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David Brazda 702328a242 research added (#207 )

2024-06-13 11:02:41 +02:00

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TIME based entries, exits¶

Recurring time bases entries and exits

In [1]:

from v2realbot.tools.loadbatch import load_batch
from v2realbot.utils.utils import zoneNY
import pandas as pd
import numpy as np
import vectorbtpro as vbt
from itables import init_notebook_mode, show
import datetime
from itertools import product
from v2realbot.config import ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY, DATA_DIR

init_notebook_mode(all_interactive=True)

vbt.settings.set_theme("dark")
vbt.settings['plotting']['layout']['width'] = 1280
vbt.settings.plotting.auto_rangebreaks = True
# Set the option to display with pagination
pd.set_option('display.notebook_repr_html', True)
pd.set_option('display.max_rows', 10)  # Number of rows per page

# Define the market open and close times
market_open = datetime.time(9, 30)
market_close = datetime.time(16, 0)
entry_window_opens = 1
entry_window_closes = 370

forced_exit_start = 380
forced_exit_end = 390

#LOAD FROM PARQUET
#list all files is dir directory with parquet extension
dir = DATA_DIR + "/notebooks/"
import os
files = [f for f in os.listdir(dir) if f.endswith(".parquet")]
print('\n'.join(map(str, files)))
file_name = "ohlcv_df-BAC-2023-01-01T09_30_00-2024-05-25T15_30_00-47BCFOPUVWZ-100.parquet"
ohlcv_df = pd.read_parquet(dir+file_name,engine='pyarrow')
#filter ohlcv_df to certain date range (assuming datetime index)
ohlcv_df = ohlcv_df.loc["2024-02-12 9:30":"2024-02-14 16:00"]

#add vwap column to ohlcv_df
#ohlcv_df["hlcc4"] = (ohlcv_df["close"] + ohlcv_df["high"] + ohlcv_df["low"] + ohlcv_df["close"]) / 4

basic_data = vbt.Data.from_data(vbt.symbol_dict({"BAC": ohlcv_df}), tz_convert=zoneNY)
ohlcv_df= None

Loaded env variables from file /Users/davidbrazda/Documents/Development/python/.env

Activating profile profile1

Realtime Websocket connection will use FEED: sip and credential of ACCOUNT1

This is the init_notebook_mode cell from ITables v2.0.1
(you should not see this message - is your notebook trusted?)

trades_df-BAC-2024-01-01T09_30_00-2024-05-14T16_00_00-CO4B7VPWUZF-100.parquet
trades_df-BAC-2024-01-11T09:30:00-2024-01-12T16:00:00.parquet
trades_df-SPY-2024-01-01T09:30:00-2024-05-14T16:00:00.parquet
trades_df-BAC-2023-01-01T09_30_00-2024-05-25T16_00_00-47BCFOPUVWZ-100.parquet
ohlcv_df-BAC-2024-01-11T09:30:00-2024-01-12T16:00:00.parquet
trades_df-BAC-2024-05-15T09_30_00-2024-05-25T16_00_00-47BCFOPUVWZ-100.parquet
ohlcv_df-BAC-2024-01-01T09_30_00-2024-05-25T16_00_00-47BCFOPUVWZ-100.parquet
ohlcv_df-SPY-2024-01-01T09:30:00-2024-05-14T16:00:00.parquet
ohlcv_df-BAC-2024-01-01T09_30_00-2024-05-14T16_00_00-CO4B7VPWUZF-100.parquet
ohlcv_df-BAC-2023-01-01T09_30_00-2024-05-25T16_00_00-47BCFOPUVWZ-100.parquet
ohlcv_df-BAC-2023-01-01T09_30_00-2024-05-25T15_30_00-47BCFOPUVWZ-100.parquet

Add resample function to custom columns¶

In [2]:

from vectorbtpro.utils.config import merge_dicts, Config, HybridConfig
from vectorbtpro import _typing as tp
from vectorbtpro.generic import nb as generic_nb

_feature_config: tp.ClassVar[Config] = HybridConfig(
    {
        "buyvolume": dict(
            resample_func=lambda self, obj, resampler: obj.vbt.resample_apply(
                resampler,
                generic_nb.sum_reduce_nb,
            )
        ),
        "sellvolume": dict(
            resample_func=lambda self, obj, resampler: obj.vbt.resample_apply(
                resampler,
                generic_nb.sum_reduce_nb,
            )
        )
    }
)

basic_data._feature_config = _feature_config

In [3]:

#asic_data.stats()
basic_data.wrapper.index.normalize().nunique()

Out[3]:

In [5]:

basic_data.ohlcv.plot()

Out[5]:

FigureWidget({
    'data': [{'close': array([33.   , 33.01 , 33.015, ..., 33.145, 33.145, 33.13 ]),
              'decreasing': {'fillcolor': '#ee534f', 'line': {'color': '#ee534f'}},
              'high': array([33.   , 33.05 , 33.02 , ..., 33.145, 33.145, 33.13 ]),
              'increasing': {'fillcolor': '#26a69a', 'line': {'color': '#26a69a'}},
              'low': array([33.  , 33.  , 33.  , ..., 33.14, 33.14, 33.13]),
              'name': 'OHLC',
              'opacity': 0.75,
              'open': array([33.  , 33.02, 33.  , ..., 33.14, 33.14, 33.13]),
              'type': 'candlestick',
              'uid': '8850593d-755a-4a3e-a805-75d2e0ce2713',
              'x': array([datetime.datetime(2024, 2, 12, 9, 30, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 9, 30, 1, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 9, 30, 2, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          ...,
                          datetime.datetime(2024, 2, 14, 15, 59, 57, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 14, 15, 59, 58, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 14, 15, 59, 59, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>)],
                         dtype=object),
              'xaxis': 'x',
              'yaxis': 'y'},
             {'marker': {'color': array(['#7f7f7f', '#ee534f', '#26a69a', ..., '#26a69a', '#26a69a', '#7f7f7f'],
                                        dtype=object),
                         'line': {'width': 0}},
              'name': 'Volume',
              'opacity': 0.5,
              'type': 'bar',
              'uid': '9131088d-2dcc-48dd-bd78-1f01b796caa4',
              'x': array([datetime.datetime(2024, 2, 12, 9, 30, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 9, 30, 1, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 9, 30, 2, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          ...,
                          datetime.datetime(2024, 2, 14, 15, 59, 57, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 14, 15, 59, 58, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 14, 15, 59, 59, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>)],
                         dtype=object),
              'xaxis': 'x2',
              'y': array([   800., 267365.,   1100., ...,   1071.,   8707.,  19373.]),
              'yaxis': 'y2'}],
    'layout': {'height': 350,
               'legend': {'orientation': 'h',
                          'traceorder': 'normal',
                          'x': 1,
                          'xanchor': 'right',
                          'y': 1.02,
                          'yanchor': 'bottom'},
               'margin': {'b': 30, 'l': 30, 'r': 30, 't': 30},
               'showlegend': True,
               'template': '...',
               'width': 1280,
               'xaxis': {'anchor': 'y',
                         'domain': [0.0, 1.0],
                         'matches': 'x2',
                         'rangeslider': {'visible': False},
                         'showgrid': True,
                         'showticklabels': False},
               'xaxis2': {'anchor': 'y2', 'domain': [0.0, 1.0], 'showgrid': True},
               'yaxis': {'anchor': 'x', 'domain': [0.3, 1.0], 'showgrid': True},
               'yaxis2': {'anchor': 'x2', 'domain': [0.0, 0.3], 'showgrid': True}}
})

