refs https://github.com/kernc/backtesting.py/issues/8 fixes https://github.com/kernc/backtesting.py/issues/28 closes https://github.com/kernc/backtesting.py/issues/77 closes https://github.com/kernc/backtesting.py/issues/83 closes https://github.com/kernc/backtesting.py/issues/92

New features:

• Add order sizing, pyramiding, order types (stop-limit order) (fixes https://github.com/kernc/backtesting.py/issues/3, fixes https://github.com/kernc/backtesting.py/issues/10, closes https://github.com/kernc/backtesting.py/pull/24, closes https://github.com/kernc/backtesting.py/issues/76, closes https://github.com/kernc/backtesting.py/issues/106),
• Strategy.orders list of active orders,
• Strategy.trades list of active trades,
• Strategy.closed_trades list of settled trades,
• Order class, representing an active order,
• Trade class, representing an active or already settled trade,
• auto-downsample plots when too much data (fixes https://github.com/kernc/backtesting.py/issues/35),
• New stats keys (fixes https://github.com/kernc/backtesting.py/issues/4, fixes https://github.com/kernc/backtesting.py/issues/29):
  • _equity_curve, equity, drawdown, and drawdown duration,
  • _trades, DataFrame of executed trades.

Breakages:

• Need to call explicit Strategy.position.close() between subsequent Strategy.buy/sell() calls to approximate previous behavior.
• Strategy.buy(price=) → Strategy.buy(limit=)
• Strategy.buy/sell() only takes keyword parameters.
• Position.open_price → Trade.entry_price
• Position.open_time → Trade.entry_time
• Strategy.orders → Strategy.orders[i]
• Backtest.plot(omit_missing=) is gone.
• Revised backtesting.lib.SignalStrategy.

Expected Behavior

Returning the stats on the best run for backtest.run()

Actual Behavior

raised a BrokenProcessPool error : A process in the process pool was terminated abruptly while the future was running or pending.

Steps to Reproduce

bt = Backtest(df, Strat,, cash=starting_money, commission=.002) bt.run() stats = bt.optimize(tresholdVol=range(5,100, 5), maximize='Equity Final [$]')

Additional info

• Backtesting version:

When running an Optimize routine, it appears as though the child processes that are created aren't releasing memory after they finish. If I write a simple routine that just evaluates multiple SMA values for a closing price crossover (doesn't really matter what) the routine will gradually consume all memory available on the machine and then produces a "raised a BrokenProcessPool error : A process in the process pool was terminated abruptly while the future was running or pending." error. I am currently running in AWS on an Ubuntu box.

Thanks in advance.

Beginning of the routine:

After 5 minutes:

• Backtesting version: Backtesting 0.1.2

Expected Behavior

Calculate the strategy and display the result:

Start                     2004-08-19 00:00:00
End                       2013-03-01 00:00:00
Duration                   3116 days 00:00:00
Exposure [%]                          94.2875
Equity Final [$]                      69665.1
Equity Peak [$]                       69722.1
Return [%]                            596.651
Buy & Hold Return [%]                 703.458
Max. Drawdown [%]                    -33.6059
Avg. Drawdown [%]                    -5.62833
Max. Drawdown Duration      689 days 00:00:00
Avg. Drawdown Duration       41 days 00:00:00
# Trades                                   93
Win Rate [%]                          53.7634
Best Trade [%]                        56.9786
Worst Trade [%]                      -17.0259
Avg. Trade [%]                        2.44454
Max. Trade Duration         121 days 00:00:00
Avg. Trade Duration          32 days 00:00:00
Expectancy [%]                        6.91837
SQN                                   1.77227
Sharpe Ratio                         0.220629
Sortino Ratio                        0.541607
Calmar Ratio                        0.0727415
_strategy                            SmaCross
dtype: object

Actual Behavior

No trades were made.

