# Crypto This section contains 7 examples for Crypto using the `onetick-py`.
\\\\ Each example is a self-contained script that can be run against the OneTick Cloud sample databases. ```default # onetick-py WebAPI configuration for OneTick Cloud import os os.environ['OTP_WEBAPI'] = '1' os.environ['OTP_HTTP_ADDRESS'] = 'https://rest.cloud.onetick.com' os.environ['OTP_ACCESS_TOKEN_URL'] = 'https://cloud-auth.parent.onetick.com/realms/OMD/protocol/openid-connect/token' os.environ['OTP_CLIENT_ID'] = '__FILL_IN__' os.environ['OTP_CLIENT_SECRET'] = '__FILL_IN__' ``` ## Crypto Trade Retrieval Retrieving Trades from a Crypto Venue.
\\\\ Unlike Equities and Futures, the Trade Size on a crypto venue is fractional. ```ipython3 import onetick.py as otp data = otp.DataSource(db='BINANCE', tick_type='TRD') data = data.limit(1000) result = otp.run(data, start=otp.dt(2026, 7, 28), end=otp.dt(2026, 7, 29), timezone='UTC', symbols='BTCUSD') result ``` ```myst-ansi Time EXCH_TIME PRICE SIZE \ 0 2026-07-28 00:00:00.145371 2026-07-28 00:00:00.144160 63686.99 0.00008 1 2026-07-28 00:00:04.350446 2026-07-28 00:00:04.349603 63687.03 0.00130 2 2026-07-28 00:00:04.383817 2026-07-28 00:00:04.382689 63687.03 0.00158 3 2026-07-28 00:00:04.517794 2026-07-28 00:00:04.516315 63687.04 0.00008 4 2026-07-28 00:00:05.096039 2026-07-28 00:00:05.094642 63687.04 0.00008 .. ... ... ... ... 995 2026-07-28 11:18:21.409880 2026-07-28 11:18:21.408894 63456.00 0.00008 996 2026-07-28 11:18:21.409888 2026-07-28 11:18:21.408894 63472.87 0.00001 997 2026-07-28 11:18:33.822821 2026-07-28 11:18:33.821948 63480.00 0.00008 998 2026-07-28 11:18:45.583787 2026-07-28 11:18:45.582047 63468.01 0.00008 999 2026-07-28 11:18:45.584726 2026-07-28 11:18:45.583657 63480.01 0.00072 TRADE_VENUE BUYER SELLER AGGRESSOR_SIDE TRADE_TYPE TRADE_PERIOD BOOK_TYPE \ 0 BINANCE B - 0 1 BINANCE S - 0 2 BINANCE S - 0 3 BINANCE B - 0 4 BINANCE B - 0 .. ... ... ... ... ... ... ... 995 BINANCE B - 0 996 BINANCE B - 0 997 BINANCE B - 0 998 BINANCE B - 0 999 BINANCE S - 0 TRADE_ID OMDSEQ DELETED_TIME TICK_STATUS 0 606647 0 1970-01-01 0 1 606648 0 1970-01-01 0 2 606649 0 1970-01-01 0 3 606650 0 1970-01-01 0 4 606651 0 1970-01-01 0 .. ... ... ... ... 995 607642 0 1970-01-01 0 996 607643 1 1970-01-01 0 997 607644 0 1970-01-01 0 998 607645 0 1970-01-01 0 999 607646 0 1970-01-01 0 [1000 rows x 15 columns] ``` ## Crypto Quote Retrieval Retrieving Quotes from a Crypto Venue.
