cerebro.getbroker().startingcash
results_dict["endcash"] = cerebro.getbroker().getvalue()
results_dict["profit"] = (
results_dict["endcash"] -
results_dict["startcash"]) / results_dict["startcash"]
_logger.info("[pair-performance]: {}".format(results_dict))
size = int(self.broker.getcash() / self.datas[0].open)
self.log(
f'BUY CREATED --- Size: {size}, Cash:{self.broker.getcash(): .2f},'
f' Open: {self.data_open[0]}, Close: {self.data_close[0]}')
self.buy(size=size)
else:
if self.sell_signal < 0:
self.log(f'SELL CREATED --- Size: {self.position.size}')
self.sell(size=self.position.size)
data = bt.feeds.YahooFinanceCSVData(dataname='AAPL.csv',
fromdate=datetime(2017, 1, 1),
todate=datetime(2020, 4, 30))
cerebro = bt.Cerebro(stdstats=False, cheat_on_open=True)
cerebro.addstrategy(BBand_Strategy)
cerebro.adddata(data)
cerebro.broker.setcash(10000.0)
cerebro.broker.setcommission(commission=0.001)
cerebro.addobserver(bt.observers.BuySell)
cerebro.addobserver(bt.observers.Value)
cerebro.addanalyzer(bt.analyzers.Returns, _name='returns')
cerebro.addanalyzer(bt.analyzers.TimeReturn, _name='time_return')
print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
backtest_result = cerebro.run()
print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
cerebro.plot(style='candlestick', iplot=True, volume=False)
print(backtest_result[0].analyzers.returns.get_analysis())
def main():
# Model Settings
startcash = 500000
momentum_period = 126 #days
num_positions = 20
reserve = 0.05
printlog = False
# Commission and Slippage Settings
commission = 0.0025
db_conn = get_db_conn(p_uid='freedbtech_tradesoft',
p_pwd='tradesoft',
p_host='freedb.tech',
p_database='freedbtech_tradesoft',
p_port=3306)
prices = historical_price(tickers, db_conn)
db_conn.close()
# remove tickers where we have less than 10 years of data.
min_obs = 2520
nobs = prices.groupby(level='ticker').size()
keep = nobs[nobs > min_obs].index
prices = prices.loc[idx[keep, :], :]
# prices.info()
print('Number of tickers with min 10 years history= ',
prices.index.unique(level='ticker'))
print('latest Date: ', prices.loc['ACC'].index[-1])
print('latest Date - 126 Trading days: ', prices.loc['ACC'].index[-126])
from_date = input('start date in format yyyy-mm-dd:')
to_date = input('end date in format yyyy-mm-dd:')
fromdate = datetime.datetime.strptime(from_date, '%Y-%m-%d')
todate = datetime.datetime.strptime(to_date, '%Y-%m-%d')
cerebro = bt.Cerebro(stdstats=False, cheat_on_open=True)
# cerebro.broker.set_coc(True)
cerebro.broker.setcash(startcash)
cerebro.broker.setcommission(commission=commission)
# Add securities as datas1:
for ticker, data in prices.groupby(level=0):
if ticker in tickers:
print(f"Adding ticker: {ticker}")
data = bt.feeds.PandasData(dataname=data.droplevel(level=0),
name=str(ticker),
fromdate=fromdate,
todate=todate,
plot=False)
cerebro.adddata(data)
cerebro.addanalyzer(bt.analyzers.Returns, _name='pfreturn')
cerebro.addanalyzer(bt.analyzers.DrawDown, _name='pfdrawdown')
cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name='pfsharpe')
cerebro.addanalyzer(bt.analyzers.PyFolio, _name='pyfolio')
cerebro.addstrategy(StrategyRiskparity,
momentum_period=momentum_period,
num_positions=num_positions,
printlog=printlog,
reserve=reserve)
print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
# Run the strategy. Results will be output from stop.
results_eq_wts = cerebro.run()
results_eq_wt = results_eq_wts[0]
pyfoliozer = results_eq_wt.analyzers.getbyname('pyfolio')
returns, positions, transactions, gross_lev = pyfoliozer.get_pf_items()
transactions.to_csv('data/transactions.csv')
positions.to_csv('data/positions.csv')
returns.to_csv('data/returns.csv')
# Print out the return
print('\nPortfolio Return:',
results_eq_wt.analyzers.pfreturn.get_analysis())
# Print out the drawdown
print('\nPortfolio Drawdown:',
results_eq_wt.analyzers.pfdrawdown.get_analysis())
# Print out the sharpe
print('\nPortfolio Sharpe ratio:',
results_eq_wt.analyzers.pfsharpe.get_analysis())
# Print out the final result
print('\nFinal Portfolio Value: %.2f' % cerebro.broker.getvalue())
import backtrader
import datetime
from strategy import TestStrategy
cerebro = backtrader.Cerebro()
cerebro.addsizer(backtrader.sizers.FixedSize, stake=1000)
cerebro.broker.set_cash(1000000)
data = backtrader.feeds.YahooFinanceCSVData(
dataname='oracle.csv',
# Do not pass values before this date
fromdate=datetime.datetime(2000, 1, 1),
# Do not pass values after this date
todate=datetime.datetime(2000, 12, 31),
reverse=False)
cerebro.adddata(data)
cerebro.addstrategy(TestStrategy)
print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
cerebro.run()
print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
cerebro.plot()
def backtest(cfg, strategy, plot=False, m_name='Test', **kwargs):
def getReturnAsDataFrame(pfa, bencha):
d = bencha.get_analysis()
t = pfa.get_analysis()
lists = sorted(d.items()) # sorted by key, return a list of tuples
x, y = zip(*lists) # unpack a list of pairs into two tuples
listst = sorted(t.items()) # sorted by key, return a list of tuples
x, y1 = zip(*listst) # unpack a list of pairs into two tuples
dd = {'data':x, 'Benchmark':np.cumsum(y), 'Portfolio':np.cumsum(y1)}
df = pd.DataFrame(dd).set_index('data')
return df
cerebro = bt.Cerebro()
if 'set_coc' in cfg:
cerebro.broker.set_coc(cfg['set_coc'])
if 'set_coo' in cfg:
cerebro.broker.set_coo(cfg['set_coo'])
cerebro.addstrategy(strategy, **kwargs)
for a in cfg['assets']:
if cfg['stocks_df'] is None:
cerebro.adddata(bt.feeds.YahooFinanceData(dataname=a, fromdate=cfg['startd'], todate=cfg['endd'], plot=False))
else:
cerebro.adddata(PandasData(dataname=getStock(cfg['stocks_df'],a)), name=a)
#add benchmark
if 'benchmark' in cfg:
if (cfg['benchmark_df'] is None) and (cfg['stocks_df'] is None):
bm = bt.feeds.YahooFinanceData(dataname=cfg['benchmark'], fromdate=cfg['startd'], todate=cfg['endd'])
else:
bm = PandasData(dataname=cfg['benchmark_df'],name=a)
cerebro.adddata(bm)
cerebro.addanalyzer(bt.analyzers.TimeReturn, timeframe=bt.TimeFrame.Months, data=bm, _name='BenchmarkReturns')
cerebro.addanalyzer(bt.analyzers.TimeReturn, timeframe=bt.TimeFrame.Months, _name='PortfolioReturns')
# Analyzer
cerebro.addanalyzer(btanalyzers.SharpeRatio, riskfreerate=0.0, timeframe=bt.TimeFrame.Months, _name='mysharpe')