In [6]:

t1data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap','buyvolume','sellvolume']].resample("1T")

In [31]:

t1data = t1data.xloc["2024-02-12 9:30":"2024-02-12 10:20"]
#t1data = t1data.transform(lambda df: df.between_time('09:30', '10:00').dropna())

In [25]:

t1data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap','buyvolume','sellvolume']]

Out[25]:

	close
time
Loading ITables v2.0.1 from the `init_notebook_mode` cell... (need help?)

In [42]:

buyvolume = t1data.data["BAC"].buyvolume
sellvolume = t1data.data["BAC"].sellvolume
totalvolume = buyvolume + sellvolume

#adjust to minimal value to avoid division by zero
sellvolume_adjusted = sellvolume.replace(0, 1e-10)
oibratio = buyvolume / sellvolume

#cumulative order flow (net difference)
cof = buyvolume - sellvolume

# Calculate the order imbalance (net differene) normalize the order imbalance by calculating the difference between buy and sell volumes and then scaling it by the total volume.
order_imbalance = cof / totalvolume
order_imbalance.fillna(0) #nan nahradime 0

order_imbalance_allvolume = cof / t1data.data["BAC"].volume

In [50]:

cof

Out[50]:

	0
time
Loading ITables v2.0.1 from the `init_notebook_mode` cell... (need help?)

In [ ]:

In [33]:

order_imbalance.vbt.plot()

Out[33]:

FigureWidget({
    'data': [{'showlegend': False,
              'type': 'scatter',
              'uid': '2242e1ee-ab70-413f-bd43-af59f5e8ba40',
              'x': array([datetime.datetime(2024, 2, 12, 9, 30, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 9, 30, 1, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 9, 30, 2, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          ...,
                          datetime.datetime(2024, 2, 12, 10, 19, 49, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 10, 19, 51, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 10, 19, 52, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>)],
                         dtype=object),
              'y': array([        nan,  0.99250276, -0.09090909, ...,         nan, -1.        ,
                           1.        ])}],
    'layout': {'height': 350,
               'legend': {'orientation': 'h',
                          'traceorder': 'normal',
                          'x': 1,
                          'xanchor': 'right',
                          'y': 1.02,
                          'yanchor': 'bottom'},
               'margin': {'b': 30, 'l': 30, 'r': 30, 't': 30},
               'template': '...',
               'width': 1280}
})

In [22]:

order_imbalance

Out[22]:

	0
time
Loading ITables v2.0.1 from the `init_notebook_mode` cell... (need help?)

In [34]:

#priminds list (same Y as price), secinds list (secondary Y napr. rsi), close, voluminds (volume based) list
def plot_2y_close(priminds, secinds, close, volumeinds):
    fig = vbt.make_subplots(rows=2, cols=1, shared_xaxes=True, 
                            specs=[[{"secondary_y": True}], [{"secondary_y": False}]], 
                            vertical_spacing=0.02, subplot_titles=("Price and Indicators", "Volume"))

    # Plotting the close price
    close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False,row=1, col=1), trace_kwargs=dict(line=dict(color="blue")))
    
    # Plotting primary indicators on the first row
    for ind in priminds:
        if isinstance(ind, pd.Series):
            #if series has no name, make the name same as the variable name
            
            ind = ind.vbt
        ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False, row=1, col=1))
    
    # Plotting secondary indicators on the first row
    for ind in secinds:
        #ind = ind.rename(str(ind.name))
        if isinstance(ind, pd.Series):
            ind = ind.vbt
        ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True, row=1, col=1), trace_kwargs=dict(line=dict(color="rgba(255, 0, 0, 0.4)")))
    
    for indvolume in volumeinds:
        # Plotting the volume on the second row
        indvolume.rename(str(indvolume.name)).vbt.barplot(fig=fig, add_trace_kwargs=dict(secondary_y=False, row=2, col=1))
        #vbt.Bar(indvolume, fig=fig, add_trace_kwargs=dict(secondary_y=False, row=2, col=1))
    
    return fig

plot_2y_close([], [order_imbalance.rename("order_imbalance_norm")], t1data.close, [t1data.data["BAC"].buyvolume, t1data.data["BAC"].sellvolume, t1data.volume])

Out[34]:

FigureWidget({
    'data': [{'line': {'color': 'blue'},
              'name': 'close',
              'showlegend': True,
              'type': 'scatter',
              'uid': 'c93f46da-c2c8-4fab-8e2a-64a071f207c0',
              'x': array([datetime.datetime(2024, 2, 12, 9, 30, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 9, 30, 1, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 9, 30, 2, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          ...,
                          datetime.datetime(2024, 2, 12, 10, 19, 49, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 10, 19, 51, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 10, 19, 52, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>)],
                         dtype=object),
              'xaxis': 'x',
              'y': array([33.    , 33.01  , 33.015 , ..., 33.365 , 33.3601, 33.37  ]),
              'yaxis': 'y'},
             {'line': {'color': 'rgba(255, 0, 0, 0.4)'},
              'name': 'order_imbalance_norm',
              'showlegend': True,
              'type': 'scatter',
              'uid': '9e8ebec3-4752-4314-b482-aa55b8474584',
              'x': array([datetime.datetime(2024, 2, 12, 9, 30, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 9, 30, 1, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 9, 30, 2, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          ...,
                          datetime.datetime(2024, 2, 12, 10, 19, 49, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 10, 19, 51, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 10, 19, 52, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>)],
                         dtype=object),
              'xaxis': 'x',
              'y': array([        nan,  0.99250276, -0.09090909, ...,         nan, -1.        ,
                           1.        ]),
              'yaxis': 'y2'},
             {'name': 'buyvolume',
              'showlegend': True,
              'type': 'bar',
              'uid': 'afdd4075-e165-4ed0-83d5-1007ad97c881',
              'x': array([datetime.datetime(2024, 2, 12, 9, 30, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 9, 30, 1, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 9, 30, 2, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          ...,
                          datetime.datetime(2024, 2, 12, 10, 19, 49, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 10, 19, 51, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 10, 19, 52, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>)],
                         dtype=object),
              'xaxis': 'x2',
              'y': array([0.00000e+00, 2.65765e+05, 5.00000e+02, ..., 0.00000e+00, 0.00000e+00,
                          1.00000e+02]),
              'yaxis': 'y3'},
             {'name': 'sellvolume',
              'showlegend': True,
              'type': 'bar',
              'uid': '63f1ab97-13c5-4ef9-b98b-36033e4bd745',
              'x': array([datetime.datetime(2024, 2, 12, 9, 30, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 9, 30, 1, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 9, 30, 2, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          ...,
                          datetime.datetime(2024, 2, 12, 10, 19, 49, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 10, 19, 51, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 10, 19, 52, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>)],
                         dtype=object),
              'xaxis': 'x2',
              'y': array([   0., 1000.,  600., ...,    0., 5125.,    0.]),
              'yaxis': 'y3'},
             {'name': 'volume',
              'showlegend': True,
              'type': 'bar',
              'uid': 'd17028ed-2781-4495-9821-dac8d386f85b',
              'x': array([datetime.datetime(2024, 2, 12, 9, 30, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 9, 30, 1, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 9, 30, 2, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          ...,
                          datetime.datetime(2024, 2, 12, 10, 19, 49, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 10, 19, 51, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 10, 19, 52, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>)],
                         dtype=object),
              'xaxis': 'x2',
              'y': array([8.00000e+02, 2.67365e+05, 1.10000e+03, ..., 2.78000e+02, 5.12500e+03,
                          1.00000e+02]),
              'yaxis': 'y3'}],
    'layout': {'annotations': [{'font': {'size': 16},
                                'showarrow': False,
                                'text': 'Price and Indicators',
                                'x': 0.47,
                                'xanchor': 'center',
                                'xref': 'paper',
                                'y': 1.0,
                                'yanchor': 'bottom',
                                'yref': 'paper'},
                               {'font': {'size': 16},
                                'showarrow': False,
                                'text': 'Volume',
                                'x': 0.47,
                                'xanchor': 'center',
                                'xref': 'paper',
                                'y': 0.49,
                                'yanchor': 'bottom',
                                'yref': 'paper'}],
               'height': 350,
               'legend': {'orientation': 'h',
                          'traceorder': 'normal',
                          'x': 1,
                          'xanchor': 'right',
                          'y': 1.02,
                          'yanchor': 'bottom'},
               'margin': {'b': 30, 'l': 30, 'r': 30, 't': 30},
               'template': '...',
               'width': 1280,
               'xaxis': {'anchor': 'y', 'domain': [0.0, 0.94], 'matches': 'x2', 'showticklabels': False},
               'xaxis2': {'anchor': 'y3', 'domain': [0.0, 0.94]},
               'yaxis': {'anchor': 'x', 'domain': [0.51, 1.0]},
               'yaxis2': {'anchor': 'x', 'overlaying': 'y', 'side': 'right'},
               'yaxis3': {'anchor': 'x2', 'domain': [0.0, 0.49]}}
})

In [38]:

%matplotlib inline
t0data = basic_data
t1data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap']].resample("1T")
t2data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap']].resample("15T")
t3data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap']].resample("30T")
t4data = basic_data[['open', 'high', 'low', 'close', 'volume', 'vwap']].resample("D").dropna()

t1data = t1data.transform(lambda df: df.between_time('09:30', '16:00').dropna())
t2data = t2data.transform(lambda df: df.between_time('09:30', '16:00').dropna())
t3data = t3data.transform(lambda df: df.between_time('09:30', '16:00').dropna())

#30min data to daily
# t4data = t3data.resample("D").dropna()

#t4data = t4data.transform(lambda df: df.between_time('09:30', '16:00').dropna())
#m1data.data["SPY"].info()

#m1data.data["SPY"].vbt.ohlcv.plot()
#h2data.data["SPY"].vbt.ohlcv.plot()
#ddata.data["SPY"]
t2data.data["BAC"].vbt.ohlcv.plot().show()


#t4data.data["BAC"]

In [21]:

t2data.close

#in df remove rows with nan

Out[21]:

	close
time
Loading ITables v2.0.1 from the `init_notebook_mode` cell... (need help?)

In [25]:

#realign na 1T = t1data + oriznout main session
t2data_vwap = t2data.vwap.vbt.realign_closing("1T").between_time('09:30', '16:00').dropna()
t3data_vwap = t3data.vwap.vbt.realign_closing("1T").between_time('09:30', '16:00').dropna()
t4data_vwap = t4data.vwap.vbt.realign_closing("1T").dropna()

In [29]:

t2data_vwap

Out[29]:

	vwap
time
Loading ITables v2.0.1 from the `init_notebook_mode` cell... (need help?)

In [39]:

def plot_2y_close(priminds, secinds, close):
    fig = vbt.make_subplots(rows=1, cols=1, shared_xaxes=True, specs=[[{"secondary_y": True}]], vertical_spacing=0.02, subplot_titles=("MOM", "Price" ))
    close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False), trace_kwargs=dict(line=dict(color="blue")))
    for ind in priminds:
        if isinstance(ind, pd.Series):
            ind = ind.vbt
        ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False))
    for ind in secinds:
        if isinstance(ind, pd.Series):
            ind = ind.vbt
        ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True))
    return fig

In [43]:

t4data.clos.vbt

Out[43]:

	close
time
Loading ITables v2.0.1 from the `init_notebook_mode` cell... (need help?)

In [ ]:

obvind = vbt.indicator.obv.run(t1data.close, t1data.volume)

In [48]:

t1_lengtgh = 15
t2_length = 15
t3_length = 15
t4_length = 5
t1_th = 0.1
t2_th = 0.1
t3_th = 0.1
t4_th = 0.1



#minute
t1slope = vbt.indicator("talib:LINEARREG_SLOPE ").run(t1data.close, timeperiod=t1_lengtgh) # -0.09, 0.09
t2slope = vbt.indicator("talib:LINEARREG_SLOPE ").run(t2data.vwap, timeperiod=t2_length) #   -0.08 , 0.079
t3slope = vbt.indicator("talib:LINEARREG_SLOPE ").run(t3data.vwap, timeperiod=t3_length) #   -0.08, 0.08
#daily
t4slope = vbt.indicator("talib:LINEARREG_SLOPE ").run(t4data.vwap, timeperiod=t4_length) #   -0.1, 0.09

plot_2y_close(priminds=[], secinds=[t1slope, t2slope, t3slope, t4slope], close=t1data.close).show()

In [49]:

#thirtymin_slope = thirtymin_slope.real.rename("30min") #timto se prejmenuje real na 30min
t3slope = t3slope.real.vbt.realign_closing("1T").between_time('09:30', '16:00').dropna()
##filter daily_slope_to_compare to only monday to friday
t3slope = t3slope[t3slope.index.dayofweek < 5]

#t3slope.info()

t2slope = t2slope.real.vbt.realign_closing("1T").between_time('09:30', '16:00').dropna()
##filter daily_slope_to_compare to only monday to friday
t2slope = t2slope[t2slope.index.dayofweek < 5]

t2slope.info()

<class 'pandas.core.series.Series'>
DatetimeIndex: 8469 entries, 2024-02-12 14:14:00-05:00 to 2024-03-13 12:00:00-04:00
Series name: 15
Non-Null Count  Dtype  
--------------  -----  
8469 non-null   float64
dtypes: float64(1)
memory usage: 132.3 KB

In [15]:

oibratio

Out[15]:

	0
time
Loading ITables v2.0.1 from the `init_notebook_mode` cell... (need help?)