Start                     2004-08-19 00:00:00
End                       2013-03-01 00:00:00
Duration                   3116 days 00:00:00
Exposure [%]                                0
Equity Final [$]                        10000
Equity Peak [$]                         10000
Return [%]                                  0
Buy & Hold Return [%]                 703.458
Max. Drawdown [%]                          -0
Avg. Drawdown [%]                         NaN
Max. Drawdown Duration                    NaN
Avg. Drawdown Duration                    NaN
# Trades                                    0
Win Rate [%]                              NaN
Best Trade [%]                            NaN
Worst Trade [%]                           NaN
Avg. Trade [%]                            NaN
Max. Trade Duration                       NaT
Avg. Trade Duration                       NaT
Expectancy [%]                            NaN
SQN                                       NaN
Sharpe Ratio                              NaN
Sortino Ratio                             NaN
Calmar Ratio                              NaN
_strategy                            SmaCross
dtype: object

Steps to Reproduce

1. Using the code

from backtesting.test import GOOG
from backtesting.test import SMA
from backtesting import Strategy
from backtesting.lib import crossover
from backtesting import Backtest


class SmaCross(Strategy):
    # Define the two MA lags as *class variables*
    # for later optimization
    n1 = 10
    n2 = 20

    def init(self):
        # Precompute two moving averages
        self.sma1 = self.I(SMA, self.data.Close, self.n1)
        self.sma2 = self.I(SMA, self.data.Close, self.n2)

    def next(self):
        # If sma1 crosses above sma2, buy the asset
        # Values ok here!
        print(self.data.index[-1],self.sma1[-1])
        if crossover(self.sma1, self.sma2):
            self.buy()

        # Else, if sma1 crosses below sma2, sell it
        elif crossover(self.sma2, self.sma1):
            self.sell()


bt = Backtest(GOOG, SmaCross, cash=10000, commission=.002)
print(bt.run())

2. Change the code to

from backtesting.test import GOOG
from backtesting.test import SMA
from backtesting import Strategy
from backtesting.lib import crossover
from backtesting import Backtest


class SmaCross(Strategy):
    # Define the two MA lags as *class variables*
    # for later optimization
    n1 = 10
    n2 = 20
    timeseries = {}

    def init(self):
        # Precompute two moving averages
        self.timeseries["sma1"] = self.I(SMA, self.data.Close, self.n1)
        self.timeseries["sma2"] = self.I(SMA, self.data.Close, self.n2)

    def next(self):
        # If sma1 crosses above sma2, buy the asset
        # Is showing the same value!
        print(self.data.index[-1], self.timeseries["sma1"][-1])
        if crossover(self.timeseries["sma1"], self.timeseries["sma2"]):
            self.buy()

        # Else, if sma1 crosses below sma2, sell it
        elif crossover(self.timeseries["sma2"], self.timeseries["sma1"]):
            self.sell()


bt = Backtest(GOOG, SmaCross, cash=10000, commission=.002)
print(bt.run())

Additional info

• Backtesting version: 0.1.7
• I'm working on a dynamic strategy feature, that's why i need use the dicts, how could i fix this?

Thanks

When I'm using the buy function with a stop loss / take profit argument I would expect that the order can only be closed on that specific stop loss / take profit price. But when I look at the chart (stop loss=0.66%, take profit=1%), I see that the average negative trade result is around -0.80% with as max -1.14752%. How is this possible?

Also why isn't it possible to optimize the take profit / stop loss rate by using the "optimize" function? Can this be added in a later release? Or can I help contribute?

Backtesting version: 0.1.7

It's a rare situation - you need both stop loss and new order to happen in the same time step - but when happens it is such a pain to nail down what's really wrong, since the framework won't break. Instead, all the data following will be slightly wrong. Started debugging this when trying to understand why ohlc_trades plot would draw two trades going on at the same time, if clearly existing positions need to be closed before opening new ones. That "double trade" plotting is result of that misaligned data due to the erroneous open position and instant stop loss.

Expected Behavior

Close the original position, ignore the stop loss and create a new order

Actual Behavior

Close the original position, create a new order, and instantly close it due to stop loss

Steps to Reproduce

1. Create a strategy that will produce a new sell order the same time step in which a stop loss threshold is reached.

Additional info

A couple of fix suggestions:

1. Update open, high, low once self._open_position(entry, is_long) is called (backtesting.py line 539)

backtesting.py line 532

if entry or orders._close:
    self._close_position()
    orders._close = False

################ THIS
# First make the entry order, if hit
if entry:
    if entry is _MARKET_PRICE or high > orders._entry > low:
        self._open_position(entry, is_long)
################ THIS