cerebro.addanalyzer(btanalyzers.Returns, _name='myreturn')
cerebro.addanalyzer(btanalyzers.DrawDown, _name='mydrawdown')
cerebro.addanalyzer(AllocationHistory, _name='allocationHistory')
cerebro.broker.setcash(cfg['cash'])
print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
thestrats = cerebro.run()
thestrat = thestrats[0]
print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
if plot:
cerebro.plot()
df = pd.DataFrame()
if 'benchmark' in cfg:
df = getReturnAsDataFrame(thestrat.analyzers.getbyname('PortfolioReturns'),
thestrat.analyzers.getbyname('BenchmarkReturns'))
ret = {
m_name : {
'Max_Drawdown':thestrat.analyzers.getbyname('mydrawdown').get_analysis()['max']['drawdown'],
'CAGR':thestrat.analyzers.getbyname('myreturn').get_analysis()['rnorm100'],
'Sharp_Ratio':thestrat.analyzers.getbyname('mysharpe').get_analysis()['sharperatio'],
'Value': cerebro.broker.getvalue(),
}
}
allocation_history = thestrat.analyzers.getbyname('allocationHistory').get_analysis()['allocation']
return (pd.DataFrame.from_dict(ret).T,
cleanWarmUperiod(df),
allocation_history)
def runstrat_main(settings, **kwargs):
# clock the start of the process
tstart = process_time()
args = parse_args(kwargs)
# signal = list(kwargs["signal"].keys())[0]
# Create a cerebro entity
cerebro = bt.Cerebro(stdstats=False, # default kwarg: stdstats=True
# maxcpus=args.maxcpus,
# runonce=not args.no_runonce,
# exactbars=args.exactbars,
# optdatas=not args.no_optdatas,
# optreturn=not args.no_optreturn
)
# Add a strategy
cerebro.addstrategy(strategies.MainStrategy, **settings, **kwargs)
# Get a data source path
datapath = args.data
# Pass it to the backtrader datafeed and add it to the cerebro
data = bt.feeds.PandasData(dataname=pandasdatafeed(datapath, args=args),
fromdate=args.fromdate, # fromdate=args.fromdate,
todate=args.todate) # todate=args.todate)
cerebro.adddata(data)
# Set our desired cash start
cerebro.broker.setcash(args.cash) # cerebro.broker.setcash(100000.0)
# Add a FixedSize sizer according to the stake
cerebro.addsizer(bt.sizers.FixedSize, stake=1)
# Set the commission
cerebro.broker.setcommission(commission=0.0)
# Set Observer
cerebro.addobserver(bt.observers.Broker)
cerebro.addobserver(bt.observers.Trades)
cerebro.addobserver(bt.observers.BuySell, barplot=True, bardist=0.0025)
cerebro.addobserver(bt.observers.DrawDown)
# Set Analyzer
cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name='tradeanalyzer') # muito completo
# cerebro.addanalyzer(bt.analyzers.TimeDrawDown, _name='timedrawdown')
cerebro.addanalyzer(bt.analyzers.VWR, _name='vwr') # VariabilityWeightedReturn
cerebro.addanalyzer(bt.analyzers.SQN, _name='sqn') # SystemQualityNumber.
# Writer
# cerebro.addwriter(bt.WriterFile, csv=True, out='./logs/log.csv')
# cerebro.addwriter(bt.WriterFile, csv=args.writercsv, out='./logs/log.csv')
# Print out the starting conditions
# print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
# Run over everything
results = cerebro.run()
# # Extract/save analyzers
# analyzers_log(settings, results)
# clock the end of the process
tend = process_time()
# print out the result
print('Time used:', str(tend - tstart))
# # Plot the result
# if args.plot:
# pkwargs = dict(style='bar')
# # pkwargs = dict(style='ohlc')
# if args.plot is not True: # evals to True but is not True
# npkwargs = eval('dict(' + args.plot + ')') # args were passed
# pkwargs.update(npkwargs)
# cerebro.plot(**pkwargs)
# # cerebro.plot(style='bar', bardist=0)
# Print out the final result
print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
else:
pass
else:
pass
def stop(self):
print('%.2f,%.2f,%.2f,%.2f,,%2d,%2d,%.2f' %
(self.params.k, self.params.differ, self.params.rangeDays,
self.tradeCount, self.winCount, self.loseCount,
self.broker.getvalue()))
if __name__ == '__main__':
# Create a cerebro entity
cerebro = bt.Cerebro(maxcpus=4, optreturn=False, stdstats=False)
strats = cerebro.optstrategy(dtStrategyV12,
ordersize=1,
k=range(4, 7, 1),
differ=range(0, 1, 1),
rangeDays=range(2, 12, 1))
# Set the commission
cerebro.broker.setcommission(leverage=1, mult=5, commission=0.01)
cerebro.addsizer(maxRiskSizer)
#cerebro.broker.setcommission(commission=0.0)
# Add a strategy
#cerebro.addstrategy(DTStrategy01)
#cerebro.addanalyzer(btanalyzers.SharpeRatio, _name='mysharpe')
#cerebro.addanalyzer(btanalyzers.AnnualReturn, _name='annual')
def run_strat(**kwargs):
# Instatiates backtester
cerebro = bt.Cerebro()
# Set Cash & Commission
cerebro.broker.setcash(10000)
cerebro.broker.setcommission(commission=0.005)
# Allow fractional shares
cerebro.broker.addcommissioninfo(CommInfoFractional())
# Adds DataFeeds for universe to the backtester
if kwargs["data_set"] == "testing":
from_dt = datetime.datetime(2020, 11, 1) - datetime.timedelta(40)
to_dt = datetime.datetime(2021, 2, 8)
else:
from_dt = datetime.datetime(2019, 8, 19)
to_dt = datetime.datetime(2020, 10, 31)
dataPath = Path(__file__).parent / "data/*.csv"
for file in glob.glob(str(dataPath)):
data = btfeeds.GenericCSVData(
dataname=file,
fromdate=from_dt,
todate=to_dt,
datetime=0,
open=1,
high=2,
low=3,
close=4,
volume=5,
openinterest=-1,
time=-1,
dtformat=("%Y-%m-%d"),
)
cerebro.adddata(data)
# cerebro.optstrategy(
# PairTrading,
# trigger=map(lambda x: x / 10, range(10, 40, 5)),
# period=range(10, 55, 5),
# sell_mul=map(lambda x: x / 10, range(1, 11, 1)),
# printout=kwargs["show_log"],
# )
cerebro.addstrategy(PairTrading,
sell_mul=0.2,
trigger=3,
period=30,
printout=kwargs["show_log"])
if kwargs["show_pyfolio"]:
cerebro.addanalyzer(bt.analyzers.PyFolio, _name="pyfolio")
results = cerebro.run()
if kwargs["show_plot"]:
cerebro.plot()
if kwargs["show_pyfolio"]:
pyfoliozer = results[0].analyzers.getbyname("pyfolio")
returns, positions, transactions, _ = pyfoliozer.get_pf_items()
returns.to_pickle(
Path(__file__).parent / "results" / kwargs["data_set"] /
"returns.pickle")
positions.to_pickle(
Path(__file__).parent / "results" / kwargs["data_set"] /
"positions.pickle")
transactions.to_pickle(
Path(__file__).parent / "results" / kwargs["data_set"] /
"transactions.pickle")
target = self.broker.getvalue(
) * self.params.prop # Ideal total value of the position
price = data.close[0]
shares = int(target / price)
if shares * price > cash:
