Features
Market data sources
Retrieve market data from specialized providers through a consistent Python interface
✅ Introducing a new class for findatapy, designed to pull data from Bloomberg, Eikon, Quandl, Dukascopy, and other data sources.
Discover tickers on Dukascopy and pull one day of tick data
vbt.FinPyData.list_symbols(data_source="dukascopy")['fx.dukascopy.tick.NYC.AUDCAD',
'fx.dukascopy.tick.NYC.AUDCHF',
'fx.dukascopy.tick.NYC.AUDJPY',
...
'fx.dukascopy.tick.NYC.USDTRY',
'fx.dukascopy.tick.NYC.USDZAR',
'fx.dukascopy.tick.NYC.ZARJPY']data = vbt.FinPyData.pull(
"fx.dukascopy.tick.NYC.EURUSD",
start="14 Jun 2016",
end="15 Jun 2016"
)
data.get() close
Date
2016-06-14 00:00:00.844000+00:00 1.128795
2016-06-14 00:00:01.591000+00:00 1.128790
2016-06-14 00:00:01.743000+00:00 1.128775
2016-06-14 00:00:02.464000+00:00 1.128770
2016-06-14 00:00:02.971000+00:00 1.128760
... ...
2016-06-14 23:59:57.733000+00:00 1.121020
2016-06-14 23:59:58.239000+00:00 1.121030
2016-06-14 23:59:58.953000+00:00 1.121035
2016-06-14 23:59:59.004000+00:00 1.121050
2016-06-14 23:59:59.934000+00:00 1.121055
[82484 rows x 1 columns]data = vbt.FinPyData.pull(
"EURUSD",
start="14 Jun 2016",
end="15 Jun 2016",
timeframe="tick",
category="fx",
data_source="dukascopy",
fields=["bid", "ask", "bidv", "askv"]
)
data.get() bid ask bidv askv
Date
2016-06-14 00:00:00.844000+00:00 1.12877 1.12882 1.00 10.12
2016-06-14 00:00:01.591000+00:00 1.12877 1.12881 1.00 1.00
2016-06-14 00:00:01.743000+00:00 1.12875 1.12880 3.11 3.00
2016-06-14 00:00:02.464000+00:00 1.12875 1.12879 2.21 1.00
2016-06-14 00:00:02.971000+00:00 1.12875 1.12877 2.21 1.00
... ... ... ... ...
2016-06-14 23:59:57.733000+00:00 1.12100 1.12104 1.24 1.50
2016-06-14 23:59:58.239000+00:00 1.12101 1.12105 9.82 1.12
2016-06-14 23:59:58.953000+00:00 1.12102 1.12105 1.50 1.12
2016-06-14 23:59:59.004000+00:00 1.12103 1.12107 1.50 1.12
2016-06-14 23:59:59.934000+00:00 1.12103 1.12108 1.87 2.25
[82484 rows x 4 columns]✅ Introducing a new class specialized in pulling data from Databento.
Get best bid and offer (BBO) data from Databento
vbt.BentoData.set_custom_settings(
client_config=dict(
key="YOUR_KEY"
)
)
params = dict(
symbols="ESH3",
dataset="GLBX.MDP3",
start=vbt.timestamp("2022-10-28 20:30:00"),
end=vbt.timestamp("2022-10-28 21:00:00"),
schema="tbbo"
)
vbt.BentoData.get_cost(**params)1.2002885341644287e-05data = vbt.BentoData.pull(**params)
data.get() ts_event \
ts_recv
2022-10-28 20:30:59.047138053+00:00 2022-10-28 20:30:59.046914657+00:00
2022-10-28 20:37:53.112494436+00:00 2022-10-28 20:37:53.112246421+00:00
...
2022-10-28 20:59:15.075191111+00:00 2022-10-28 20:59:15.074953895+00:00
2022-10-28 20:59:34.607239899+00:00 2022-10-28 20:59:34.606984277+00:00
rtype publisher_id instrument_id \
ts_recv
2022-10-28 20:30:59.047138053+00:00 1 1 206299
2022-10-28 20:37:53.112494436+00:00 1 1 206299
...
2022-10-28 20:59:15.075191111+00:00 1 1 206299
2022-10-28 20:59:34.607239899+00:00 1 1 206299
action side depth price size flags \
ts_recv
2022-10-28 20:30:59.047138053+00:00 T B 0 3955.25 1 0
2022-10-28 20:37:53.112494436+00:00 T A 0 3955.00 1 0
...
2022-10-28 20:59:15.075191111+00:00 T A 0 3953.75 1 0
2022-10-28 20:59:34.607239899+00:00 T A 0 3954.50 2 0
ts_in_delta sequence bid_px_00 \
ts_recv
2022-10-28 20:30:59.047138053+00:00 18553 73918214 3954.75
2022-10-28 20:37:53.112494436+00:00 18334 73926240 3955.00
...
2022-10-28 20:59:15.075191111+00:00 19294 73945515 3953.75
2022-10-28 20:59:34.607239899+00:00 18701 73945932 3954.50
ask_px_00 bid_sz_00 ask_sz_00 \
ts_recv
2022-10-28 20:30:59.047138053+00:00 3955.25 1 1
2022-10-28 20:37:53.112494436+00:00 3955.75 1 1
...