# Check if stop-loss threshold was hit
if sl and self._position:
    price = (sl if low <= sl <= high else              # hit
             open if (is_long and open < sl or         # gapped hit
                      not is_long and open > sl) else
             None)                                     # not hit
    if price is not None:
        self._close_position(price)
        self.orders.cancel()

# Check if take-profit threshold was hit
if tp and self._position:
    price = (tp if low < tp < high else
             open if (is_long and open > tp or
                      not is_long and open > sl) else
             None)
    if price is not None:
        self._close_position(price)
        self.orders.cancel()
   
###########################################
#### SHOULD PROBABLY BE MOVED HERE #######
###########################################

I am seeing this error when try to run backtest with 10000 days worth data. It doesn't happen for all the stocks, but some. e.g AAPL, AMZN.

Index: 1946  Close: 35.88999938964844  ATR: 1.6433353276970901
Index: 1947  Close: 35.779998779296875  ATR: 1.5673829350797757
Index: 1948  Close: 35.779998779296875  ATR: 1.5232842084983242
Index: 1949  Close: 36.029998779296875  ATR: 1.4951925556138177
Index: 1950  Close: 36.13999938964844  ATR: 1.438393141851363
Index: 1951  Close: 35.65999984741211  ATR: 1.3906505347952947
Traceback (most recent call last):
  File "backtesting_usa.py", line 116, in <module>
    output = bt.run()
  File "/usr/local/lib/python3.8/site-packages/backtesting/backtesting.py", line 1165, in run
    strategy.next()
  File "backtesting_usa.py", line 83, in next
    super().next()
  File "/usr/local/lib/python3.8/site-packages/backtesting/lib.py", line 446, in next
    trade.sl = max(trade.sl or -np.inf,
  File "/usr/local/lib/python3.8/site-packages/backtesting/backtesting.py", line 633, in sl
    self.__set_contingent('sl', price)
  File "/usr/local/lib/python3.8/site-packages/backtesting/backtesting.py", line 652, in __set_contingent
    assert price is None or 0 < price < np.inf
AssertionError

I tried printing the index values for each iteration and I found that for this dataset, it processed till index 1951 and failed on 1952.

If I reduce the backtesting period to 5000, it works because it doesn't come across that piece of data (index 1952). I checked that data and verified the close price, and calculated ATR(14).

Below is the code snippet:

class Breakout(TrailingStrategy):
	timeperiod = 10
	position_size_decimal = 0.2
	min_close = []
	max_close = []
	def init(self):
		super().init()
		self.ema20 = self.I(EMA,self.data.Close,20,overlay=True)
		self.atr14 = self.I(ATR,self.data.High,self.data.Low,self.data.Close,14)
		self.set_atr_periods(14)
		self.set_trailing_sl(1)
		print(type(self.data.Close))
		self.min_close, self.max_close = MINMAX(self.data.Close, timeperiod=self.timeperiod)

	def next(self):
		super().next()
		index = len(self.data)-1
		print('Index: '+f'{index} '+' Close: '+f'{self.data.Close[index]} '+' ATR: '+f'{self.atr14[index]}')
		if not self.position.is_long and self.min_close[index] > (self.max_close[index] * 0.98) and self.max_close[index] < (self.min_close[index] * 1.02):
			#print('BUY ZONE:'+ f'{self.data.index[index]} {self.rsi2[index]} {self.rsi5[index]} {self.ema200[index]} {self.data.Close[index]}')
			self.buy(size=self.position_size_decimal)

I download data using yfinance python api. Any help will be greatly appreciated.

Originally posted by @zlpatel in https://github.com/kernc/backtesting.py/discussions/315

Expected Behavior

I would expect that the trade would open the 'longLine' and close on the 'sma' line (in case of a long trade), but the trade opens/closes always on the close price. Is there a way to make sure that the the trade opens on the long/short line and closes on the sma line. These lines are based on the previous candle close value, so these are fixed values.

Code

def next(self):
    if not self.position:
        longLine = self.sma[-2] - (self.sma[-2] * (self.longLineParam / 100))
        shortLine = self.sma[-2] + (self.sma[-2] * (self.shortLineParam / 100))
        if self.data.Close[-1] <= longLine:
            self.buy()
        elif self.data.Close[-1] >= shortLine:
            self.sell()
    
    elif self.position:
        if cross(self.data.Close, self.sma[-2]):
            self.position.close()

Additional info

Example:

Fixes https://github.com/kernc/backtesting.py/issues/197 Fixes https://github.com/kernc/backtesting.py/issues/53

Hello again. Many apologies for my butchering of the PR process. Git is certainly something I am no expert at. :cry:

This is another PR which implements the tagging feature, makes tags an object type and fixes previous pep8 issues.