\\\\ Unlike Equities and Futures, the Bid and Ask Sizes on a crypto venue are fractional. ```ipython3 import onetick.py as otp data = otp.DataSource(db='BINANCE', tick_type='QTE') data = data.limit(1000) result = otp.run(data, start=otp.dt(2026, 7, 28), end=otp.dt(2026, 7, 29), timezone='UTC', symbols='BTCUSD') result ``` ```myst-ansi Time EXCH_TIME BID_PRICE BID_SIZE ASK_PRICE \ 0 2026-07-28 00:00:00.005578 1970-01-01 63686.98 0.03223 63714.40 1 2026-07-28 00:00:00.011008 1970-01-01 63686.98 0.03223 63686.99 2 2026-07-28 00:00:00.141446 1970-01-01 63681.91 0.00099 63686.99 3 2026-07-28 00:00:00.144103 1970-01-01 63682.16 0.03228 63686.99 4 2026-07-28 00:00:00.145341 1970-01-01 63682.16 0.03228 63714.40 .. ... ... ... ... ... 995 2026-07-28 00:11:57.949695 1970-01-01 63532.69 0.02221 63558.95 996 2026-07-28 00:11:58.084374 1970-01-01 63532.69 0.02221 63566.55 997 2026-07-28 00:11:58.370894 1970-01-01 63536.78 0.04423 63566.55 998 2026-07-28 00:11:58.374133 1970-01-01 63536.95 0.02221 63566.55 999 2026-07-28 00:11:58.419118 1970-01-01 63538.37 0.00099 63566.55 ASK_SIZE QUOTE_VENUE OMDSEQ 0 0.00319 BINANCE 18 1 0.00008 BINANCE 0 2 0.00008 BINANCE 1 3 0.00008 BINANCE 1 4 0.00319 BINANCE 0 .. ... ... ... 995 0.01555 BINANCE 14 996 0.01555 BINANCE 0 997 0.01555 BINANCE 48 998 0.01555 BINANCE 3 999 0.01555 BINANCE 3 [1000 rows x 8 columns] ``` ## Crypto Book Update Retrieval Retrieving Book Updates from a Crypto Venue.
\\\\ Book Updates are provided as an L2 dataset, providing updates to Price Levels.
\\\\ Basic retrieval is useful for counting order book changes.
\\\\ To reconstruct the order book, order book aggregations like [`ob_snapshot_wide()`](https://docs.pip.distribution.sol.onetick.com/api/source/ob_snapshot_wide.html.md#onetick.py.Source.ob_snapshot_wide) should be used. ```ipython3 import onetick.py as otp data = otp.DataSource(db='BINANCE', tick_type='PRL') data = data.limit(1000) result = otp.run(data, start=otp.dt(2026, 7, 28), end=otp.dt(2026, 7, 29), timezone='UTC', symbols='BTCUSD') result ``` ```myst-ansi Time EXCH_TIME BUY_SELL_FLAG \ 0 2026-07-28 00:00:00.000000 1970-01-01 00:00:00.000000 0 1 2026-07-28 00:00:00.000000 1970-01-01 00:00:00.000000 0 2 2026-07-28 00:00:00.000000 1970-01-01 00:00:00.000000 0 3 2026-07-28 00:00:00.000000 1970-01-01 00:00:00.000000 0 4 2026-07-28 00:00:00.000000 1970-01-01 00:00:00.000000 0 .. ... ... ... 995 2026-07-28 00:01:06.071861 2026-07-28 00:01:06.071349 1 996 2026-07-28 00:01:07.272235 2026-07-28 00:01:07.271454 0 997 2026-07-28 00:01:07.272235 2026-07-28 00:01:07.271454 0 998 2026-07-28 00:01:07.272235 2026-07-28 00:01:07.271454 0 999 2026-07-28 00:01:07.272235 2026-07-28 00:01:07.271454 1 PRICE SIZE RECORD_TYPE TICK_STATUS DELETED_TIME OMDSEQ 0 0.00 0.00000 Z 0 1970-01-01 91088 1 63686.98 0.03223 C 0 1970-01-01 91089 2 63681.91 0.00099 C 0 1970-01-01 91090 3 63680.47 0.04853 C 0 1970-01-01 91091 4 63676.83 0.00022 C 0 1970-01-01 91092 .. ... ... ... ... ... ... 995 63718.84 0.00000 R 0 1970-01-01 6 996 63693.00 0.00000 R 0 1970-01-01 1 997 63680.73 0.00000 R 0 1970-01-01 2 998 63676.63 0.04785 R 0 1970-01-01 3 999 63711.05 0.01457 R 0 1970-01-01 4 [1000 rows x 9 columns] ``` ## Crypto Book Snapshot Retrieval Retrieving a Book Snapshot at a Specified Time from a Crypto Venue.