return 0 # Not enough money for this trade
else:
return shares
if __name__ == '__main__':
start = datetime.datetime(2017, 8, 1)
end = datetime.datetime(2020, 4, 30)
is_first = True
# Create a cerebro entity
cerebro = bt.Cerebro(stdstats=False, optreturn=False)
symbols = [
'adaniports_15min.csv', 'asianpaint_15min.csv', 'axisbank_15min.csv',
'bajajfinsv_15min.csv', 'bajaj_auto_15min.csv', 'bajfinance_15min.csv',
'bhartiartl_15min.csv', 'bpcl_15min.csv', 'britannia_15min.csv',
'cipla_15min.csv', 'coalindia_15min.csv', 'drreddy_15min.csv',
'eichermot_15min.csv', 'gail_15min.csv', 'grasim_15min.csv',
'hcltech_15min.csv', 'hdfcbank_15min.csv', 'hdfc_15min.csv',
'heromotoco_15min.csv', 'hindalco_15min.csv', 'hindunilvr_15min.csv',
'icicibank_15min.csv', 'indusindbk_15min.csv', 'infratel_15min.csv',
'infy_15min.csv', 'ioc_15min.csv', 'itc_15min.csv',
'jswsteel_15min.csv', 'kotakbank_15min.csv', 'lt_15min.csv',
'maruti_15min.csv', 'mm_15min.csv', 'nestleind_15min.csv',
'ntpc_15min.csv', 'ongc_15min.csv', 'powergrid_15min.csv',
'reliance_15min.csv', 'sbin_15min.csv', 'sunpharma_15min.csv',
'tatamotors_15min.csv', 'tatasteel_15min.csv', 'tcs_15min.csv',
def runstrat(pargs=None):
args = parse_args(pargs)
# Create a cerebro
cerebro = bt.Cerebro()
if args.cash is not None:
cerebro.broker.set_cash(args.cash)
# Get the dates from the args
fromdate = datetime.datetime.strptime(args.fromdate, '%Y-%m-%d')
todate = datetime.datetime.strptime(args.todate, '%Y-%m-%d')
# Create the 1st data
data = bt.feeds.BacktraderCSVData(
dataname=args.data,
fromdate=fromdate,
todate=todate)
cerebro.adddata(data) # Add the data to cerebro
# Add the strategy
cerebro.addstrategy(bt.strategies.SMA_CrossOver)
tframes = dict(
days=bt.TimeFrame.Days,
weeks=bt.TimeFrame.Weeks,
months=bt.TimeFrame.Months,
years=bt.TimeFrame.Years)
# Add the Analyzers
cerebro.addanalyzer(bt.analyzers.TimeReturn,
timeframe=tframes[args.tframe])
shkwargs = dict()
if args.annualize:
shkwargs['annualize'] = True
if args.riskfreerate is not None:
shkwargs['riskfreerate'] = args.riskfreerate
if args.factor is not None:
shkwargs['factor'] = args.factor
if args.stddev_sample:
shkwargs['stddev_sample'] = True
if args.no_convertrate:
shkwargs['convertrate'] = False
cerebro.addanalyzer(bt.analyzers.SharpeRatio,
timeframe=tframes[args.tframe],
**shkwargs)
# Add a writer to get output
cerebro.addwriter(bt.WriterFile, csv=args.writercsv, rounding=4)
cerebro.run() # And run it
# Plot if requested
if args.plot:
pkwargs = dict(style='bar')
if args.plot is not True: # evals to True but is not True
npkwargs = eval('dict(' + args.plot + ')') # args were passed
pkwargs.update(npkwargs)
cerebro.plot(**pkwargs)
def runstrat():
#Create an instance of cerebro
cerebro = bt.Cerebro(exactbars=-1) #exactbars True reduces memory usage significantly, but change to '-1' for partial memory savings (keeping indicators in memory) or 'false' to turn off completely if having trouble accessing bars beyond max indicator paramaters.
cerebro.broker.set_coc(False) #cheat on close allows you to buy the close price of the current bar in which order was made. cheat on open allows you to simulate a market order on the open price of the next bar
cerebro.broker.set_coo(False) #cheat on open allows you to buy the close price of the current bar in which order was made. cheat on open allows you to simulate a market order on the open price of the next bar
cerebro.broker.set_shortcash(True) #False means decrease cash available when you short, True means increase it
#Add our strategy
cerebro.addstrategy(Strategy)
#Create/Instantiate objects to access parameters from UserInput Class
#Can NOT create object referencing Strategy class as per backtrader
modelp = UserInputs.model_params()
datalist = UserInputs.datalist('hist')
ibdatalist = UserInputs.datalist('ib')
#Ensure stock lists have no duplicates - duplicates will BREAK program
if len(datalist) != len(set(datalist)) or len(ibdatalist) != len(set(ibdatalist)):
print("*****You have duplicates in stock list - FIX LIST*****")
#GET THE DATA
session_start = modelp.get('sessionstart')
session_end = modelp.get('sessionend')
if modelp.get('live_status'):
#***** LIVE TRADING PARAMETERS*******
store = bt.stores.IBStore(host='127.0.0.1',
port=7497,
clientId = 100)
#get number of tickers
indicator_dict = UserInputs.ind_params()
max_ind = max(indicator_dict.values())
#Data set for live trading IB
for i,j in enumerate(ibdatalist):
#Data for live IB trading
data = store.getdata(dataname=j,
timeframe=bt.TimeFrame.Minutes,
tz = pytz.timezone('US/Central'),
#historical = True,
backfill_start = True, #true enables maximum allowable backfill in single request
useRTH = True,
rtbar=True,
fromdate = UserInputs.ib_backfill_start(UserInputs.max_ind()),#from date determined by today - max period paramater
sessionstart = session_start,
sessionend = session_end,
notifyall=True,
qcheck=2.0,
debug=True)
cerebro.resampledata(data, name="{}0".format(j),timeframe=bt.TimeFrame.Minutes, compression=modelp.get('timeframe0'))
#Apply resamplings
if modelp.get('timeframe1on'):
data_Timeframe1 = cerebro.resampledata(data,name="{}1".format(j),
timeframe=bt.TimeFrame.Minutes,
compression = modelp.get('timeframe1'))
if modelp.get('timeframe2on'):
data_Timeframe2 = cerebro.resampledata(data,name="{}2".format(j),
timeframe=bt.TimeFrame.Minutes,
compression = modelp.get('timeframe2'))
cerebro.broker = store.getbroker()
elif not modelp.get('live_status'):
#******BACTESTING ONLY - MYSQL DATABASE*********
#mysql configuration items for connection
host = '127.0.0.1'
user = '******'
password = '******'
database = 'Stock_Prices'
table = '5_min_prices'
#Determine data range to run
start_date = modelp.get('start_date')
end_date = modelp.get('end_date')
for i,j in enumerate(datalist):
#Get data from mysql and add data to Cerebro
data = mysql.MySQLData(dbHost = host,
dbUser = user,
dbPWD = password,
dbName = database,
table = table,
symbol = j,
fromdate = start_date,
todate= end_date,
sessionstart = session_start,
sessionend = session_end,
timeframe=bt.TimeFrame.Minutes,
compression = modelp.get('timeframe0'),
)
data_BaseTimeframe = cerebro.adddata(data=data,
name="{}0".format(j),
)
data_BaseTimeframe.csv=True #Report this data to csv file (true) or not (false)
