Can we make it support other type of datframes to make it faster ?

[RBouallegue]

Maybe we can use VAEX type to make the running of the backtesting library faster for large data frames

[kernc]

The first performance improvement on a platform like Windos might be made by reenabling multiprocessing:

backtesting.py/backtesting/backtesting.py

Lines 1353 to 1380 in 65f54f6

	# Save necessary objects into "global" state; pass into concurrent executor
	# (and thus pickle) nothing but two numbers; receive nothing but numbers.
	# With start method "fork", children processes will inherit parent address space
	# in a copy-on-write manner, achieving better performance/RAM benefit.
	backtest_uuid = np.random.random()
	param_batches = list(_batch(param_combos))
	Backtest._mp_backtests[backtest_uuid] = (self, param_batches, maximize) # type: ignore
	try:
	# If multiprocessing start method is 'fork' (i.e. on POSIX), use
	# a pool of processes to compute results in parallel.
	# Otherwise (i.e. on Windos), sequential computation will be "faster".
	if mp.get_start_method(allow_none=False) == 'fork':
	with ProcessPoolExecutor() as executor:
	futures = [executor.submit(Backtest._mp_task, backtest_uuid, i)
	for i in range(len(param_batches))]
	for future in _tqdm(as_completed(futures), total=len(futures),
	desc='Backtest.optimize'):
	batch_index, values = future.result()
	for value, params in zip(values, param_batches[batch_index]):
	heatmap[tuple(params.values())] = value
	else:
	if os.name == 'posix':
	warnings.warn("For multiprocessing support in `Backtest.optimize()` "
	"set multiprocessing start method to 'fork'.")
	for batch_index in _tqdm(range(len(param_batches))):
	_, values = Backtest._mp_task(backtest_uuid, batch_index)
	for value, params in zip(values, param_batches[batch_index]):
	heatmap[tuple(params.values())] = value

I welcome your investigation.