\\\\ To reconstruct the order book, the [`ob_snapshot_wide()`](https://docs.pip.distribution.sol.onetick.com/api/source/ob_snapshot_wide.html.md#onetick.py.Source.ob_snapshot_wide) aggregation is used.
\\\\ As this is a crypto book, the `size_max_fractional_digits` attribute is set, allowing the book to be reconstructed with size stored with up to 9 fractional digits.
\\\\ [`ob_snapshot_wide()`](https://docs.pip.distribution.sol.onetick.com/api/source/ob_snapshot_wide.html.md#onetick.py.Source.ob_snapshot_wide) returns the book with Bid and Ask on the same row. ```ipython3 import onetick.py as otp data = otp.DataSource(db='BINANCE', tick_type='PRL') data = data.ob_snapshot_wide(size_max_fractional_digits=9) data = data[['BID_PRICE', 'BID_SIZE', 'ASK_PRICE', 'ASK_SIZE', 'LEVEL']] result = otp.run(data, start=otp.dt(2026, 7, 28, 12), end=otp.dt(2026, 7, 28, 12), timezone='UTC', symbols='BTCUSD') result ``` ```myst-ansi Time BID_PRICE BID_SIZE ASK_PRICE ASK_SIZE LEVEL 0 2026-07-28 12:00:00 63372.00 0.00008 63392.01 0.00008 1 1 2026-07-28 12:00:00 63366.05 0.02220 63396.00 0.00008 2 2 2026-07-28 12:00:00 63366.04 0.00100 63396.88 0.00076 3 3 2026-07-28 12:00:00 63364.00 0.00008 63399.00 0.00008 4 4 2026-07-28 12:00:00 63362.46 0.04722 63399.40 0.04762 5 .. ... ... ... ... ... ... 229 2026-07-28 12:00:00 32000.00 0.51281 NaN 0.00000 230 230 2026-07-28 12:00:00 30000.00 0.13842 NaN 0.00000 231 231 2026-07-28 12:00:00 23600.02 0.04238 NaN 0.00000 232 232 2026-07-28 12:00:00 19389.02 0.10057 NaN 0.00000 233 233 2026-07-28 12:00:00 18785.00 0.00368 NaN 0.00000 234 [234 rows x 6 columns] ``` ## Crypto Book Snapshot Retrieval with Accumulative Values Retrieving a Book Snapshot at a Specified Time from a Crypto Venue, outputting Accumulative Depth.
\\\\ To reconstruct the order book, the [`ob_snapshot_wide()`](https://docs.pip.distribution.sol.onetick.com/api/source/ob_snapshot_wide.html.md#onetick.py.Source.ob_snapshot_wide) aggregation is used.
\\\\ As this is a crypto book, the `size_max_fractional_digits` attribute is set.
\\\\ [`ob_snapshot_wide()`](https://docs.pip.distribution.sol.onetick.com/api/source/ob_snapshot_wide.html.md#onetick.py.Source.ob_snapshot_wide) returns the book in a format with Bid and Ask on the same row.
\\\\ Bid and Ask Value is calculated using `PRICE * SIZE`.