#data_BaseTimeframe.plotinfo.plot = False
if modelp.get('timeframe1on'):
#Apply resamplings
data_Timeframe1 = cerebro.resampledata(data,
name="{}1".format(j),
timeframe=bt.TimeFrame.Minutes,
compression = modelp.get('timeframe1'),
)
data_Timeframe1.csv=False #Report this data to csv file (true) or not (false)
#data_Timeframe1.plotinfo.plot = False
#data_Timeframe1.plotinfo.plotmaster = data_BaseTimeframe
if modelp.get('timeframe2on'):
data_Timeframe2 = cerebro.resampledata(data,
name="{}2".format(j),
timeframe=bt.TimeFrame.Minutes,
compression = modelp.get('timeframe2'),
)
data_Timeframe2.csv=False #Report this data to csv file (true) or not (false)
#data_Timeframe2.plotinfo.plot = False
#data_Timeframe2.plotinfo.plotmaster = data_BaseTimeframe
# Set our desired cash start
cerebro.broker.setcash(modelp.get('start_cash'))
# Set the commission
cerebro.broker.setcommission(commission=0.00003,
margin= None,
mult=1.0,
commtype=None,
percabs=True,
stocklike=True,
leverage=1)
"""
#Set the slippage
cerebro.broker.set_slippage_perc(0.001,
slip_open=True,
slip_limit=True,
slip_match=True,
slip_out=False)
"""
# Add SQN to qualify the trades (rating to analyze quality of trading system: 2.5-3 good, above 3 excellent. SquareRoot(NumberTrades) * Average(TradesProfit) / StdDev(TradesProfit). Need at least 30 trades to be reliable
cerebro.addanalyzer(bt.analyzers.SQN)
#Add Sharpe Ratio - configured for daily returns
cerebro.addanalyzer(bt.analyzers.SharpeRatio)
#Adding my own custom analyzer - when creating analyzer, make sure to include file in _init_.py within backtrader.analyzer folder so it runs
cerebro.addanalyzer(bt.analyzers.AcctStats)
#Adding analyzer for drawdown
cerebro.addanalyzer(bt.analyzers.DrawDown)
# Add TradeAnalyzer to output trade statistics - THESE ARE THE TRADE NOTIFICATIONS THAT ARE PRINTED WHEN PROGRAM IS RUN
cerebro.addanalyzer(bt.analyzers.Transactions)
#Adds just buy/sell observer to chart (assuming stdstats is set to false)
cerebro.addobservermulti(bt.observers.BuySell,)
#Adds custom observers
cerebro.addobserver(bt.observers.AcctValue) #reports trade statistics in command prompt at end of program run
cerebro.addobserver(bt.observers.OrderObserver) #reports trades in command prompt when program is run
#Generate output report in csv format
if UserInputs.model_params().get('writer')=='on':
current_time = datetime.now().strftime("%Y-%m-%d_%H.%M.%S.csv")
csv_file = 'C:/Program Files (x86)/Python36-32/Lib/site-packages/backtrader/out/'
csv_file += 'Strategy'
csv_file += current_time
cerebro.addwriter(bt.WriterFile, csv = True, out=csv_file)
print("Writer CSV Report On")
#RUN EVERYTHING
results = cerebro.run(stdstats=False, #enables some additional chart information like profit/loss, buy/sell, etc, but tends to clutter chart
runonce=True,
)
if not modelp.get('live_status'):
#Print analyzers
for alyzer in results[0].analyzers:
alyzer.print()
#Calculate Program end time
end_time = datetime.now().time()
print('Program end at {}'.format(end_time))
#PLOT TRADING RESULTS - **Ensure trading session ends at 2:55 (if it ends at 3, some tickers only go up to 2:55, creating data length mismatches between tickers that doesn't allow plotting)
plot_end = modelp.get('end_date')-timedelta(days=1) #Hack to prevent errors with plotting, specifically x and y shape different error
#Chart 5 minute timeframes only, 1 by 1
for i in range (0,len(results[0].datas),3):
for j, d in enumerate(results[0].datas):
d.plotinfo.plot = i ==j
cerebro.plot(barup='olive', bardown='lightpink',volup = 'lightgreen',voldown='crimson')
def runstrategy(ticker_list, bench_ticker):
args = parse_args()
print(args)
# Create a cerebro
cerebro = bt.Cerebro()
# Get the dates from the args
fromdate = datetime.datetime.strptime(args.fromdate, '%Y-%m-%d')
todate = datetime.datetime.strptime(args.todate, '%Y-%m-%d')
# bench = bt.feeds.YahooFinanceData(
# dataname=bench_ticker,
# fromdate=fromdate,
# todate=todate,
# buffered=True,plot = False
# )
bench = bt.feeds.GenericCSVData(
dataname='/Users/joan/PycharmProjects/CSV_DB/IB/' + bench_ticker +
'.csv',
fromdate=fromdate,
todate=todate,
nullvalue=0.0,
dtformat=('%Y%m%d'),
datetime=1,
open=2,
high=3,
low=4,
close=5,
volume=6,
reverse=False,
plot=False)
cerebro.adddata(bench, name=bench_ticker)
for i in ticker_list:
print('Loading data: ' + i)
# data = bt.feeds.YahooFinanceData(
# dataname=i,
# fromdate=fromdate,
# todate=todate,
# adjclose=True,
# buffered=True, plot = False
# )
data = bt.feeds.GenericCSVData(
dataname='/Users/joan/PycharmProjects/CSV_DB/IB/' + i + '.csv',
fromdate=fromdate,
todate=todate,
nullvalue=0.0,
dtformat=('%Y%m%d'),
datetime=1,
open=2,
high=3,
low=4,
close=5,
volume=6,
reverse=False,
plot=False)
cerebro.adddata(data, name=i)
# Add the strategy
cerebro.addstrategy(PairTradingStrategy,
period=args.period,
stake=args.stake)
# cerebro.optstrategy(
# PairTradingStrategy,
# period=range(45, 75),
# )
# Add the commission - only stocks like a for each operation
cerebro.broker.setcash(args.cash)
# Add the commission - only stocks like a for each operation
# cerebro.broker.setcommission(commission=args.commperc)
comminfo = FixedCommisionScheme()
cerebro.broker.addcommissioninfo(comminfo)
cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name='sharpe_ratio')
cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name="ta")
cerebro.addanalyzer(bt.analyzers.SQN, _name="sqn")
cerebro.addanalyzer(bt.analyzers.SharpeRatio_A,
_name='myysharpe',
riskfreerate=args.rf_rate)
cerebro.addanalyzer(bt.analyzers.PyFolio, _name='mypyf')
cerebro.addanalyzer(bt.analyzers.TimeReturn,
timeframe=bt.TimeFrame.Days,
data=bench,
_name='benchreturns')
cerebro.addobserver(bt.observers.Value)
cerebro.addobserver(bt.observers.Benchmark, plot=False)
cerebro.addobserver(bt.observers.DrawDown)
# And run it
strat = cerebro.run(runonce=not args.runnext,
preload=not args.nopreload,
oldsync=args.oldsync)
#
# strat = cerebro.optstrategy(
# PairTradingStrategy,
# period=range(45, 75),
# printlog=True)
# Plot if requested
if args.plot:
cerebro.plot(style='candlestick',
barup='green',
bardown='red',
figsize=(100, 100))
bench_returns = strat[0].analyzers.benchreturns.get_analysis()
bench_df = pd.DataFrame.from_dict(bench_returns,
orient='index',