2022-10-28 20:59:15.075191111+00:00 3956.00 1 1
2022-10-28 20:59:34.607239899+00:00 3956.00 4 1
bid_ct_00 ask_ct_00 symbol
ts_recv
2022-10-28 20:30:59.047138053+00:00 1 1 ESH3
2022-10-28 20:37:53.112494436+00:00 1 1 ESH3
...
2022-10-28 20:59:15.075191111+00:00 1 1 ESH3
2022-10-28 20:59:34.607239899+00:00 3 1 ESH3✅ A new class specialized for pulling data from TradingView is now available.
Pull 1-minute AAPL data
data = vbt.TVData.pull(
"NASDAQ:AAPL",
timeframe="1 minute",
tz="US/Eastern"
)
data.get() Open High Low Close Volume
datetime
2022-12-05 09:30:00-05:00 147.75 148.31 147.50 148.28 37769.0
2022-12-05 09:31:00-05:00 148.28 148.67 148.28 148.49 10525.0
2022-12-05 09:32:00-05:00 148.50 148.73 148.30 148.30 4860.0
2022-12-05 09:33:00-05:00 148.25 148.73 148.25 148.64 5306.0
2022-12-05 09:34:00-05:00 148.62 148.97 148.52 148.97 5808.0
... ... ... ... ... ...
2023-01-17 15:55:00-05:00 135.80 135.91 135.80 135.86 37573.0
2023-01-17 15:56:00-05:00 135.85 135.88 135.80 135.88 18796.0
2023-01-17 15:57:00-05:00 135.88 135.93 135.85 135.91 21019.0
2023-01-17 15:58:00-05:00 135.90 135.97 135.89 135.95 20934.0
2023-01-17 15:59:00-05:00 135.94 136.00 135.84 135.94 86696.0
[11310 rows x 5 columns]✅ Most data classes can retrieve the complete list of symbols available for an exchange, and optionally filter the list using a globbing or regular expression pattern. This also works for local data classes.
Get all XRP pairs listed on Binance
vbt.BinanceData.list_symbols("XRP*"){'XRPAUD',
'XRPBEARBUSD',
'XRPBEARUSDT',
'XRPBIDR',
'XRPBKRW',
'XRPBNB',
'XRPBRL',
'XRPBTC',
'XRPBULLBUSD',
'XRPBULLUSDT',
'XRPBUSD',
'XRPDOWNUSDT',
'XRPETH',
'XRPEUR',
'XRPGBP',
'XRPNGN',
'XRPPAX',
'XRPRUB',
'XRPTRY',
'XRPTUSD',
'XRPUPUSDT',
'XRPUSDC',
'XRPUSDT'}✅ Since VBT leverages multi-indexes in Pandas, you can associate each symbol with one or more classes, such as sectors. This allows you to analyze the performance of a trading strategy relative to each class.
Compare equal-weighted portfolios for three sectors
classes = vbt.symbol_dict({
"MSFT": dict(sector="Technology"),
"GOOGL": dict(sector="Technology"),
"META": dict(sector="Technology"),
"JPM": dict(sector="Finance"),
"BAC": dict(sector="Finance"),
"WFC": dict(sector="Finance"),
"AMZN": dict(sector="Retail"),
"WMT": dict(sector="Retail"),
"BABA": dict(sector="Retail"),
})
data = vbt.YFData.pull(
list(classes.keys()),
classes=classes,
missing_index="drop"
)
pf = vbt.PF.from_orders(
data,
size=vbt.index_dict({0: 1 / 3}),
size_type="targetpercent",
group_by="sector",
cash_sharing=True
)
pf.value.vbt.plot().show()✅ Welcome a new class specialized in pulling data from Polygon.io!
Get one month of 30-minute AAPL data from Polygon.io
vbt.PolygonData.set_custom_settings(
client_config=dict(
api_key="YOUR_API_KEY"
)
)
data = vbt.PolygonData.pull(
"AAPL",
start="2022-12-01",
end="2023-01-01",
timeframe="30 minutes",
tz="US/Eastern"
)
data.get() Open High Low Close Volume \
Open time
2022-12-01 04:00:00-05:00 148.08 148.08 147.04 147.3700 50886.0
2022-12-01 04:30:00-05:00 147.37 147.37 147.12 147.2600 16575.0
2022-12-01 05:00:00-05:00 147.31 147.51 147.20 147.3800 20753.0
2022-12-01 05:30:00-05:00 147.43 147.56 147.38 147.3800 7388.0
2022-12-01 06:00:00-05:00 147.30 147.38 147.24 147.2400 7416.0
... ... ... ... ... ...