\\\\ The Bid and Ask Sizes are used to calculate accumulative sizes across the book depth, computed with a running sum aggregation across the levels. ```ipython3 import onetick.py as otp data = otp.DataSource(db='BINANCE', tick_type='PRL') data = data.ob_snapshot_wide(size_max_fractional_digits=9) # Value per level = PRICE * SIZE data['BID_VALUE'] = data['BID_PRICE'] * data['BID_SIZE'] data['ASK_VALUE'] = data['ASK_PRICE'] * data['ASK_SIZE'] # Accumulative sizes across the book depth data = data.agg({'ACCUM_BID_SIZE': otp.agg.sum('BID_SIZE'), 'ACCUM_ASK_SIZE': otp.agg.sum('ASK_SIZE')}, running=True, all_fields=True) data = data[['BID_PRICE', 'BID_SIZE', 'ASK_PRICE', 'ASK_SIZE', 'LEVEL', 'BID_VALUE', 'ASK_VALUE', 'ACCUM_BID_SIZE', 'ACCUM_ASK_SIZE']] result = otp.run(data, start=otp.dt(2026, 7, 28, 12), end=otp.dt(2026, 7, 28, 12), timezone='UTC', symbols='BTCUSD') result ``` ```myst-ansi Time BID_PRICE BID_SIZE ASK_PRICE ASK_SIZE LEVEL \ 0 2026-07-28 12:00:00 63372.00 0.00008 63392.01 0.00008 1 1 2026-07-28 12:00:00 63366.05 0.02220 63396.00 0.00008 2 2 2026-07-28 12:00:00 63366.04 0.00100 63396.88 0.00076 3 3 2026-07-28 12:00:00 63364.00 0.00008 63399.00 0.00008 4 4 2026-07-28 12:00:00 63362.46 0.04722 63399.40 0.04762 5 .. ... ... ... ... ... ... 229 2026-07-28 12:00:00 32000.00 0.51281 NaN 0.00000 230 230 2026-07-28 12:00:00 30000.00 0.13842 NaN 0.00000 231 231 2026-07-28 12:00:00 23600.02 0.04238 NaN 0.00000 232 232 2026-07-28 12:00:00 19389.02 0.10057 NaN 0.00000 233 233 2026-07-28 12:00:00 18785.00 0.00368 NaN 0.00000 234 BID_VALUE ASK_VALUE ACCUM_BID_SIZE ACCUM_ASK_SIZE 0 5.069760 5.071361 0.00008 0.00008 1 1406.726310 5.071680 0.02228 0.00016 2 63.366040 48.181629 0.02328 0.00092 3 5.069120 5.071920 0.02336 0.00100 4 2991.975298 3019.079428 0.07058 0.04862 .. ... ... ... ... 229 16409.919968 NaN 47.13080 6.48798 230 4152.599970 NaN 47.26922 6.48798 231 1000.168848 NaN 47.31160 6.48798 232 1949.953741 NaN 47.41217 6.48798 233 69.128800 NaN 47.41585 6.48798 [234 rows x 10 columns] ``` ## Crypto Book Snapshot Retrieval with Accumulative Values and Best Prices Retrieving a Book Snapshot at a Specified Time from a Crypto Venue, outputting Accumulative Depth and Best Prices.
\\\\ To reconstruct the order book, the [`ob_snapshot_wide()`](https://docs.pip.distribution.sol.onetick.com/api/source/ob_snapshot_wide.html.md#onetick.py.Source.ob_snapshot_wide) aggregation is used.
\\\\ As this is a crypto book, the `size_max_fractional_digits` attribute is set.
\\\\ [`ob_snapshot_wide()`](https://docs.pip.distribution.sol.onetick.com/api/source/ob_snapshot_wide.html.md#onetick.py.Source.ob_snapshot_wide) returns the book in a format with Bid and Ask on the same row.
\\\\ Bid and Ask Value is calculated using `PRICE * SIZE`.
\\\\ The Bid and Ask Sizes are used to calculate accumulative sizes across the book depth, computed with a running sum aggregation across the levels.
