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records module

Numba-compiled functions for portfolio records.

apply_weights_to_orders_nb function

apply_weights_to_orders_nb(
    order_records,
    col_arr,
    weights
)

Apply weights to order records.

best_price_idx_nb function

best_price_idx_nb(
    records,
    open,
    high,
    low,
    close,
    entry_price_open=False,
    exit_price_close=False,
    max_duration=None,
    relative=True
)

Get index of best price by applying trade_best_worst_price_nb on each trade.

best_price_nb function

best_price_nb(
    records,
    open,
    high,
    low,
    close,
    entry_price_open=False,
    exit_price_close=False,
    max_duration=None
)

Get best price by applying trade_best_worst_price_nb on each trade.

copy_trade_record_nb function

copy_trade_record_nb(
    new_records,
    r,
    trade_record
)

Copy a trade record.

edge_ratio_nb function

edge_ratio_nb(
    records,
    col_map,
    open,
    high,
    low,
    close,
    volatility,
    entry_price_open=False,
    exit_price_close=False,
    max_duration=None
)

Get edge ratio of each column.

expanding_best_price_nb function

expanding_best_price_nb(
    records,
    open,
    high,
    low,
    close,
    entry_price_open=False,
    exit_price_close=False,
    max_duration=None
)

Get expanding best price of each trade.

expanding_mae_nb function

expanding_mae_nb(
    records,
    expanding_worst_price,
    use_returns=False
)

Get expanding MAE of each trade.

expanding_mfe_nb function

expanding_mfe_nb(
    records,
    expanding_best_price,
    use_returns=False
)

Get expanding MFE of each trade.

expanding_worst_price_nb function

expanding_worst_price_nb(
    records,
    open,
    high,
    low,
    close,
    entry_price_open=False,
    exit_price_close=False,
    max_duration=None
)

Get expanding worst price of each trade.

expectancy_reduce_nb function

expectancy_reduce_nb(
    pnl_arr
)

Expectancy of a PnL array.

fill_entry_trades_in_position_nb function

fill_entry_trades_in_position_nb(
    order_records,
    col_map,
    col,
    sim_start,
    sim_end,
    first_c,
    last_c,
    init_price,
    first_entry_size,
    first_entry_fees,
    exit_idx,
    exit_size_sum,
    exit_gross_sum,
    exit_fees_sum,
    direction,
    status,
    parent_id,
    new_records,
    r
)

Fill entry trades located within a single position.

Returns the next trade id.

fill_position_record_nb function

fill_position_record_nb(
    new_records,
    r,
    trade_records
)

Fill a position record by aggregating trade records.

fill_trade_record_nb function

fill_trade_record_nb(
    new_records,
    r,
    col,
    size,
    entry_order_id,
    entry_idx,
    entry_price,
    entry_fees,
    exit_order_id,
    exit_idx,
    exit_price,
    exit_fees,
    direction,
    status,
    parent_id
)

Fill a trade record.

get_entry_trades_nb function

get_entry_trades_nb(
    order_records,
    close,
    col_map,
    init_position=0.0,
    init_price=nan,
    sim_start=None,
    sim_end=None
)

Fill entry trade records by aggregating order records.

Entry trade records are buy orders in a long position and sell orders in a short position.

Usage

>>> from vectorbtpro import *

>>> close = order_price = np.array([
...     [1, 6],
...     [2, 5],
...     [3, 4],
...     [4, 3],
...     [5, 2],
...     [6, 1]
... ])
>>> size = np.array([
...     [1, -1],
...     [0.1, -0.1],
...     [-1, 1],
...     [-0.1, 0.1],
...     [1, -1],
...     [-2, 2]
... ])
>>> target_shape = close.shape
>>> group_lens = np.full(target_shape[1], 1)
>>> init_cash = np.full(target_shape[1], 100)

>>> sim_out = vbt.pf_nb.from_orders_nb(
...     target_shape,
...     group_lens,
...     init_cash=init_cash,
...     size=size,
...     price=close,
...     fees=np.asarray([[0.01]]),
...     slippage=np.asarray([[0.01]])
... )