I am currently testing this for the first time on my own trading platform. The code seems to work fine to persist tags at the moment, but there are far too many trades being taken out, so unless that's a likely outcome of these changes, that problem is likely with my code outside of backtesting.py.

Expected Behavior

Note this is nitpicky but I noticed a few people misunderstanding the legend when I showed it to them so only a focus group of a few, take the following with a grain of salt.

The plot legend shows the peak and final equity value as a % but it doesn't subtract the original 100% from the result. This gives the impression that the peak and final "Return" of the strategy was 100% higher than it actually was. I fully recognize that the use of the word "return" was inferred by my brain, not by the chart, but I don't think I'm alone in keying in on the % sign as an indicator of returns.

This would be fine if it were a "growth of $100" type of chart and used a $ instead of a % However because it is labeled in % (which would be my preference) then perhaps consider subtracting 100% from the legend Peak % and Final %. I think it is perfectly reasonable to show the y-axis starting at 100% but if you wanted to get technical, you could change it to a 0% starting point so everything lined up.

Actual Behavior

The chart shows the percent of the original investment, for example in the image below, the "Peak (690%)" and "Final (689%)" imply 690% and 689% of the original invested amount, not a 690% /689% return. The actual returns would have only been 590%/589% over that time period.

Again, this was just something someone else pointed out to me as a bother and I think tend to agree. It is certainly not a priority or even consensus.

@kernc I am working an issue where I need to use the abstract module of talib

from backtesting import Backtest, Strategy
from backtesting.lib import crossover

from backtesting.test import GOOG
from talib import abstract


class SmaCross(Strategy):
    def init(self):
        Ind = abstract.Function('sma')
        inputs = {
            'open': self.data.Open,
            'high': self.data.High,
            'low': self.data.Low,
            'close': self.data.Close,
            'volume': self.data.Volume
        }
        self.ma1 = self.I(Ind, inputs, 10)
        self.ma2 = self.I(Ind, inputs, 20)

    def next(self):
        if crossover(self.ma1, self.ma2):
            self.buy()
        elif crossover(self.ma2, self.ma1):
            self.sell()


bt = Backtest(GOOG, SmaCross,
              cash=10000, commission=.002)
print(bt.run())
bt.plot()

I need this feature as I am dynamically getting Indicators using just function name

Running this ends up in this error

Traceback (most recent call last):
  File "/home/skywalker/PycharmProjects/backtester/backtest.py", line 30, in <module>
    print(bt.run())
  File "/home/skywalker/PycharmProjects/backtester/venv/lib/python3.8/site-packages/backtesting/backtesting.py", line 692, in run
    strategy.init()
  File "/home/skywalker/PycharmProjects/backtester/backtest.py", line 18, in init
    self.ma1 = self.I(Ind, inputs, 10)
  File "/home/skywalker/PycharmProjects/backtester/venv/lib/python3.8/site-packages/backtesting/backtesting.py", line 118, in I
    func_name = _as_str(func)
  File "/home/skywalker/PycharmProjects/backtester/venv/lib/python3.8/site-packages/backtesting/_util.py", line 24, in _as_str
    name = value.__name__.replace('<lambda>', 'λ')
AttributeError: 'Function' object has no attribute '__name__'

The issue seems to be way the function is constructed later in the backtesting.py

        if name is None:
            params = ','.join(filter(None, map(_as_str, chain(args, kwargs.values()))))
            func_name = _as_str(func)
            name = ('{}({})' if params else '{}').format(func_name, params)

Any tips on how to get around it?

• Backtesting version: latest master

Expected Behavior

Graph should not be empty

Actual Behavior

There are also tons of warnings in the console...