columns=['return'])
return_df = pd.DataFrame.from_dict(
strat[0].analyzers.mypyf.get_analysis()['returns'],
orient='index',
columns=['return'])
# print('Sharpe Ratio(bt):', firstStrat.analyzers.myysharpe.get_analysis()['sharperatio'])
# print('Sharpe Ratio:', empyrical.sharpe_ratio(return_df, risk_free=args.rf_rate / 252, period='daily')[0])
# print('Sharpe Ratio Benchmark:', empyrical.sharpe_ratio(bench_df, risk_free=args.rf_rate / 252, period='daily')[0])
# print('')
#
# print('Sortino Ratio:', empyrical.sortino_ratio(return_df, period='daily')[0])
# print('Sortino Ratio Benchmark:', empyrical.sortino_ratio(bench_df, period='daily')[0])
# print('')
# print('VaR:', empyrical.value_at_risk(return_df) * 100, '%')
# print('VaR Benchmark:', empyrical.value_at_risk(bench_df) * 100, '%')
#
# print('')
#
# print('Capture:', round(empyrical.capture(return_df, bench_df, period='daily')[0] * 100), '%')
# print('')
#
# print('Max drawdown: ', round(empyrical.max_drawdown(return_df)[0] * 100), '%')
# print('Max drawdown Benchmark: ', round(empyrical.max_drawdown(bench_df)[0] * 100), '%')
#
# print('')
alpha, beta = empyrical.alpha_beta(return_df,
bench_df,
risk_free=args.rf_rate)
# print('Beta: ', beta)
# print('')
# print('Annual return:', round(empyrical.annual_return(return_df)[0] * 100), '%')
# print('Annual Vol:', round(empyrical.annual_volatility(return_df)[0] * 100), '%')
# print('')
# print('Annual return Benchmark:', round(empyrical.annual_return(bench_df)[0] * 100), '%')
# print('Annual Vol Benchmark:', round(empyrical.annual_volatility(bench_df)[0] * 100), '%')
# print('')
printTradeAnalysis(strat[0].analyzers.ta.get_analysis())
dic = {
'SQN':
printSQN(strat[0].analyzers.sqn.get_analysis()),
'sharpe':
empyrical.sharpe_ratio(return_df,
risk_free=args.rf_rate / 252,
period='daily')[0],
'sharpe_bm':
empyrical.sharpe_ratio(bench_df,
risk_free=args.rf_rate / 252,
period='daily')[0],
'sortino':
empyrical.sortino_ratio(bench_df, period='daily')[0],
'sortino_bm':
empyrical.sortino_ratio(bench_df, period='daily')[0],
'VaR':
empyrical.value_at_risk(return_df) * 100,
'VaR_bm':
empyrical.value_at_risk(bench_df) * 100,
'capture':
round(empyrical.capture(return_df, bench_df, period='daily')[0] * 100),
'max_dd':
round(empyrical.max_drawdown(return_df)[0] * 100, 2),
'max_dd_bm':
round(empyrical.max_drawdown(bench_df)[0] * 100, 2),
'beta':
beta,
'return_annual':
round(empyrical.annual_return(return_df)[0] * 100, 2),
'return_annual_bm':
round(empyrical.annual_volatility(return_df)[0] * 100, 2),
'vol_annual':
round(empyrical.annual_return(bench_df)[0] * 100, 2),
'vol_annual_bm':
round(empyrical.annual_volatility(bench_df)[0] * 100, 2)
}
df = pd.DataFrame(dic, index=[0])
print(df)
def calc_stats(df):
df['perc_ret'] = (1 + df['return']).cumprod() - 1
# print(df.tail())
return df
s = return_df.rolling(30).std()
b = bench_df.rolling(30).std()
# Get final portfolio Value
portvalue = cerebro.broker.getvalue()
# Print out the final result
print('Final Portfolio Value: ${}'.format(round(portvalue, 2)),
'PnL: ${}'.format(round(portvalue - args.cash, 2)),
'PnL: {}%'.format(round(((portvalue / args.cash) - 1) * 100, 2)))
# Finally plot the end results
if args.plot:
fig, axs = plt.subplots(2, sharex=True)
fig.autofmt_xdate()
axs[1].plot(s)
axs[1].plot(b)
axs[1].set_title('Drawdown')
axs[1].legend(['Fund', 'Benchmark'])
axs[0].set_title('Returns')
axs[0].plot(calc_stats(return_df)['perc_ret'])
axs[0].plot(calc_stats(bench_df)['perc_ret'])
axs[0].legend(['Fund', 'Benchmark'])
plt.show()
def runstrat(args=None):
args = parse_args(args)
cerebro = bt.Cerebro()
# Data feed kwargs
kwargs = dict()
# Parse from/to-date
dtfmt, tmfmt = '%Y-%m-%d', 'T%H:%M:%S'
for a, d in ((getattr(args, x), x) for x in ['fromdate', 'todate']):
if a:
strpfmt = dtfmt + tmfmt * ('T' in a)
kwargs[d] = datetime.datetime.strptime(a, strpfmt)
# Data feed
# data0 = bt.feeds.YahooFinanceCSVData(dataname=args.data0, **kwargs)
# cerebro.adddata(data0, name='d0')
# data1 = bt.feeds.YahooFinanceCSVData(dataname=args.data1, **kwargs)
# data1.plotinfo.plotmaster = data0
# cerebro.adddata(data1, name='d1')
# data2 = bt.feeds.YahooFinanceCSVData(dataname=args.data2, **kwargs)
# data2.plotinfo.plotmaster = data0
# cerebro.adddata(data2, name='d2')
# Futu data feed
symble_list = ['US.ATHM', 'US.CMCM'] # 'US.RYB'
quote_ctx = ft.OpenQuoteContext(host='127.0.0.1', port=11111)
# _, prices = quote_ctx.get_history_kline(symble_list, start=args.fromdate, end=args.todate,
# fields=[ft.KL_FIELD.DATE_TIME, ft.KL_FIELD.OPEN, ft.KL_FIELD.HIGH,
# ft.KL_FIELD.LOW, ft.KL_FIELD.CLOSE, ft.KL_FIELD.TRADE_VOL])
_, prices = quote_ctx.get_multiple_history_kline(symble_list,
start=args.fromdate,
end=args.todate)
# print(prices)
for price in prices:
name = price['code'][0]
price = price.assign(Data=price.time_key.apply(
lambda x: str(x)[0:10])).set_index('Data')
price = price.drop(columns=['code', 'time_key'])
price = price.rename(index=str,
columns={
"open": "Open",
"high": "High",
"low": "Low",
"close": "Close",
"volume": "Volume"
})
price['Volume'] = price['Volume'].astype('int64')
price.index = price.index.astype('datetime64[ns]')
# Pass it to the backtrader datafeed and add it to the cerebro
data = bt.feeds.PandasData(
dataname=price,
# datetime='Date',
nocase=True,
)
cerebro.adddata(data, name=name)
# Broker
cerebro.broker = bt.brokers.BackBroker(**eval('dict(' + args.broker + ')'))
cerebro.broker.setcommission(commission=0.001)
# Sizer
# cerebro.addsizer(bt.sizers.FixedSize, **eval('dict(' + args.sizer + ')'))
cerebro.addsizer(TestSizer, **eval('dict(' + args.sizer + ')'))
# Strategy
cerebro.addstrategy(St, **eval('dict(' + args.strat + ')'))
# Execute
cerebro.run(**eval('dict(' + args.cerebro + ')'))
args.plot = 'style="candle"'
if args.plot: # Plot if requested to
cerebro.plot(**eval('dict(' + args.plot + ')'))
self.lastEntryPrice = self.dataclose[0]
elif self.dataclose[0] > self.exitPrice:
self.close()
else:
pass
def stop(self):
print('%2d,%2d,%2d,%2d,%2d,%2d,%2d,%2d,%.2f' %
(self.params.longIN, self.params.differIN, self.params.longExit,
self.params.differExit, self.params.atrDays, self.tradeCount,
self.winCount, self.loseCount, self.broker.getvalue()))
if __name__ == '__main__':
# Create a cerebro entity
cerebro = bt.Cerebro(maxcpus=6)