In [32]:

#
short_entries = order_imbalance.vbt < 0.0002
#short_entries = oibratio.vbt < 0.01
short_entries.value_counts()

entries = order_imbalance.vbt > 0.7
#entries = oibratio.vbt > 10
entries.value_counts()

Out[32]:

	count
Loading ITables v2.0.1 from the `init_notebook_mode` cell... (need help?)

In [33]:

fig = vbt.make_subplots(rows=3, cols=1, shared_xaxes=True, 
                        specs=[[{"secondary_y": True}], [{"secondary_y": True}], [{"secondary_y": False}]], 
                        vertical_spacing=0.02, subplot_titles=("Price and Indicators", "Volume"))
t1data.data["BAC"].vbt.ohlcv.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False, row=1, col=1))
#oibratio.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True, row=1, col=1))
order_imbalance.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True, row=1, col=1))
entries.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name="LONGS",
                                                                                    line=dict(color="#ffe476"),
                                                                                    marker=dict(color="limegreen"),
                                                                                    fill=None,
                                                                                    connectgaps=True,
                                                                                    ), add_trace_kwargs=dict(secondary_y=False, row=1, col=1))

short_entries.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name="SHORTS",
                                                                                    line=dict(color="#ffe476"),
                                                                                    marker=dict(color="red"),
                                                                                    fill=None,
                                                                                    connectgaps=True,
                                                                                    ), add_trace_kwargs=dict(secondary_y=False, row=1, col=1))

Out[33]:

FigureWidget({
    'data': [{'close': array([33.385, 33.39 , 33.39 , ..., 33.025, 33.016, 32.98 ]),
              'decreasing': {'fillcolor': '#ee534f', 'line': {'color': '#ee534f'}},
              'high': array([33.3961, 33.4   , 33.4   , ..., 33.04  , 33.03  , 33.025 ]),
              'increasing': {'fillcolor': '#26a69a', 'line': {'color': '#26a69a'}},
              'low': array([33.36 , 33.38 , 33.385, ..., 33.015, 33.01 , 32.98 ]),
              'name': 'OHLC',
              'opacity': 0.75,
              'open': array([33.36 , 33.385, 33.39 , ..., 33.015, 33.02 , 33.015]),
              'type': 'candlestick',
              'uid': 'c74adf87-a9bf-4900-a1ab-21527974806d',
              'x': array([datetime.datetime(2024, 2, 12, 10, 30, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 10, 31, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 10, 32, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          ...,
                          datetime.datetime(2024, 2, 14, 11, 58, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 14, 11, 59, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 14, 12, 0, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>)],
                         dtype=object),
              'xaxis': 'x',
              'yaxis': 'y'},
             {'marker': {'color': array(['#26a69a', '#26a69a', '#7f7f7f', ..., '#26a69a', '#ee534f', '#ee534f'],
                                        dtype=object),
                         'line': {'width': 0}},
              'name': 'Volume',
              'opacity': 0.5,
              'type': 'bar',
              'uid': '6d33d912-0ba2-464c-b000-faa09950d214',
              'x': array([datetime.datetime(2024, 2, 12, 10, 30, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 10, 31, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 10, 32, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          ...,
                          datetime.datetime(2024, 2, 14, 11, 58, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 14, 11, 59, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 14, 12, 0, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>)],
                         dtype=object),
              'xaxis': 'x2',
              'y': array([26275., 29950., 65481., ..., 14018.,  7571., 67175.]),
              'yaxis': 'y3'},
             {'showlegend': False,
              'type': 'scatter',
              'uid': '4b8d368d-c48e-4941-a2da-57a85fc012f1',
              'x': array([datetime.datetime(2024, 2, 12, 10, 30, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 10, 31, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 10, 32, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          ...,
                          datetime.datetime(2024, 2, 14, 11, 58, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 14, 11, 59, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 14, 12, 0, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>)],
                         dtype=object),
              'xaxis': 'x',
              'y': array([-0.40728376, -0.12637696,  0.06152107, ..., -0.5304681 ,  0.15376964,
                           0.01765388]),
              'yaxis': 'y2'},
             {'connectgaps': True,
              'line': {'color': '#ffe476'},
              'marker': {'color': 'limegreen',
                         'line': {'color': 'rgb(34,143,34)', 'width': 1},
                         'size': 8,
                         'symbol': 'triangle-up'},
              'mode': 'markers',
              'name': 'LONGS',
              'showlegend': True,
              'type': 'scatter',
              'uid': 'f0a2bc78-967f-41f0-8a62-8d194b207dba',
              'x': array([datetime.datetime(2024, 2, 12, 10, 42, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 10, 53, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 13, 5, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 13, 12, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 13, 36, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 14, 29, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 14, 35, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 13, 9, 30, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 13, 12, 56, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 13, 14, 36, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 14, 10, 11, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 14, 10, 17, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 14, 11, 22, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 14, 11, 24, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 14, 11, 25, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>)],
                         dtype=object),
              'xaxis': 'x',
              'y': array([33.42  , 33.485 , 33.755 , 33.785 , 33.8401, 33.825 , 33.795 , 33.1999,
                          32.815 , 32.5793, 33.04  , 33.055 , 33.035 , 33.075 , 33.105 ]),
              'yaxis': 'y'},
             {'connectgaps': True,
              'line': {'color': '#ffe476'},
              'marker': {'color': 'red',
                         'line': {'color': 'rgb(178,0,0)', 'width': 1},
                         'size': 8,
                         'symbol': 'triangle-up'},
              'mode': 'markers',
              'name': 'SHORTS',
              'showlegend': True,
              'type': 'scatter',
              'uid': '0912f3bf-c65e-484a-b3ea-1b14c6f184de',
              'x': array([datetime.datetime(2024, 2, 12, 10, 30, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 10, 31, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 12, 10, 33, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          ...,
                          datetime.datetime(2024, 2, 14, 11, 56, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 14, 11, 57, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 2, 14, 11, 58, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>)],
                         dtype=object),
              'xaxis': 'x',
              'y': array([33.385, 33.39 , 33.36 , ..., 33.046, 33.015, 33.025]),
              'yaxis': 'y'}],
    'layout': {'annotations': [{'font': {'size': 16},
                                'showarrow': False,
                                'text': 'Price and Indicators',
                                'x': 0.47,
                                'xanchor': 'center',
                                'xref': 'paper',
                                'y': 1.0,
                                'yanchor': 'bottom',
                                'yref': 'paper'},
                               {'font': {'size': 16},
                                'showarrow': False,
                                'text': 'Volume',
                                'x': 0.47,
                                'xanchor': 'center',
                                'xref': 'paper',
                                'y': 0.66,
                                'yanchor': 'bottom',
                                'yref': 'paper'}],
               'height': 350,
               'legend': {'orientation': 'h',
                          'traceorder': 'normal',
                          'x': 1,
                          'xanchor': 'right',
                          'y': 1.02,
                          'yanchor': 'bottom'},
               'margin': {'b': 30, 'l': 30, 'r': 30, 't': 30},
               'template': '...',
               'width': 1280,
               'xaxis': {'anchor': 'y',
                         'domain': [0.0, 0.94],
                         'matches': 'x3',
                         'rangeslider': {'visible': False},
                         'showticklabels': False},
               'xaxis2': {'anchor': 'y3', 'domain': [0.0, 0.94], 'matches': 'x3', 'showticklabels': False},
               'xaxis3': {'anchor': 'y5', 'domain': [0.0, 0.94]},
               'yaxis': {'anchor': 'x', 'domain': [0.68, 1.0]},
               'yaxis2': {'anchor': 'x', 'overlaying': 'y', 'side': 'right'},
               'yaxis3': {'anchor': 'x2', 'domain': [0.34, 0.66]},
               'yaxis4': {'anchor': 'x2', 'overlaying': 'y3', 'side': 'right'},
               'yaxis5': {'anchor': 'x3', 'domain': [0.0, 0.32]}}
})