2022-12-30 17:30:00-05:00 129.94 130.05 129.91 129.9487 35694.0
2022-12-30 18:00:00-05:00 129.95 130.00 129.94 129.9500 15595.0
2022-12-30 18:30:00-05:00 129.94 130.05 129.94 130.0100 20287.0
2022-12-30 19:00:00-05:00 129.99 130.04 129.99 130.0000 12490.0
2022-12-30 19:30:00-05:00 130.00 130.04 129.97 129.9700 28271.0
Trade count VWAP
Open time
2022-12-01 04:00:00-05:00 1024 147.2632
2022-12-01 04:30:00-05:00 412 147.2304
2022-12-01 05:00:00-05:00 306 147.3466
2022-12-01 05:30:00-05:00 201 147.4818
2022-12-01 06:00:00-05:00 221 147.2938
... ... ...
2022-12-30 17:30:00-05:00 350 129.9672
2022-12-30 18:00:00-05:00 277 129.9572
2022-12-30 18:30:00-05:00 312 130.0034
2022-12-30 19:00:00-05:00 176 130.0140
2022-12-30 19:30:00-05:00 366 129.9941
[672 rows x 7 columns]✅ Welcome a new class specialized in pulling data from Alpha Vantage!
Get Stochastic RSI of IBM from Alpha Vantage
data = vbt.AVData.pull(
"IBM",
category="technical-indicators",
function="STOCHRSI",
params=dict(fastkperiod=14)
)
data.get() FastD FastK
1999-12-07 00:00:00+00:00 100.0000 100.0000
1999-12-08 00:00:00+00:00 100.0000 100.0000
1999-12-09 00:00:00+00:00 77.0255 31.0765
1999-12-10 00:00:00+00:00 43.6922 0.0000
1999-12-13 00:00:00+00:00 12.0197 4.9826
... ... ...
2023-01-26 00:00:00+00:00 11.7960 0.0000
2023-01-27 00:00:00+00:00 3.7773 0.0000
2023-01-30 00:00:00+00:00 4.4824 13.4471
2023-01-31 00:00:00+00:00 7.8258 10.0302
2023-02-01 16:00:01+00:00 13.0966 15.8126
[5826 rows x 2 columns]✅ Welcome a new class specialized in pulling data from Nasdaq Data Link!
Get Index of Consumer Sentiment from Nasdaq Data Link
data = vbt.NDLData.pull("UMICH/SOC1")
data.get() Index
Date
1952-11-30 00:00:00+00:00 86.2
1953-02-28 00:00:00+00:00 90.7
1953-08-31 00:00:00+00:00 80.8
1953-11-30 00:00:00+00:00 80.7
1954-02-28 00:00:00+00:00 82.0
... ...
2022-08-31 00:00:00+00:00 58.2
2022-09-30 00:00:00+00:00 58.6
2022-10-31 00:00:00+00:00 59.9
2022-11-30 00:00:00+00:00 56.8
2022-12-31 00:00:00+00:00 59.7
[632 rows x 1 columns]✅ Welcome a new class specialized in pulling data from Alpaca!
Get one week of adjusted 1-minute AAPL data from Alpaca
vbt.AlpacaData.set_custom_settings(
client_config=dict(
api_key="YOUR_API_KEY",
secret_key="YOUR_API_SECRET"
)
)
data = vbt.AlpacaData.pull(
"AAPL",
start="one week ago 00:00",
end="15 minutes ago",
timeframe="1 minute",
adjustment="all",
tz="US/Eastern"
)
data.get() Open High Low Close Volume \
Open time
2023-01-30 04:00:00-05:00 145.5400 145.5400 144.0100 144.0200 5452.0
2023-01-30 04:01:00-05:00 144.0800 144.0800 144.0000 144.0500 3616.0
2023-01-30 04:02:00-05:00 144.0300 144.0400 144.0100 144.0100 1671.0
2023-01-30 04:03:00-05:00 144.0100 144.0300 144.0000 144.0300 4721.0
2023-01-30 04:04:00-05:00 144.0200 144.0200 144.0200 144.0200 1343.0
... ... ... ... ... ...
2023-02-03 19:54:00-05:00 154.3301 154.3301 154.3301 154.3301 347.0
2023-02-03 19:55:00-05:00 154.3300 154.3400 154.3200 154.3400 1438.0
2023-02-03 19:56:00-05:00 154.3400 154.3400 154.3300 154.3300 588.0
2023-02-03 19:58:00-05:00 154.3500 154.3500 154.3500 154.3500 555.0
2023-02-03 19:59:00-05:00 154.3400 154.3900 154.3300 154.3900 3835.0
Trade count VWAP
Open time
2023-01-30 04:00:00-05:00 165 144.376126
2023-01-30 04:01:00-05:00 81 144.036336
2023-01-30 04:02:00-05:00 52 144.035314
2023-01-30 04:03:00-05:00 56 144.012680
2023-01-30 04:04:00-05:00 40 144.021854
... ... ...
2023-02-03 19:54:00-05:00 21 154.331340
2023-02-03 19:55:00-05:00 38 154.331756
2023-02-03 19:56:00-05:00 17 154.338971
2023-02-03 19:58:00-05:00 27 154.343090
2023-02-03 19:59:00-05:00 58 154.357219
[4224 rows x 7 columns]Copyright © 2021–2026 Oleg Polakow. All rights reserved.
Site content and documentation are provided for using and evaluating VectorBT PRO and for educational purposes. Any other use, including building or supporting competing products or services, requires prior written consent.