>>> col_map = vbt.rec_nb.col_map_nb(sim_out.order_records['col'], target_shape[1])
>>> entry_trade_records = vbt.pf_nb.get_entry_trades_nb(sim_out.order_records, close, col_map)
>>> pd.DataFrame.from_records(entry_trade_records)
   id  col  size  entry_order_id  entry_idx  entry_price  entry_fees  \
0   0    0   1.0               0          0         1.01     0.01010
1   1    0   0.1               1          1         2.02     0.00202
2   2    0   1.0               4          4         5.05     0.05050
3   3    0   1.0               5          5         5.94     0.05940
4   0    1   1.0               0          0         5.94     0.05940
5   1    1   0.1               1          1         4.95     0.00495
6   2    1   1.0               4          4         1.98     0.01980
7   3    1   1.0               5          5         1.01     0.01010

   exit_order_id  exit_idx  exit_price  exit_fees       pnl    return  \
0              3         3    3.060000   0.030600  2.009300  1.989406
1              3         3    3.060000   0.003060  0.098920  0.489703
2              5         5    5.940000   0.059400  0.780100  0.154475
3             -1         5    6.000000   0.000000 -0.119400 -0.020101
4              3         3    3.948182   0.039482  1.892936  0.318676
5              3         3    3.948182   0.003948  0.091284  0.184411
6              5         5    1.010000   0.010100  0.940100  0.474798
7             -1         5    1.000000   0.000000 -0.020100 -0.019901

   direction  status  parent_id
0          0       1          0
1          0       1          0
2          0       1          1
3          1       0          2
4          1       1          0
5          1       1          0
6          1       1          1
7          0       0          2

get_exit_trades_nb function

get_exit_trades_nb(
    order_records,
    close,
    col_map,
    init_position=0.0,
    init_price=nan,
    sim_start=None,
    sim_end=None
)

Fill exit trade records by aggregating order records.

Exit trade records are sell orders in a long position and buy orders in a short position.

Usage

>>> exit_trade_records = vbt.pf_nb.get_exit_trades_nb(sim_out.order_records, close, col_map)
>>> pd.DataFrame.from_records(exit_trade_records)
   id  col  size  entry_order_id  entry_idx  entry_price  entry_fees  \
0   0    0   1.0               0          0     1.101818    0.011018
1   1    0   0.1               0          0     1.101818    0.001102
2   2    0   1.0               4          4     5.050000    0.050500
3   3    0   1.0               5          5     5.940000    0.059400
4   0    1   1.0               0          0     5.850000    0.058500
5   1    1   0.1               0          0     5.850000    0.005850
6   2    1   1.0               4          4     1.980000    0.019800
7   3    1   1.0               5          5     1.010000    0.010100

   exit_order_id  exit_idx  exit_price  exit_fees       pnl    return  \
0              2         2        2.97    0.02970  1.827464  1.658589
1              3         3        3.96    0.00396  0.280756  2.548119
2              5         5        5.94    0.05940  0.780100  0.154475
3             -1         5        6.00    0.00000 -0.119400 -0.020101
4              2         2        4.04    0.04040  1.711100  0.292496
5              3         3        3.03    0.00303  0.273120  0.466872
6              5         5        1.01    0.01010  0.940100  0.474798
7             -1         5        1.00    0.00000 -0.020100 -0.019901

   direction  status  parent_id
0          0       1          0
1          0       1          0
2          0       1          1
3          1       0          2
4          1       1          0
5          1       1          0
6          1       1          1
7          0       0          2

get_long_view_orders_nb function

get_long_view_orders_nb(
    order_records,
    close,
    col_map,
    init_position=0.0,
    init_price=nan,
    sim_start=None,
    sim_end=None
)

Get view of orders in long positions only.

get_position_feature_nb function

get_position_feature_nb(
    order_records,
    close,
    col_map,
    feature=0,
    init_position=0.0,
    init_price=nan,
    fill_closed_position=False,
    fill_exit_price=True,
    sim_start=None,
    sim_end=None
)

Get the position's feature at each time step.

For the list of supported features see PositionFeature.

If fill_exit_price is True and a part of the position is not closed yet, will fill the exit price as if the part was closed using the current close.

If fill_closed_position is True, will forward-fill missing values with the prices of the previously closed position.

get_positions_nb function

get_positions_nb(
    trade_records,
    col_map
)

Fill position records by aggregating trade records.

Trades can be entry trades, exit trades, and even positions themselves - all will produce the same results.

