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David Brazda 0bda14409d new version vbt (#211 )

2024-06-20 22:15:05 +02:00

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Multi timeframe momentum¶

Cílen je nalézt kombinaci trendu, kdy je velmi pravdě+podobné, že trend bude o určitou hodnootu ještě pokračovat.

jsou počítány linregression úhly pro více timeframů a délku oken

Pro každou kombinaci je daný parametr nad kterým musí být. Pokud je nad všemi pak je entry (short/long).

Zvážit i nějaký kumulativní počítadlo anglů - něco jako trend kummulátor.

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 BATCH
# res, df = load_batch(batch_id="f1ac6651", #138170bc 0fb5043a  bde6d0be f1ac6651
#                      space_resolution_evenly=False,
#                      indicators_columns=["Rsi14"],
#                      main_session_only=True,
#                      verbose = False)
# if res < 0:
#     print("Error" + str(res) + str(df))
# df = df["bars"]

# basic_data = vbt.Data.from_data(vbt.symbol_dict({"BAC": df}), tz_convert=zoneNY)
# #m1_data = basic_data[['Open', 'High', 'Low', 'Close', 'Volume']]
# basic_data = basic_data.transform(lambda df: df.between_time('09:30', '16:00'))
# #basic_data.info()

#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 10:30":"2024-02-14 12:00"]

#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

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 [26]:

Out[26]:

frozen_cfg({'silence_warnings': False})

In [3]:

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

Out[3]:

In [23]:

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

In [10]:

t1data.data["BAC"].buyvolume

Out[10]:

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

In [12]:

t1data.data["BAC"].sellvolume

Out[12]:

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

In [24]:

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

In [19]:

order_imbalance

Out[19]:

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

In [49]:

#priminds list (cena), secinds list (napr. rsi), close, voluminds (volume based)
def plot_2y_close(priminds, secinds, close, volume):
    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):
            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))
    
    for indvolume in volume:
        # 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([], [cof,oibratio], t1data.close, [t1data.data["BAC"].buyvolume, t1data.data["BAC"].sellvolume, t1data.volume])

Out[49]:

FigureWidget({
    'data': [{'line': {'color': 'blue'},
              'name': 'close',
              'showlegend': True,
              'type': 'scatter',
              'uid': '2739f9c4-6a09-4b94-8c8d-d4f4acfdc248',
              '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([33.385, 33.39 , 33.39 , ..., 33.025, 33.016, 32.98 ]),
              'yaxis': 'y'},
             {'name': 'None',
              'showlegend': True,
              'type': 'scatter',
              'uid': '631932f0-7128-4768-9c43-db8ef6a72ea7',
              '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([-5368., -1457.,   762., ..., -4431.,   773.,   411.]),
              'yaxis': 'y2'},
             {'name': 'None',
              'showlegend': True,
              'type': 'scatter',
              'uid': '380226e9-d10f-4c7e-ba3f-fc93a97f44ee',
              '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([0.42117749, 0.7756045 , 1.13110805, ..., 0.30678974, 1.36342266,
                          1.03594228]),
              'yaxis': 'y2'},
             {'name': 'buyvolume',
              'showlegend': True,
              'type': 'bar',
              'uid': '2dcd3ba2-1485-4ec9-8df2-98a878de2abc',
              '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([ 3906.,  5036.,  6574., ...,  1961.,  2900., 11846.]),
              'yaxis': 'y3'},
             {'name': 'sellvolume',
              'showlegend': True,
              'type': 'bar',
              'uid': '0c088e56-556e-4803-aa78-1194cbf2e31d',
              '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([ 9274.,  6493.,  5812., ...,  6392.,  2127., 11435.]),
              'yaxis': 'y3'},
             {'name': 'volume',
              'showlegend': True,
              'type': 'bar',
              'uid': '8eb849fe-6629-4a37-a46c-95311cb245b6',
              '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'}],
    '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''