In [239]:

# thirtymin_slope_to_compare.vbt.xloc["04-16-2024"].get()
thirty_down_signal.vbt.xloc["04-16-2024"].get()

Out[239]:

	20
time
Loading ITables v2.0.1 from the `init_notebook_mode` cell... (need help?)

In [53]:

#short_signal = t1slope.real_below(t1_th) & t2slope.real_below(t2_th) & t3slope.real_below(t3_th) & t4slope.real_below(t4_th)
#long_signal = t1slope.real_above(t1_th) & t2slope.real_above(t2_th) & t3slope.real_above(t3_th) & t4slope.real_above(t4_th)

#test na daily s reversem  crossed 0
short_signal = t2slope.vbt < -0.01 & t3slope.vbt < -0.01  #min value of threshold
long_signal = t2slope.vbt > 0.01 & t3slope.vbt > 0.01  #min

# thirty_up_signal = t3slope.vbt.crossed_above(0.01)
# thirty_down_signal = t3slope.vbt.crossed_below(-0.01)

fig = plot_2y_close(priminds=[], secinds=[t3slope], close=t1data.close)
#short_signal.vbt.signals.plot_as_entries(basic_data.close, fig=fig)

short_signal.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name="SHORTS",
                                                                                    line=dict(color="#ffe476"),
                                                                                    marker=dict(color="red", symbol="triangle-down"),
                                                                                    fill=None,
                                                                                    connectgaps=True,
                                                                                    ))
long_signal.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name="LONGS",
                                                                                    line=dict(color="#ffe476"),
                                                                                    marker=dict(color="limegreen"),
                                                                                    fill=None,
                                                                                    connectgaps=True,
                                                                                    ))

# thirty_down_signal.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name="DOWN30",
#                                                                                     line=dict(color="#ffe476"),
#                                                                                     marker=dict(color="yellow", symbol="triangle-down"),
#                                                                                     fill=None,
#                                                                                     connectgaps=True,
#                                                                                     ))
# thirty_up_signal.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name="UP30",
#                                                                                     line=dict(color="#ffe476"),
#                                                                                     marker=dict(color="grey"),
#                                                                                     fill=None,
#                                                                                     connectgaps=True,
#                                                                                     ))

# thirtymin_slope_to_compare.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True), trace_kwargs=dict(name="30min slope",
#                                                                                         line=dict(color="yellow"),                                                                                     
#                                                                                         fill=None,
#                                                                                         connectgaps=True,
#                                                                                         ))

fig.show()
# print("short signal")
# print(short_signal.value_counts())

#forced_exit = pd.Series(False, index=close.index)
forced_exit = basic_data.symbol_wrapper.fill(False)
#entry_window_open = pd.Series(False, index=close.index)
entry_window_open=  basic_data.symbol_wrapper.fill(False)

# Calculate the time difference in minutes from market open for each timestamp
elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)

entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True

#print(entry_window_open.value_counts())

forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True
short_entries = (short_signal & entry_window_open)
short_exits = forced_exit

entries = (long_signal & entry_window_open)
exits = forced_exit
#long_entries.info()
#number of trues and falses in long_entries
# print(short_exits.value_counts())
# print(short_entries.value_counts())

#fig = plot_2y_close([],[momshort, rocp], close)
#short_signal.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False))
#print(sl_stop)
#short_entries=short_entries, short_exits=short_exits,
# pf = vbt.Portfolio.from_signals(close=basic_data, entries=short_entries, exits=exits, tsl_stop=0.005, tp_stop = 0.05, fees=0.0167/100, freq="1s") #sl_stop=sl_stop, tp_stop = sl_stop,

# pf.stats()

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[53], line 5
      1 #short_signal = t1slope.real_below(t1_th) & t2slope.real_below(t2_th) & t3slope.real_below(t3_th) & t4slope.real_below(t4_th)
      2 #long_signal = t1slope.real_above(t1_th) & t2slope.real_above(t2_th) & t3slope.real_above(t3_th) & t4slope.real_above(t4_th)
      3 
      4 #test na daily s reversem  crossed 0
----> 5 short_signal = t2slope.vbt < -0.01 & t3slope.vbt < -0.01  #min value of threshold
      6 long_signal = t2slope.vbt > 0.01 & t3slope.vbt > 0.01  #min
      8 # thirty_up_signal = t3slope.vbt.crossed_above(0.01)
      9 # thirty_down_signal = t3slope.vbt.crossed_below(-0.01)

File ~/Documents/Development/python/v2trading/.venv/lib/python3.10/site-packages/vectorbtpro/utils/magic_decorators.py:90, in attach_binary_magic_methods.<locals>.wrapper.<locals>.new_method(self, other, _translate_func, _func)
     84 def new_method(
     85     self,
     86     other: tp.Any,
     87     _translate_func: BinaryTranslateFuncT = translate_func,
     88     _func: tp.Callable = func,
     89 ) -> tp.SeriesFrame:
---> 90     return _translate_func(self, other, _func)