Usage

>>> col_map = vbt.rec_nb.col_map_nb(exit_trade_records['col'], target_shape[1])
>>> position_records = vbt.pf_nb.get_positions_nb(exit_trade_records, col_map)
>>> pd.DataFrame.from_records(position_records)
   id  col  size  entry_order_id  entry_idx  entry_price  entry_fees  \
0   0    0   1.1               0          0     1.101818     0.01212
1   1    0   1.0               4          4     5.050000     0.05050
2   2    0   1.0               5          5     5.940000     0.05940
3   0    1   1.1               0          0     5.850000     0.06435
4   1    1   1.0               4          4     1.980000     0.01980
5   2    1   1.0               5          5     1.010000     0.01010

   exit_order_id  exit_idx  exit_price  exit_fees      pnl    return  \
0              3         3    3.060000    0.03366  2.10822  1.739455
1              5         5    5.940000    0.05940  0.78010  0.154475
2             -1         5    6.000000    0.00000 -0.11940 -0.020101
3              3         3    3.948182    0.04343  1.98422  0.308348
4              5         5    1.010000    0.01010  0.94010  0.474798
5             -1         5    1.000000    0.00000 -0.02010 -0.019901

   direction  status  parent_id
0          0       1          0
1          0       1          1
2          1       0          2
3          1       1          0
4          1       1          1
5          0       0          2

get_short_view_orders_nb function

get_short_view_orders_nb(
    order_records,
    close,
    col_map,
    init_position=0.0,
    init_price=nan,
    sim_start=None,
    sim_end=None
)

Get view of orders in short positions only.

mae_nb function

mae_nb(
    size,
    direction,
    entry_price,
    worst_price,
    use_returns=False
)

Apply trade_mae_nb on each trade.

mfe_nb function

mfe_nb(
    size,
    direction,
    entry_price,
    best_price,
    use_returns=False
)

Apply trade_mfe_nb on each trade.

price_status_nb function

price_status_nb(
    records,
    high,
    low
)

Return the status of the order's price related to high and low.

See OrderPriceStatus.

profit_factor_reduce_nb function

profit_factor_reduce_nb(
    pnl_arr
)

Profit factor of a PnL array.

records_within_sim_range_nb function

records_within_sim_range_nb(
    target_shape,
    records,
    col_arr,
    idx_arr,
    sim_start=None,
    sim_end=None
)

Return records within simulation range.

running_edge_ratio_nb function

running_edge_ratio_nb(
    records,
    col_map,
    open,
    high,
    low,
    close,
    volatility,
    entry_price_open=False,
    exit_price_close=False,
    max_duration=None,
    incl_shorter=False
)

Get running edge ratio of each column.

sqn_reduce_nb function

sqn_reduce_nb(
    pnl_arr,
    ddof=0
)

SQN of a PnL array.

trade_best_worst_price_nb function

trade_best_worst_price_nb(
    trade,
    open,
    high,
    low,
    close,
    entry_price_open=False,
    exit_price_close=False,
    max_duration=None,
    idx_relative=True,
    cont_idx=-1,
    one_iteration=False,
    vmin=nan,
    vmax=nan,
    imin=-1,
    imax=-1
)

Best price, worst price, and their indices during a trade.

trade_losing_streak_nb function

trade_losing_streak_nb(
    records
)

Return the current losing streak of each trade.

trade_mae_nb function

trade_mae_nb(
    size,
    direction,
    entry_price,
    worst_price,
    use_returns=False
)

Compute Maximum Adverse Excursion (MAE).

trade_mfe_nb function

trade_mfe_nb(
    size,
    direction,
    entry_price,
    best_price,
    use_returns=False
)

Compute Maximum Favorable Excursion (MFE).

trade_winning_streak_nb function

trade_winning_streak_nb(
    records
)

Return the current winning streak of each trade.

weighted_price_reduce_meta_nb function

weighted_price_reduce_meta_nb(
    idxs,
    col,
    size_arr,
    price_arr
)

Size-weighted price average.

win_rate_reduce_nb function

win_rate_reduce_nb(
    pnl_arr
)

Win rate of a PnL array.

worst_price_idx_nb function

worst_price_idx_nb(
    records,
    open,
    high,
    low,
    close,
    entry_price_open=False,
    exit_price_close=False,
    max_duration=None,
    relative=True
)

Get worst price by applying trade_best_worst_price_nb on each trade.

worst_price_nb function

worst_price_nb(
    records,
    open,
    high,
    low,
    close,
    entry_price_open=False,
    exit_price_close=False,
    max_duration=None
)

Get worst price by applying trade_best_worst_price_nb on each trade.