/Users/lennard/opt/miniconda3/envs/torch/lib/python3.9/site-packages/backtesting/_plotting.py:122: UserWarning:

Data contains too many candlesticks to plot; downsampling to '1H'. See `Backtest.plot(resample=...)`

/Users/lennard/opt/miniconda3/envs/torch/lib/python3.9/site-packages/backtesting/_plotting.py:148: FutureWarning:

Passing method to Int64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

BokehDeprecationWarning: Passing lists of formats for DatetimeTickFormatter scales was deprecated in Bokeh 3.0. Configure a single string format for each scale
/Users/lennard/opt/miniconda3/envs/torch/lib/python3.9/site-packages/bokeh/models/formatters.py:399: UserWarning:

DatetimeFormatter scales now only accept a single format. Using the first prodvided: '%d %b' 

BokehDeprecationWarning: Passing lists of formats for DatetimeTickFormatter scales was deprecated in Bokeh 3.0. Configure a single string format for each scale
/Users/lennard/opt/miniconda3/envs/torch/lib/python3.9/site-packages/bokeh/models/formatters.py:399: UserWarning:

DatetimeFormatter scales now only accept a single format. Using the first prodvided: '%m/%Y' 

Steps to Reproduce

... doesn't matter ...

bt = Backtest(dfpl, MyStrategy, cash=100, margin=1/10, commission=.0)

# backtesting.set_bokeh_output(notebook=False) - doesn't matter

bt.plot(show_legend=False)

Additional info

• Backtesting version: 0.3.3
• bokeh.__version__: 3.0.2
• Using Jupyter Notebook
• OS: MacOS Ventura 13.0 (Apple Silicon)

yes there are stats if I run the strategy:

Start                     2020-01-01 22:00:00
End                       2020-12-31 21:55:00
Duration                    364 days 23:55:00
Exposure Time [%]                    4.700638
Equity Final [$]                   139.304018
Equity Peak [$]                    139.533855
Return [%]                          39.304018
Buy & Hold Return [%]                8.932887
Return (Ann.) [%]                   30.478123
Volatility (Ann.) [%]               17.919015
Sharpe Ratio                         1.700882
Sortino Ratio                        3.531841
Calmar Ratio                         4.042517
Max. Drawdown [%]                   -7.539393
Avg. Drawdown [%]                   -0.858869
Max. Drawdown Duration       55 days 19:15:00
Avg. Drawdown Duration        3 days 12:49:00
# Trades                                  748
Win Rate [%]                        49.465241
Best Trade [%]                       0.222753
Worst Trade [%]                     -0.230765
Avg. Trade [%]                        0.00461
Max. Trade Duration           2 days 00:45:00
Avg. Trade Duration           0 days 00:27:00
Profit Factor                        1.269556
Expectancy [%]                       0.004625
SQN                                  2.384091
_strategy                          MyStrategy
_equity_curve                             ...
_trades                        Size  Entry...
dtype: object

commission= is now applied twice as common with brokers. spread= takes the role commission= had previously.

Fixes https://github.com/kernc/backtesting.py/issues/149 Fixes https://github.com/kernc/backtesting.py/issues/113 Closes https://github.com/kernc/backtesting.py/pull/662

The example doc "Parameter Heatmap", section "Model-based optimization". https://kernc.github.io/backtesting.py/doc/examples/Parameter%20Heatmap%20&%20Optimization.html

When I run this snippet I get an error.

stats_skopt, heatmap, optimize_result = backtest.optimize(
     n1=[10, 100], # Note: For method="skopt", we
     n2=[20, 200], # only need interval end-points
     n_enter=[10, 40],
     n_exit=[10, 30],
     constraint=lambda p: p.n_exit < p.n_enter < p.n1 < p.n2,
     maximize='Equity Final [$]',
     method='skopt',
     max_tries=200,
     random_state=0,
     return_heatmap=True,
     return_optimization=True)

---------------------------------------------------------------------------
InvalidParameterError                     Traceback (most recent call last)
File <timed exec>:1, in <module>

File ~/anaconda3/envs/trade/lib/python3.8/site-packages/backtesting/backtesting.py:1490, in Backtest.optimize(self, maximize, method, max_tries, constraint, return_heatmap, return_optimization, random_state, **kwargs)
   1488     output = _optimize_grid()
   1489 elif method == 'skopt':
-> 1490     output = _optimize_skopt()
   1491 else:
   1492     raise ValueError(f"Method should be 'grid' or 'skopt', not {method!r}")