File ~/Documents/Development/python/v2trading/.venv/lib/python3.10/site-packages/vectorbtpro/base/accessors.py:417, in BaseAccessor.<lambda>(self, other, np_func)
    411         return get_index_ranges(self.obj, self.any_freq, *args, **kwargs)
    414 BaseAccessorT = tp.TypeVar("BaseAccessorT", bound="BaseAccessor")
--> 417 @attach_binary_magic_methods(lambda self, other, np_func: self.combine(other, combine_func=np_func))
    418 @attach_unary_magic_methods(lambda self, np_func: self.apply(apply_func=np_func))
    419 class BaseAccessor(Wrapping):
    420     """Accessor on top of Series and DataFrames.
    421 
    422     Accessible via `pd.Series.vbt` and `pd.DataFrame.vbt`, and all child accessors.
   (...)
    539         ```
    540     """
    542     @classmethod
    543     def resolve_row_stack_kwargs(
    544         cls: tp.Type[BaseAccessorT],
    545         *objs: tp.MaybeTuple[BaseAccessorT],
    546         **kwargs,
    547     ) -> tp.Kwargs:

File ~/Documents/Development/python/v2trading/.venv/lib/python3.10/site-packages/vectorbtpro/base/accessors.py:1702, in BaseAccessor.combine(cls_or_self, obj, combine_func, allow_multiple, keep_pd, to_2d, concat, keys, broadcast_named_args, broadcast_kwargs, template_context, wrap_kwargs, *args, **kwargs)
   1699     return wrapper.wrap(out, **merge_dicts(dict(columns=new_columns), wrap_kwargs))
   1700 else:
   1701     # Combine arguments pairwise into one object
-> 1702     out = combining.combine_multiple(inputs, combine_func, *args, **kwargs)
   1703     return wrapper.wrap(out, **resolve_dict(wrap_kwargs))

File ~/Documents/Development/python/v2trading/.venv/lib/python3.10/site-packages/vectorbtpro/base/combining.py:314, in combine_multiple(objs, combine_func, jitted_loop, *args, **kwargs)
    312 result = objs[0]
    313 for i in range(1, len(objs)):
--> 314     result = combine_func(result, objs[i], *args, **kwargs)
    315 return result

File ~/Documents/Development/python/v2trading/.venv/lib/python3.10/site-packages/vectorbtpro/utils/magic_decorators.py:41, in <lambda>(x, y)
     10 __all__ = []
     12 __pdoc__ = {}
     14 binary_magic_config = ReadonlyConfig(
     15     {
     16         "__eq__": dict(func=np.equal),
     17         "__ne__": dict(func=np.not_equal),
     18         "__lt__": dict(func=np.less),
     19         "__gt__": dict(func=np.greater),
     20         "__le__": dict(func=np.less_equal),
     21         "__ge__": dict(func=np.greater_equal),
     22         # arithmetic ops
     23         "__add__": dict(func=np.add),
     24         "__sub__": dict(func=np.subtract),
     25         "__mul__": dict(func=np.multiply),
     26         "__pow__": dict(func=np.power),
     27         "__mod__": dict(func=np.mod),
     28         "__floordiv__": dict(func=np.floor_divide),
     29         "__truediv__": dict(func=np.true_divide),
     30         "__radd__": dict(func=lambda x, y: np.add(y, x)),
     31         "__rsub__": dict(func=lambda x, y: np.subtract(y, x)),
     32         "__rmul__": dict(func=lambda x, y: np.multiply(y, x)),
     33         "__rpow__": dict(func=lambda x, y: np.power(y, x)),
     34         "__rmod__": dict(func=lambda x, y: np.mod(y, x)),
     35         "__rfloordiv__": dict(func=lambda x, y: np.floor_divide(y, x)),
     36         "__rtruediv__": dict(func=lambda x, y: np.true_divide(y, x)),
     37         # mask ops
     38         "__and__": dict(func=np.bitwise_and),
     39         "__or__": dict(func=np.bitwise_or),
     40         "__xor__": dict(func=np.bitwise_xor),
---> 41         "__rand__": dict(func=lambda x, y: np.bitwise_and(y, x)),
     42         "__ror__": dict(func=lambda x, y: np.bitwise_or(y, x)),
     43         "__rxor__": dict(func=lambda x, y: np.bitwise_xor(y, x)),
     44     }
     45 )
     46 """_"""
     48 __pdoc__[
     49     "binary_magic_config"
     50 ] = f"""Config of binary magic methods to be attached to a class.
   (...)
     54 ```
     55 """

TypeError: ufunc 'bitwise_and' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''

In [29]:

# pf.plot()

Out[29]:

FigureWidget({
    'data': [{'close': array([477.55  , 477.545 , 477.5088, ..., 476.6   , 476.66  , 476.665 ]),
              'decreasing': {'fillcolor': '#ee534f', 'line': {'color': '#ee534f'}},
              'high': array([477.59, 477.66, 477.62, ..., 476.73, 476.66, 476.68]),
              'increasing': {'fillcolor': '#26a69a', 'line': {'color': '#26a69a'}},
              'low': array([477.53, 477.54, 477.49, ..., 476.6 , 476.59, 476.63]),
              'name': 'OHLC',
              'opacity': 0.5,
              'open': array([477.58, 477.55, 477.61, ..., 476.71, 476.6 , 476.68]),
              'type': 'candlestick',
              'uid': '4806638f-1773-49e9-b51d-bd24ba7b62a8',
              'x': array([datetime.datetime(2024, 1, 11, 9, 30, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 11, 9, 30, 1, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 11, 9, 30, 2, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          ...,
                          datetime.datetime(2024, 1, 12, 15, 59, 57, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 12, 15, 59, 58, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 12, 15, 59, 59, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>)],
                         dtype=object),
              'xaxis': 'x',
              'yaxis': 'y'},
             {'customdata': array([['0', '2024-01-11 10:19:10-05:00', '2024-01-11 10:19:10-05:00',
                                    '2024-01-11 10:19:10-05:00', 0.2106818863148415, 474.57,
                                    0.0166972115656705, 'Market', 'None'],
                                   ['2', '2024-01-11 11:50:07-05:00', '2024-01-11 11:50:07-05:00',
                                    '2024-01-11 11:50:07-05:00', 0.21048335663426113, 473.4803,
                                    0.01664317371498214, 'Market', 'None'],
                                   ['4', '2024-01-12 10:07:40-05:00', '2024-01-12 10:07:40-05:00',
                                    '2024-01-12 10:07:40-05:00', 0.20988622968010992, 477.27,
                                    0.016728790940192084, 'Market', 'None']], dtype=object),
              'hovertemplate': ('<br>Order Id: %{customdata[0]}' ... 'br>Stop Type: %{customdata[8]}'),
              'marker': {'color': '#37B13F',
                         'line': {'color': 'rgb(38,123,44)', 'width': 1},
                         'size': 8,
                         'symbol': 'triangle-up'},
              'mode': 'markers',
              'name': 'Buy',
              'type': 'scatter',
              'uid': 'f210178b-1c2d-40f3-8866-8dc25dbe6131',
              'x': array([datetime.datetime(2024, 1, 11, 10, 19, 10, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 11, 11, 50, 7, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 12, 10, 7, 40, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>)],
                         dtype=object),
              'xaxis': 'x',
              'y': array([474.57  , 473.4803, 477.27  ]),
              'yaxis': 'y'},
             {'customdata': array([['1', '2024-01-11 10:19:10-05:00', '2024-01-11 11:13:18-05:00',
                                    '2024-01-11 11:13:18-05:00', 0.2106818863148415, 473.19214999999997,
                                    0.016648733463479694, 'Market', 'TSL'],
                                   ['3', '2024-01-11 15:50:00-05:00', '2024-01-11 15:50:00-05:00',
                                    '2024-01-11 15:50:00-05:00', 0.21048335663426113, 476.075,
                                    0.01673437928961253, 'Market', 'None'],
                                   ['5', '2024-01-12 15:50:00-05:00', '2024-01-12 15:50:00-05:00',
                                    '2024-01-12 15:50:00-05:00', 0.20988622968010992, 476.77,
                                    0.01671126544000586, 'Market', 'None']], dtype=object),
              'hovertemplate': ('<br>Order Id: %{customdata[0]}' ... 'br>Stop Type: %{customdata[8]}'),
              'marker': {'color': '#EA4335',
                         'line': {'color': 'rgb(181,31,18)', 'width': 1},
                         'size': 8,
                         'symbol': 'triangle-down'},
              'mode': 'markers',
              'name': 'Sell',
              'type': 'scatter',
              'uid': '361777ba-6c03-4530-9ebb-dc7511772e2c',
              'x': array([datetime.datetime(2024, 1, 11, 11, 13, 18, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 11, 15, 50, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 12, 15, 50, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>)],
                         dtype=object),
              'xaxis': 'x',
              'y': array([473.19215, 476.075  , 476.77   ]),
              'yaxis': 'y'},
             {'customdata': array([['1', '1', '2024-01-11 15:50:00-05:00', 0.5127636124543252,
                                    0.005145143873778922]], dtype=object),
              'hovertemplate': ('<br>Exit Trade Id: %{customdat' ... 'br>Return: %{customdata[4]:,%}'),
              'marker': {'color': '#37B13F',
                         'line': {'color': 'rgb(38,123,44)', 'width': 1},
                         'opacity': array([0.9]),
                         'size': array([14.]),
                         'symbol': 'circle'},
              'mode': 'markers',
              'name': 'Closed - Profit',
              'type': 'scatter',
              'uid': 'c448cad7-8dfa-4ecc-8a78-732f9d184a98',
              'x': array([datetime.datetime(2024, 1, 11, 15, 50, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>)],
                         dtype=object),
              'xaxis': 'x2',
              'y': array([0.00514514]),
              'yaxis': 'y2'},
             {'customdata': array([['0', '0', '2024-01-11 11:13:18-05:00', -0.32363398208806465,
                                    -0.0032368802896307336],
                                   ['2', '2', '2024-01-12 15:50:00-05:00', -0.13838317122025232,
                                    -0.001381450080667201]], dtype=object),
              'hovertemplate': ('<br>Exit Trade Id: %{customdat' ... 'br>Return: %{customdata[4]:,%}'),
              'marker': {'color': '#EA4335',
                         'line': {'color': 'rgb(181,31,18)', 'width': 1},
                         'opacity': array([0.82394718, 0.75      ]),
                         'size': array([10.45086826,  7.        ]),
                         'symbol': 'circle'},
              'mode': 'markers',
              'name': 'Closed - Loss',
              'type': 'scatter',
              'uid': '6c1f305e-2e36-4dcd-b570-c5fa17cf32f9',
              'x': array([datetime.datetime(2024, 1, 11, 11, 13, 18, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 12, 15, 50, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>)],
                         dtype=object),
              'xaxis': 'x2',
              'y': array([-0.00323688, -0.00138145]),
              'yaxis': 'y2'},
             {'legendgroup': '0',
              'line': {'color': '#7f7f7f'},
              'mode': 'lines',
              'name': 'Benchmark',
              'showlegend': True,
              'type': 'scatter',
              'uid': 'd66c669c-273c-4e17-8757-592c133065c6',
              'x': array([datetime.datetime(2024, 1, 11, 9, 30, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 11, 9, 30, 1, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 11, 9, 30, 2, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          ...,
                          datetime.datetime(2024, 1, 12, 15, 59, 57, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 12, 15, 59, 58, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 12, 15, 59, 59, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>)],
                         dtype=object),
              'xaxis': 'x3',
              'y': array([1.        , 0.99998953, 0.99991373, ..., 0.99801068, 0.99813632,
                          0.99814679]),
              'yaxis': 'y3'},
             {'hoverinfo': 'skip',
              'line': {'color': 'rgba(0, 0, 0, 0)', 'width': 0},
              'mode': 'lines',
              'opacity': 0,
              'showlegend': False,
              'type': 'scatter',
              'uid': '6b09b5d1-eddb-4801-9331-01a5fe5de1b4',
              'x': array([datetime.datetime(2024, 1, 11, 9, 30, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 11, 9, 30, 1, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 11, 9, 30, 2, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          ...,
                          datetime.datetime(2024, 1, 12, 15, 59, 57, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 12, 15, 59, 58, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 12, 15, 59, 59, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>)],
                         dtype=object),
              'xaxis': 'x3',
              'y': array([1, 1, 1, ..., 1, 1, 1]),
              'yaxis': 'y3'},
             {'connectgaps': False,
              'fill': 'tonexty',
              'fillcolor': 'rgba(0, 128, 0, 0.25)',
              'hoverinfo': 'skip',
              'line': {'color': 'rgba(0, 0, 0, 0)', 'width': 0},