# Add a strategy
#cerebro.addstrategy(TurtleStrategy01)
cerebro.addstrategy(TurtleStrategy01)
# Set the commission
cerebro.broker.setcommission(leverage=1, mult=5, commission=0.01)
cerebro.addsizer(maxRiskSizer)
# Datas are in a subfolder of the samples. Need to find where the script is
# because it could have been called from anywhere
modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
#datapath = os.path.join(modpath, '../../datas/RBindex_10m.csv')
datapath = os.path.join(modpath, '../../datas/PPindex.csv')
self.end_val = self.strategy.broker.get_value()
def get_analysis(self):
return {"start": self.start_val, "end": self.end_val,
"growth": self.end_val - self.start_val, "return": self.end_val / self.start_val}
class AcctValue(bt.Observer):
alias = ('Value',)
lines = ('value',)
plotinfo = {"plot": True, "subplot": True}
def next(self):
self.lines.value[0] = self._owner.broker.getvalue() # Get today's account value (cash + stocks)
cerebro = bt.Cerebro(stdstats=False) # I don't want the default plot objects
cerebro.broker.set_cash(1000000) # Set our starting cash to $1,000,000
cerebro.broker.setcommission(0.02)
#-------------------------------------------------
start = datetime.datetime(2018, 1, 1)
end = datetime.datetime(2018, 3, 31)
#--------------------------------------------
import sys
sys.path.append('../lib/')
from makeData import *
start_test = '2018-01-01'
start_validation = '2018-01-01'
data_path = '..\\dataBackTesting\\'
self.end_val = self.strategy.broker.get_value()
def get_analysis(self):
return {"start": self.start_val, "end": self.end_val,
"growth": self.end_val - self.start_val, "return": self.end_val / self.start_val}
start = dt.datetime(2010, 1, 1)
end = dt.datetime(2016, 10, 31)
# Different stocks from past posts because of different data source (no plot for NTDOY)
symbols = ["AAPL", "GOOG", "MSFT", "AMZN", "SNY", "VZ", "IBM", "HPQ", "QCOM", "NVDA"]
datafeeds = {s: web.DataReader(s, "yahoo", start, end) for s in symbols}
for df in datafeeds.values():
df["OpenInterest"] = 0 # PandasData reader expects an OpenInterest column;
# not provided by Google and we don't use it so set to 0
cerebro = bt.Cerebro(stdstats=False)
plot_symbols = ["AAPL", "GOOG", "NVDA"]
is_first = True
#plot_symbols = []
for s, df in datafeeds.items():
data = bt.feeds.PandasData(dataname=df, name=s)
if s in plot_symbols:
if is_first:
data_main_plot = data
is_first = False
else:
data.plotinfo.plotmaster = data_main_plot
else:
data.plotinfo.plot = False
cerebro.adddata(data) # Give the data to cerebro
def backtest(tickers, intra_freq, year, params_dict):
log_file = open('trade_log.txt', "w")
sys.stdout = log_file #log output
class ValueObserver(bt.Observer):
lines = ('value', )
plotinfo = dict(plot=True, subplot=True, plotlinelabels=True)
plotlines = dict(
#value=dict(marker='*', markersize=8.0, color='lime', fillstyle='full')
value=dict(linewidth=1.5))
def next(self):
self.lines.value[
0] = self._owner.dataclose_x - self._owner.params.m * self._owner.dataclose_y
# Create a Stratey
class PairStrategy(bt.Strategy):
params = (
('m', round(float(params_dict['m']), 5)), #slope
('b', round(float(params_dict['b']), 5)), #intercept
('std', round(float(params_dict['std']), 5)),
('avg', params_dict['avg']),
('muti', params_dict['muti']), # muti*std+avg
('size', params_dict['size']))
def log(self, txt, dt=None):
''' Logging function fot this strategy'''
dt = dt or self.datas[0].datetime.datetime(0)
print('%s, %s' % (dt.strftime('%Y-%m-%d %H:%M:%S'), txt))
def __init__(self):
# Keep a reference to the "close" line in the data
#x is BP y is RDSA
self.dataclose_x = self.datas[0].close
self.dataclose_y = self.datas[1].close
# To keep track of pending orders and buy price/commission
self.order = None
self.buyprice = None
self.buycomm = None
def notify_cashvalue(self, cash, value):
self.log('Cash %s Value %s' % (cash, value))
def notify_order(self, order):
print(type(order), 'Is Buy ', order.isbuy())
if order.status in [order.Submitted, order.Accepted]:
# Buy/Sell order submitted/accepted to/by broker - Nothing to do
return
# Check if an order has been completed
# Attention: broker could reject order if not enough cash
if order.status in [order.Completed]:
if order.isbuy():
self.log(
'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
(order.executed.price, order.executed.value,
order.executed.comm))
self.buyprice = order.executed.price
self.buycomm = order.executed.comm
else: # Sell
self.log(
'SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
(order.executed.price, order.executed.value,
order.executed.comm))
self.bar_executed = len(self)
elif order.status in [
order.Canceled, order.Margin, order.Rejected
]:
self.log('Order Canceled/Margin/Rejected')
self.order = None
def notify_trade(self, trade):
if not trade.isclosed:
return
self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' %
(trade.pnl, trade.pnlcomm))
def next(self):
# Simply log the closing price of the series from the reference
self.log('Close(%s), %.2f' % (tickers[0], self.dataclose_x[0]))
self.log('Close(%s), %.2f' % (tickers[1], self.dataclose_y[0]))
# Check if an order is pending ... if yes, we cannot send a 2nd one
if self.order:
return
# Check if we are in the market
if not self.position:
if ((
(self.dataclose_x[0] -
self.params.m * self.dataclose_y[0] - self.params.b) <=
(2 * self.params.std * self.params.muti + self.params.avg))
and
((self.dataclose_x[0] -
self.params.m * self.dataclose_y[0] - self.params.b) >=
-(2 * self.params.std * self.params.muti +
self.params.avg))):
#limit loss, we will not trade unless -(2*std + 0.5) <= (BP-m*RSDA-b) <= (2*std + 0.5)
# BUY
# BP-m*RSDA-b >1*std + 0.5
if ((self.dataclose_x[0] -
self.params.m * self.dataclose_y[0] - self.params.b) >
(self.params.std * self.params.muti +
self.params.avg)):
self.log('SELL PORTFOLIO,SELL{},BUY{}'.format(
self.dataclose_x[0], self.dataclose_y[0]))
# Keep track of the created order to avoid a 2nd order
self.order = self.sell(self.datas[0],
size=self.params.size)
self.order = self.buy(self.datas[1],
size=int(self.params.m *
self.params.size))
# BP-m*RSDA-b <-(1*std + 0.5)
elif ((self.dataclose_x[0] -
self.params.m * self.dataclose_y[0] - self.params.b)
< -(self.params.std * self.params.muti +
self.params.avg)):