File ~/anaconda3/envs/trade/lib/python3.8/site-packages/backtesting/backtesting.py:1456, in Backtest.optimize.<locals>._optimize_skopt()
   1452 with warnings.catch_warnings():
   1453     warnings.filterwarnings(
   1454         'ignore', 'The objective has been evaluated at this point before.')
-> 1456     res = forest_minimize(
   1457         func=objective_function,
   1458         dimensions=dimensions,
   1459         n_calls=max_tries,
   1460         base_estimator=ExtraTreesRegressor(n_estimators=20, min_samples_leaf=2),
   1461         acq_func='LCB',
   1462         kappa=3,
   1463         n_initial_points=min(max_tries, 20 + 3 * len(kwargs)),
   1464         initial_point_generator='lhs',  # 'sobel' requires n_initial_points ~ 2**N
   1465         callback=DeltaXStopper(9e-7),
   1466         random_state=random_state)
   1468 stats = self.run(**dict(zip(kwargs.keys(), res.x)))
   1469 output = [stats]

File ~/anaconda3/envs/trade/lib/python3.8/site-packages/skopt/optimizer/forest.py:186, in forest_minimize(func, dimensions, base_estimator, n_calls, n_random_starts, n_initial_points, acq_func, initial_point_generator, x0, y0, random_state, verbose, callback, n_points, xi, kappa, n_jobs, model_queue_size)
     10 def forest_minimize(func, dimensions, base_estimator="ET", n_calls=100,
     11                     n_random_starts=None, n_initial_points=10, acq_func="EI",
     12                     initial_point_generator="random",
     13                     x0=None, y0=None, random_state=None, verbose=False,
     14                     callback=None, n_points=10000, xi=0.01, kappa=1.96,
     15                     n_jobs=1, model_queue_size=None):
     16     """Sequential optimisation using decision trees.
     17 
     18     A tree based regression model is used to model the expensive to evaluate
   (...)
    184         :class:`skopt.dummy_minimize`, :class:`skopt.gbrt_minimize`
    185     """
--> 186     return base_minimize(func, dimensions, base_estimator,
    187                          n_calls=n_calls, n_points=n_points,
    188                          n_random_starts=n_random_starts,
    189                          n_initial_points=n_initial_points,
    190                          initial_point_generator=initial_point_generator,
    191                          x0=x0, y0=y0, random_state=random_state,
    192                          n_jobs=n_jobs,
    193                          acq_func=acq_func,
    194                          xi=xi, kappa=kappa, verbose=verbose,
    195                          callback=callback, acq_optimizer="sampling",
    196                          model_queue_size=model_queue_size)

File ~/anaconda3/envs/trade/lib/python3.8/site-packages/skopt/optimizer/base.py:300, in base_minimize(func, dimensions, base_estimator, n_calls, n_random_starts, n_initial_points, initial_point_generator, acq_func, acq_optimizer, x0, y0, random_state, verbose, callback, n_points, n_restarts_optimizer, xi, kappa, n_jobs, model_queue_size)
    298 next_x = optimizer.ask()
    299 next_y = func(next_x)
--> 300 result = optimizer.tell(next_x, next_y)
    301 result.specs = specs
    302 if eval_callbacks(callbacks, result):

File ~/anaconda3/envs/trade/lib/python3.8/site-packages/skopt/optimizer/optimizer.py:493, in Optimizer.tell(self, x, y, fit)
    490         y = list(y)
    491         y[1] = log(y[1])
--> 493 return self._tell(x, y, fit=fit)

File ~/anaconda3/envs/trade/lib/python3.8/site-packages/skopt/optimizer/optimizer.py:536, in Optimizer._tell(self, x, y, fit)
    534 with warnings.catch_warnings():
    535     warnings.simplefilter("ignore")
--> 536     est.fit(self.space.transform(self.Xi), self.yi)
    538 if hasattr(self, "next_xs_") and self.acq_func == "gp_hedge":
    539     self.gains_ -= est.predict(np.vstack(self.next_xs_))

File ~/anaconda3/envs/trade/lib/python3.8/site-packages/sklearn/ensemble/_forest.py:341, in BaseForest.fit(self, X, y, sample_weight)
    314 def fit(self, X, y, sample_weight=None):
    315     """
    316     Build a forest of trees from the training set (X, y).
    317 
   (...)
    339         Fitted estimator.
    340     """
--> 341     self._validate_params()
    343     # Validate or convert input data
    344     if issparse(y):