              'mode': 'lines',
              'opacity': 0,
              'showlegend': False,
              'type': 'scatter',
              'uid': '331698c2-cfb6-4ab8-8c3e-c2b834ff3138',
              'x': array([datetime.datetime(2024, 1, 11, 9, 30, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 11, 9, 30, 1, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 11, 9, 30, 2, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          ...,
                          datetime.datetime(2024, 1, 12, 15, 59, 57, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 12, 15, 59, 58, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 12, 15, 59, 59, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>)],
                         dtype=object),
              'xaxis': 'x3',
              'y': array([1.        , 1.        , 1.        , ..., 1.00050746, 1.00050746,
                          1.00050746]),
              'yaxis': 'y3'},
             {'hoverinfo': 'skip',
              'line': {'color': 'rgba(0, 0, 0, 0)', 'width': 0},
              'mode': 'lines',
              'opacity': 0,
              'showlegend': False,
              'type': 'scatter',
              'uid': '06087e40-8f49-45dc-9bc3-3c76c86149d6',
              'x': array([datetime.datetime(2024, 1, 11, 9, 30, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 11, 9, 30, 1, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 11, 9, 30, 2, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          ...,
                          datetime.datetime(2024, 1, 12, 15, 59, 57, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 12, 15, 59, 58, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 12, 15, 59, 59, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>)],
                         dtype=object),
              'xaxis': 'x3',
              'y': array([1, 1, 1, ..., 1, 1, 1]),
              'yaxis': 'y3'},
             {'connectgaps': False,
              'fill': 'tonexty',
              'fillcolor': 'rgba(255, 0, 0, 0.25)',
              'hoverinfo': 'skip',
              'line': {'color': 'rgba(0, 0, 0, 0)', 'width': 0},
              'mode': 'lines',
              'opacity': 0,
              'showlegend': False,
              'type': 'scatter',
              'uid': 'f5e35d67-9fc6-40d7-a90e-f15501073e06',
              'x': array([datetime.datetime(2024, 1, 11, 9, 30, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 11, 9, 30, 1, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 11, 9, 30, 2, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          ...,
                          datetime.datetime(2024, 1, 12, 15, 59, 57, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 12, 15, 59, 58, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 12, 15, 59, 59, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>)],
                         dtype=object),
              'xaxis': 'x3',
              'y': array([1., 1., 1., ..., 1., 1., 1.]),
              'yaxis': 'y3'},
             {'legendgroup': '1',
              'line': {'color': '#9467bd'},
              'mode': 'lines',
              'name': 'Value',
              'showlegend': True,
              'type': 'scatter',
              'uid': 'ece5fae6-ae3b-40e6-9a49-e05e740e055e',
              'x': array([datetime.datetime(2024, 1, 11, 9, 30, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 11, 9, 30, 1, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 11, 9, 30, 2, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          ...,
                          datetime.datetime(2024, 1, 12, 15, 59, 57, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 12, 15, 59, 58, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 12, 15, 59, 59, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>)],
                         dtype=object),
              'xaxis': 'x3',
              'y': array([1.        , 1.        , 1.        , ..., 1.00050746, 1.00050746,
                          1.00050746]),
              'yaxis': 'y3'},
             {'hoverinfo': 'skip',
              'line': {'color': 'rgba(0, 0, 0, 0)', 'width': 0},
              'mode': 'lines',
              'opacity': 0.0,
              'showlegend': False,
              'type': 'scatter',
              'uid': 'ed80a1ce-b11a-4d07-8a75-9c03096db9fa',
              'x': array([datetime.datetime(2024, 1, 11, 9, 30, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 11, 9, 30, 1, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 11, 9, 30, 2, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          ...,
                          datetime.datetime(2024, 1, 12, 15, 59, 57, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 12, 15, 59, 58, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>),
                          datetime.datetime(2024, 1, 12, 15, 59, 59, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>)],
                         dtype=object),
              'xaxis': 'x3',
              'y': array([1, 1, 1, ..., 1, 1, 1]),
              'yaxis': 'y3'}],
    'layout': {'annotations': [{'font': {'size': 16},
                                'showarrow': False,
                                'text': 'Orders',
                                'x': 0.5,
                                'xanchor': 'center',
                                'xref': 'paper',
                                'y': 1.0,
                                'yanchor': 'bottom',
                                'yref': 'paper'},
                               {'font': {'size': 16},
                                'showarrow': False,
                                'text': 'Trade PnL',
                                'x': 0.5,
                                'xanchor': 'center',
                                'xref': 'paper',
                                'y': 0.6527777777777777,
                                'yanchor': 'bottom',
                                'yref': 'paper'},
                               {'font': {'size': 16},
                                'showarrow': False,
                                'text': 'Cumulative Returns',
                                'x': 0.5,
                                'xanchor': 'center',
                                'xref': 'paper',
                                'y': 0.3055555555555555,
                                'yanchor': 'bottom',
                                'yref': 'paper'}],
               'height': 960,
               'legend': {'orientation': 'h',
                          'traceorder': 'normal',
                          'x': 1,
                          'xanchor': 'right',
                          'y': 1.0416666666666667,
                          'yanchor': 'bottom'},
               'margin': {'b': 30, 'l': 30, 'r': 30, 't': 30},
               'shapes': [{'line': {'color': 'gray', 'dash': 'dash'},
                           'type': 'line',
                           'x0': 0.0,
                           'x1': 1.0,
                           'xref': 'paper',
                           'y0': 0,
                           'y1': 0,
                           'yref': 'y2'},
                          {'line': {'color': 'gray', 'dash': 'dash'},
                           'type': 'line',
                           'x0': 0.0,
                           'x1': 1.0,
                           'xref': 'paper',
                           'y0': 1,
                           'y1': 1,
                           'yref': 'y3'}],
               'showlegend': True,
               'template': '...',
               'width': 1330,
               'xaxis': {'anchor': 'y',
                         'domain': [0.0, 1.0],
                         'matches': 'x3',
                         'rangeslider': {'visible': False},
                         'showticklabels': False},
               'xaxis2': {'anchor': 'y2', 'domain': [0.0, 1.0], 'matches': 'x3', 'showticklabels': False},
               'xaxis3': {'anchor': 'y3', 'domain': [0.0, 1.0], 'title': {'text': 'Index'}},
               'yaxis': {'anchor': 'x', 'domain': [0.6944444444444444, 1.0], 'title': {'text': 'Price'}},
               'yaxis2': {'anchor': 'x2',
                          'domain': [0.3472222222222222, 0.6527777777777777],
                          'tickformat': '.2%',
                          'title': {'text': 'Trade PnL'}},
               'yaxis3': {'anchor': 'x3', 'domain': [0.0, 0.3055555555555555], 'title': {'text': 'Cumulative returns'}}}
})

In [36]:

pf.get_drawdowns().records_readable

Out[36]:

Drawdown Id	Column	Start Index	Valley Index	End Index	Start Value	Valley Value	End Value	Status
Loading ITables v2.0.1 from the `init_notebook_mode` cell... (need help?)

In [12]:

pf.orders.records_readable

Out[12]:

Order Id	Column	Signal Index	Creation Index	Fill Index	Size	Price	Fees	Side	Type	Stop Type
Loading ITables v2.0.1 from the `init_notebook_mode` cell... (need help?)

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TIME based entries, exits¶

Add resample function to custom columns¶

3.2 MiB

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