self.log('BUY PORTFOLIO,BUY{},SELL{}'.format(
self.dataclose_x[0], self.dataclose_y[0]))
# Keep track of the created order to avoid a 2nd order
self.order = self.buy(self.datas[0],
size=self.params.size)
self.order = self.sell(self.datas[1],
size=int(self.params.m *
self.params.size))
else:
# this part is not exactly what I want but till now it is fine
#if the price is too high we need to buy our portfolio back incase of risk(buy BP sell RDSA)
if ((self.dataclose_x[0] -
self.params.m * self.dataclose_y[0] - self.params.b) >=
(2 * self.params.std * self.params.muti +
self.params.avg)):
self.log('CLOSE THE SELL LIMIT LOSS,BUY{},SELL{}'.format(
self.dataclose_x[0], self.dataclose_y[0]))
# Keep track of the created order to avoid a 2nd order
self.order = self.buy(self.datas[0], size=self.params.size)
self.order = self.sell(self.datas[1],
size=int(self.params.m *
self.params.size))
#if the price is too low we need to sell our portfolio back incase of risk(sell BP buy RDSA)
elif ((self.dataclose_x[0] -
self.params.m * self.dataclose_y[0] - self.params.b) <=
-(2 * self.params.std * self.params.muti +
self.params.avg)):
self.log('CLOSE THE BUY LIMIT LOSS,SELL{},BUY{}'.format(
self.dataclose_x[0], self.dataclose_y[0]))
# Keep track of the created order to avoid a 2nd order
self.order = self.sell(self.datas[0],
size=self.params.size)
self.order = self.buy(self.datas[1],
size=int(self.params.m *
self.params.size))
else:
# SELL
# -0.5 < BP-m*RSDA-b < 0.5
if (((self.dataclose_x[0] -
self.params.m * self.dataclose_y[0] - self.params.b)
< self.params.avg) and
((self.dataclose_x[0] -
self.params.m * self.dataclose_y[0] - self.params.b)
> -self.params.avg)):
# comes from higher portfolio value
if ((self.dataclose_x[0] -
self.params.m * self.dataclose_y[0] -
self.params.b) > 0):
self.log('CLOSE THE SELL,BUY{},SELL{}'.format(
self.dataclose_x[0], self.dataclose_y[0]))
# Keep track of the created order to avoid a 2nd order
self.order = self.buy(self.datas[0],
size=self.params.size)
self.order = self.sell(self.datas[1],
size=int(self.params.m *
self.params.size))
# comes from lower portfolio value
else:
self.log('CLOSE THE BUY,SELL{},BUY{}'.format(
self.dataclose_x[0], self.dataclose_y[0]))
# Keep track of the created order to avoid a 2nd order
self.order = self.sell(self.datas[0],
size=self.params.size)
self.order = self.buy(self.datas[1],
size=int(self.params.m *
self.params.size))
# Run the model
variable_name = ['stock1', 'stock2']
# Create a cerebro entity
cerebro = bt.Cerebro()
# Add a strategy
cerebro.addstrategy(PairStrategy)
# load the data
datalist = [
('data\\intra_' + tickers[0] + '_' + year + '_' + intra_freq + '.csv',
variable_name[0]), #[0] = Data file, [1] = Data name
('data\\intra_' + tickers[1] + '_' + year + '_' + intra_freq + '.csv',
variable_name[1]),
]
# Loop through the list adding to cerebro.
for i in range(len(datalist)):
data = bt.feeds.GenericCSVData(dataname=datalist[i][0],
datetime=0,
open=1,
high=2,
low=3,
close=4,
openinterest=-1,
time=-1,
volume=-1,
dtformat="%Y-%m-%d %H:%M:%S",
timeframe=bt.TimeFrame.Minutes,
compression=1)
cerebro.adddata(data, name=datalist[i][1])
# Set our desired cash start
cerebro.broker.setcash(100000.0)
# Add a FixedSize sizer according to the stake
cerebro.addsizer(bt.sizers.FixedSize, stake=10)
# Set the commission - 0.1% ... divide by 100 to remove the %
cerebro.broker.setcommission(commission=0.001)
cerebro.addobserver(ValueObserver)
# Print out the starting conditions
print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
# Run over everything
cerebro.run()
# Print out the final result
print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
# Print
plot = cerebro.plot(volume=False)[0][0]
plot.set_size_inches(18.5, 10.5)
plot.savefig(tickers[0] + '_' + tickers[1] + '.png', dpi=1000)
def next(self):
if not self.position:
if self.rsi < self.params.rsi_low:
self.buy(size=10)
else:
if self.rsi > self.params.rsi_high:
self.sell(size=10)
# INPUT CONDITIONS TO FEED INTO CEREBRO IS ADDED HERE
if __name__ == '__main__':
# Variable for our starting cash
startcash = 10000
# Create an instance of cerebro
cerebro = bt.Cerebro(optreturn=False)
# ADD STRATEGY
cerebro.optstrategy(firstStrategy,
period=range(10, 11),
rsi_low=range(33, 34),
rsi_high=range(60, 61))
cerebro.addobserver(OrderObserver)
# DATA FEED FROM EXCHANGE
symbol = str('ETH/USDT')
timeframe = str('15m')
exchange = str('poloniex')
exchange_out = str(exchange)
rets = np.zeros(len(available))
for i, d in enumerate(available):
# calculate individual daily returns
rets[i] = (d.close[0] - d.close[-1]) / d.close[-1]
# calculate weights using formula
market_ret = np.mean(rets)
weights = -(rets - market_ret)
weights = weights / np.sum(np.abs(weights))
for i, d in enumerate(available):
self.order_target_percent(d, target=weights[i])
cerebro = bt.Cerebro(stdstats=False)
cerebro.broker.set_coc(True)
for ticker in tickers:
data = bt.feeds.GenericCSVData(
fromdate=start,
todate=end,
dataname=fr"{output_dir}/{ticker}.csv",
dtformat=("%Y-%m-%d"),
openinterest=-1,
nullvalue=0.0,
plot=False,
)
cerebro.adddata(data)
cerebro.broker.setcash(1_000_000)
# Keep track of the created order to avoid a 2nd order
self.order = self.buy()
else:
if self.dataclose[0] < self.sma[0]:
# SELL, SELL, SELL!!! (with all possible default parameters)
self.log('SELL CREATE, %.2f' % self.dataclose[0])
# Keep track of the created order to avoid a 2nd order
self.order = self.sell()
if __name__ == '__main__':
cerebro = bt.Cerebro() # create a "Cerebro" engine instance
cerebro.addstrategy(SmaCross) # Add the trading strategy
data = MyTDXCSVData(dataname='C2105.csv')
cerebro.adddata(data) # Add the data feed
# Set our desired cash start
cerebro.broker.setcash(100000.0)
# Add a FixedSize sizer according to the stake
cerebro.addsizer(bt.sizers.FixedSize, stake=10)
# Set the commission - 0.1% ... divide by 100 to remove the %
cerebro.broker.setcommission(commission=0.001)
if self.dataclose[0] < self.sma[0]:
# SELL, SELL, SELL!!! (with all possible default parameters)
self.log('SELL CREATE, %.2f' % self.dataclose[0])
# Keep track of the created order to avoid a 2nd order
self.order = self.sell()
def stop(self):
self.log('(MA Period %2d) Ending Value %.2f' %
(self.params.maperiod, self.broker.getvalue()), doprint=True)