File ~/anaconda3/envs/trade/lib/python3.8/site-packages/sklearn/base.py:570, in BaseEstimator._validate_params(self)
    562 def _validate_params(self):
    563     """Validate types and values of constructor parameters
    564 
    565     The expected type and values must be defined in the `_parameter_constraints`
   (...)
    568     accepted constraints.
    569     """
--> 570     validate_parameter_constraints(
    571         self._parameter_constraints,
    572         self.get_params(deep=False),
    573         caller_name=self.__class__.__name__,
    574     )

File ~/anaconda3/envs/trade/lib/python3.8/site-packages/sklearn/utils/_param_validation.py:97, in validate_parameter_constraints(parameter_constraints, params, caller_name)
     91 else:
     92     constraints_str = (
     93         f"{', '.join([str(c) for c in constraints[:-1]])} or"
     94         f" {constraints[-1]}"
     95     )
---> 97 raise InvalidParameterError(
     98     f"The {param_name!r} parameter of {caller_name} must be"
     99     f" {constraints_str}. Got {param_val!r} instead."
    100 )

InvalidParameterError: The 'criterion' parameter of ExtraTreesRegressor must be a str among {'squared_error', 'absolute_error', 'poisson', 'friedman_mse'}. Got 'mse' instead.

• Backtesting version: 0.3.3
• bokeh.__version__: Python 3.11, bokeh 2.4.3; Python 3.8.11, bokeh 3.0.3;
• scikit-optimize 0.9.0
• OS: Ubuntu 20.04

Very concretely, I wish for there to be added support for tick data in the backtest.

One example of how it could work:

• Your backtest is on the 5m timeframe
• You can additionally pass a 1m timeframe or even down to 1 second data

Benefits:

• More accurate results when backtesting, more accurate trigger on stop loss and take profits
• More flexibility in your strategies, e.g. you can implement an approach to check if the tick distribution fits your assumptions

Cons:

• Could possibly be slower because of increased size of data, but this can be resolved with making it optional and keep the normal functionality just as fast as always

Expected Behavior

I installed the tool and all required libraries on Ubuntu 22.04, Python 3.11.0 and ta-lib (compiled from latest source code 0.4.0). The backtest from the example code on home page was run. The test itself ran without

problems but the plot html does not work. It should show the graph.

Actual Behavior

Please see the attached. The html layout is not working. Strangely, no JS error from browser console.

Steps to Reproduce

1. Install backtesting.py
2. Run the example code on the project home page
3. Open the plot in IE Edge

Additional info

• Backtesting version: 0.3.3

Expected Behavior

I wanna use it over 10000 index with this command(index_col='time', parse_dates=True) without the error ,when I import the csv.

Of cource I should type the code like follow. df = pd.read_csv("df.csv")

But the date didn't recognize ,when I don't use index_col='time', parse_dates=True. And the follow error occurred ,when I import the csvfile over 10000 index.There is reproducibility.

Actual Behavior

I read the csv by follow code and csv

code filename = "raw.csv" df = pd.read_csv(filename,index_col='time', parse_dates=True)

csvdata example actual data have 10002 index. time | Open | High | Low | Close | Volume -- | -- | -- | -- | -- | -- 2020/1/1 0:01 | 7187.67 | 7188.06 | 7182.2 | 7184.03 | 7.248148 2020/1/1 0:02 | 7184.41 | 7184.71 | 7180.26 | 7182.43 | 11.68168 2020/1/1 0:03 | 7183.83 | 7188.94 | 7182.49 | 7185.94 | 10.02539

Line 10001 does not give an error. But an error occurs when the csv reaches 10002 rows. This error occurred when reading CSV with the following command.

target code index_col='time', parse_dates=True

Steps to Reproduce

C:\Users\username\AppData\Local\Programs\Python\Python36\lib\site-packages\backtesting\_plotting.py:122: UserWarning: Data contains too many candlesticks to plot; downsampling to '1T'. See `Backtest.plot(resample=...)`
  warnings.warn(f"Data contains too many candlesticks to plot; downsampling to {freq!r}. "
Traceback (most recent call last):
  File "C:\Users\username\AppData\Local\Programs\Python\Python36\lib\site-packages\pandas\core\groupby\generic.py", line 261, in aggregate
    func, *args, engine=engine, engine_kwargs=engine_kwargs, **kwargs
  File "C:\Users\username\AppData\Local\Programs\Python\Python36\lib\site-packages\pandas\core\groupby\groupby.py", line 1085, in _python_agg_general
    result, counts = self.grouper.agg_series(obj, f)
  File "C:\Users\username\AppData\Local\Programs\Python\Python36\lib\site-packages\pandas\core\groupby\ops.py", line 904, in agg_series
    return grouper.get_result()
  File "pandas\_libs\reduction.pyx", line 164, in pandas._libs.reduction.SeriesBinGrouper.get_result
  File "pandas\_libs\reduction.pyx", line 76, in pandas._libs.reduction._BaseGrouper._apply_to_group
  File "C:\Users\username\AppData\Local\Programs\Python\Python36\lib\site-packages\pandas\core\groupby\groupby.py", line 1062, in <lambda>
    f = lambda x: func(x, *args, **kwargs)
  File "C:\Users\username\AppData\Local\Programs\Python\Python36\lib\site-packages\backtesting\_plotting.py", line 147, in f
    mean_time = int(bars.loc[s.index].view(int).mean())
  File "C:\Users\username\AppData\Local\Programs\Python\Python36\lib\site-packages\pandas\core\series.py", line 668, in view
    self._values.view(dtype), index=self.index
  File "C:\Users\username\AppData\Local\Programs\Python\Python36\lib\site-packages\pandas\core\series.py", line 314, in __init__
    f"Length of passed values is {len(data)}, "
ValueError: Length of passed values is 2, index implies 1.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "1.py", line 183, in <module>
    bt.plot() 
  File "C:\Users\username\AppData\Local\Programs\Python\Python36\lib\site-packages\backtesting\backtesting.py", line 1609, in plot
    open_browser=open_browser)
  File "C:\Users\username\AppData\Local\Programs\Python\Python36\lib\site-packages\backtesting\_plotting.py", line 204, in plot
    resample, df, indicators, equity_data, trades)
  File "C:\Users\username\AppData\Local\Programs\Python\Python36\lib\site-packages\backtesting\_plotting.py", line 158, in _maybe_resample_data
    ExitBar=_group_trades('ExitTime'),
  File "C:\Users\username\AppData\Local\Programs\Python\Python36\lib\site-packages\pandas\core\resample.py", line 288, in aggregate
    result, how = self._aggregate(func, *args, **kwargs)
  File "C:\Users\username\AppData\Local\Programs\Python\Python36\lib\site-packages\pandas\core\base.py", line 416, in _aggregate
    result = _agg(arg, _agg_1dim)
  File "C:\Users\username\AppData\Local\Programs\Python\Python36\lib\site-packages\pandas\core\base.py", line 383, in _agg
    result[fname] = func(fname, agg_how)
  File "C:\Users\username\AppData\Local\Programs\Python\Python36\lib\site-packages\pandas\core\base.py", line 367, in _agg_1dim
    return colg.aggregate(how)
  File "C:\Users\username\AppData\Local\Programs\Python\Python36\lib\site-packages\pandas\core\groupby\generic.py", line 267, in aggregate
    result = self._aggregate_named(func, *args, **kwargs)
  File "C:\Users\username\AppData\Local\Programs\Python\Python36\lib\site-packages\pandas\core\groupby\generic.py", line 480, in _aggregate_named
    output = func(group, *args, **kwargs)
  File "C:\Users\username\AppData\Local\Programs\Python\Python36\lib\site-packages\backtesting\_plotting.py", line 147, in f
    mean_time = int(bars.loc[s.index].view(int).mean())
  File "C:\Users\username\AppData\Local\Programs\Python\Python36\lib\site-packages\pandas\core\series.py", line 668, in view
    self._values.view(dtype), index=self.index
  File "C:\Users\username\AppData\Local\Programs\Python\Python36\lib\site-packages\pandas\core\series.py", line 314, in __init__
    f"Length of passed values is {len(data)}, "
ValueError: Length of passed values is 2, index implies 1.

Additional info

• Backtesting version: 0.3.3
• My python 3.6.8
• win10