if __name__ == '__main__':
# Create a cerebro entity
cerebro = bt.Cerebro()
# Add a strategy
strats = cerebro.optstrategy(
TestStrategy,
maperiod=xrange(10, 31))
# Datas are in a subfolder of the samples. Need to find where the script is
# because it could have been called from anywhere
modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
datapath = os.path.join(modpath, './datas/yahoo/orcl-1995-2014.txt')
# Create a Data Feed
data = bt.feeds.YahooFinanceCSVData(
dataname=datapath,
# Do not pass values before this date
def runstrategy():
args = parse_args()
# Create a cerebro
cerebro = bt.Cerebro()
storekwargs = dict(token=args.token,
account=args.account,
practice=not args.live)
if not args.no_store:
store = StoreCls(**storekwargs)
if args.broker:
if args.no_store:
broker = BrokerCls(**storekwargs)
else:
broker = store.getbroker()
cerebro.setbroker(broker)
timeframe = bt.TimeFrame.TFrame(args.timeframe)
# Manage data1 parameters
tf1 = args.timeframe1
tf1 = bt.TimeFrame.TFrame(tf1) if tf1 is not None else timeframe
cp1 = args.compression1
cp1 = cp1 if cp1 is not None else args.compression
if args.resample or args.replay:
datatf = datatf1 = bt.TimeFrame.Ticks
datacomp = datacomp1 = 1
else:
datatf = timeframe
datacomp = args.compression
datatf1 = tf1
datacomp1 = cp1
fromdate = None
if args.fromdate:
dtformat = '%Y-%m-%d' + ('T%H:%M:%S' * ('T' in args.fromdate))
fromdate = datetime.datetime.strptime(args.fromdate, dtformat)
DataFactory = DataCls if args.no_store else store.getdata
datakwargs = dict(timeframe=datatf,
compression=datacomp,
qcheck=args.qcheck,
historical=args.historical,
fromdate=fromdate,
bidask=args.bidask,
useask=args.useask,
backfill_start=not args.no_backfill_start,
backfill=not args.no_backfill,
tz=args.timezone)
if args.no_store and not args.broker: # neither store nor broker
datakwargs.update(storekwargs) # pass the store args over the data
data0 = DataFactory(dataname=args.data0, **datakwargs)
data1 = None
if args.data1 is not None:
if args.data1 != args.data0:
datakwargs['timeframe'] = datatf1
datakwargs['compression'] = datacomp1
data1 = DataFactory(dataname=args.data1, **datakwargs)
else:
data1 = data0
rekwargs = dict(
timeframe=timeframe,
compression=args.compression,
bar2edge=not args.no_bar2edge,
adjbartime=not args.no_adjbartime,
rightedge=not args.no_rightedge,
takelate=not args.no_takelate,
)
if args.replay:
cerebro.replaydata(dataname=data0, **rekwargs)
if data1 is not None:
rekwargs['timeframe'] = tf1
rekwargs['compression'] = cp1
cerebro.replaydata(dataname=data1, **rekwargs)
elif args.resample:
cerebro.resampledata(dataname=data0, **rekwargs)
if data1 is not None:
rekwargs['timeframe'] = tf1
rekwargs['compression'] = cp1
cerebro.resampledata(dataname=data1, **rekwargs)
else:
cerebro.adddata(data0)
if data1 is not None:
cerebro.adddata(data1)
if args.valid is None:
valid = None
else:
valid = datetime.timedelta(seconds=args.valid)
# Add the strategy
cerebro.addstrategy(TestStrategy,
smaperiod=args.smaperiod,
trade=args.trade,
exectype=bt.Order.ExecType(args.exectype),
stake=args.stake,
stopafter=args.stopafter,
valid=valid,
cancel=args.cancel,
donotcounter=args.donotcounter,
sell=args.sell)
# Live data ... avoid long data accumulation by switching to "exactbars"
cerebro.run(exactbars=args.exactbars)
if args.exactbars < 1: # plotting is possible
if args.plot:
pkwargs = dict(style='line')
if args.plot is not True: # evals to True but is not True
npkwargs = eval('dict(' + args.plot + ')') # args were passed
pkwargs.update(npkwargs)
cerebro.plot(**pkwargs)
self.log('Price, %.4f' % close)
if self.obv_cul1 > 0 and self.long_headl2:
self.buy()
elif self.long_headl2 and self.obv5 > self.obv10:
self.buy()
if self.sma_cdl1:
self.close()
if __name__ == '__main__':
# backtest from the best result
cerebro = bt.Cerebro(stdstats=True)
data = Candle4H(
dataname=IGNIS_4H,
dtformat="%Y-%m-%d",
tmformat="%H:%M:%S",
fromdate=datetime.datetime(2019, 6, 1),
todate=datetime.datetime(2019, 7, 14),
)
cerebro.adddata(data)
cerebro.addstrategy(Strat)
cerebro.addsizer(bt.sizers.PercentSizer, percents=95)
cerebro.broker.set_cash(1000000)
cerebro.broker.setcommission(commission=0.001)
cerebro.run()
cerebro.plot(style='candlestick', barup='green', bardown='red')
def train_strategy(cls, training_data, stock_id):
"""
Find the optimized parameter of the stategy by using training data.
:param training_data(DataFrame): data used to train the strategy.
:param stock_id(integer): stock on which the strategy works.
:return: params(dict like {ma_periods: dict{ma_period_s: 1, ma_period_l: 2, stock_id: '0'}}
"""
# get the params list
params_list = cls.get_params_list(training_data, stock_id)
al_results = []
cerebro = bt.Cerebro()
# Change Data Type [https://www.backtrader.com/docu/dataautoref/#pandasdata]
#convert_dict = {'date': complex, 'high': float, 'low': float, 'close': float, 'volume': int}
#data = data.astype(convert_dict)
training_data = training_data.apply(pd.to_numeric)
training_data.head()
data = bt.feeds.PandasData(dataname=training_data)
cerebro.adddata(data)
cerebro.optstrategy(cls, ma_periods=params_list)
cerebro.addanalyzer(bt.analyzers.TimeReturn, _name='al_return',
timeframe=bt.analyzers.TimeFrame.NoTimeFrame)
cerebro.addanalyzer(bt.analyzers.TimeDrawDown, _name='al_max_drawdown')
cerebro.broker.setcash(conf.DEFAULT_CASH)
logger.debug(f'Starting train the strategy for stock {stock_id}...')
results = cerebro.run()
for result in results:
params = result[0].params
analyzers = result[0].analyzers
al_return_rate = analyzers.al_return.get_analysis()
total_return_rate = 0.0
for k, v in al_return_rate.items():
total_return_rate = v
al_result = dict(
params=params,
total_return_rate=total_return_rate,
max_drawdown=analyzers.al_max_drawdown.get_analysis().get('maxdrawdown'),
max_drawdown_period=analyzers.al_max_drawdown.get_analysis().get('maxdrawdownperiod')
)
al_results.append(al_result)
# Get the best params
best_al_result = bsu.Utils.get_best_params(al_results)
params = best_al_result.get('params')
ma_periods = params.ma_periods
logger.debug(
'Stock %s best parma is ma_period_s: %d, ma_period_l: %d' %
(
ma_periods.get('stock_id'),
ma_periods.get('ma_period_s'),
ma_periods.get('ma_period_l')
))
return params