\\\\ The Bid and Ask Prices are used to return the Best Prices across the book depth, computed with a running first aggregation across the levels. ```ipython3 import onetick.py as otp data = otp.DataSource(db='BINANCE', tick_type='PRL') data = data.ob_snapshot_wide(size_max_fractional_digits=9) # Value per level = PRICE * SIZE data['BID_VALUE'] = data['BID_PRICE'] * data['BID_SIZE'] data['ASK_VALUE'] = data['ASK_PRICE'] * data['ASK_SIZE'] # Accumulative sizes and best (first) prices across the book depth data = data.agg({'ACCUM_BID_SIZE': otp.agg.sum('BID_SIZE'), 'ACCUM_ASK_SIZE': otp.agg.sum('ASK_SIZE'), 'BEST_BID_PRICE': otp.agg.first('BID_PRICE'), 'BEST_ASK_PRICE': otp.agg.first('ASK_PRICE')}, running=True, all_fields=True) data = data[['BID_PRICE', 'BID_SIZE', 'ASK_PRICE', 'ASK_SIZE', 'LEVEL', 'BID_VALUE', 'ASK_VALUE', 'ACCUM_BID_SIZE', 'ACCUM_ASK_SIZE', 'BEST_BID_PRICE', 'BEST_ASK_PRICE']] result = otp.run(data, start=otp.dt(2026, 7, 28, 12), end=otp.dt(2026, 7, 28, 12), timezone='UTC', symbols='BTCUSD') result ``` ```myst-ansi Time BID_PRICE BID_SIZE ASK_PRICE ASK_SIZE LEVEL \ 0 2026-07-28 12:00:00 63372.00 0.00008 63392.01 0.00008 1 1 2026-07-28 12:00:00 63366.05 0.02220 63396.00 0.00008 2 2 2026-07-28 12:00:00 63366.04 0.00100 63396.88 0.00076 3 3 2026-07-28 12:00:00 63364.00 0.00008 63399.00 0.00008 4 4 2026-07-28 12:00:00 63362.46 0.04722 63399.40 0.04762 5 .. ... ... ... ... ... ... 229 2026-07-28 12:00:00 32000.00 0.51281 NaN 0.00000 230 230 2026-07-28 12:00:00 30000.00 0.13842 NaN 0.00000 231 231 2026-07-28 12:00:00 23600.02 0.04238 NaN 0.00000 232 232 2026-07-28 12:00:00 19389.02 0.10057 NaN 0.00000 233 233 2026-07-28 12:00:00 18785.00 0.00368 NaN 0.00000 234 BID_VALUE ASK_VALUE ACCUM_BID_SIZE ACCUM_ASK_SIZE \ 0 5.069760 5.071361 0.00008 0.00008 1 1406.726310 5.071680 0.02228 0.00016 2 63.366040 48.181629 0.02328 0.00092 3 5.069120 5.071920 0.02336 0.00100 4 2991.975298 3019.079428 0.07058 0.04862 .. ... ... ... ... 229 16409.919968 NaN 47.13080 6.48798 230 4152.599970 NaN 47.26922 6.48798 231 1000.168848 NaN 47.31160 6.48798 232 1949.953741 NaN 47.41217 6.48798 233 69.128800 NaN 47.41585 6.48798 BEST_BID_PRICE BEST_ASK_PRICE 0 63372.0 63392.01 1 63372.0 63392.01 2 63372.0 63392.01 3 63372.0 63392.01 4 63372.0 63392.01 .. ... ... 229 63372.0 63392.01 230 63372.0 63392.01 231 63372.0 63392.01 232 63372.0 63392.01 233 63372.0 63392.01 [234 rows x 12 columns] ``` ## Crypto Book Depth Statistics to Trade a Specified Amount Across Time Calculating Bid and Ask VWAP and other Statistics across time from a Crypto Venue.
\\\\ To calculate Bid and Ask VWAP, the [`ob_summary()`](https://docs.pip.distribution.sol.onetick.com/api/source/ob_summary.html.md#onetick.py.Source.ob_summary) aggregation is used.
\\\\ As this is a crypto book, the `size_max_fractional_digits` attribute is set.
\\\\ The `max_depth_shares` attribute determines how much should be traded.
\\\\ The `bucket_interval` attribute determines how often to output the resulting book metrics. The returned `BID_VWAP` and `ASK_VWAP` can be used to calculate Effective Spread.
\\\\ The returned `BID_SIZE` and `ASK_SIZE` identify if the liquidity is present.
\\\\ The returned `BEST_ASK_PRICE` and `BEST_BID_PRICE` can be used to calculate the Price Skew together with the `BID_VWAP` and `ASK_VWAP`. ```ipython3 import onetick.py as otp data = otp.DataSource(db='BINANCE', tick_type='PRL') data = data.ob_summary(size_max_fractional_digits=9, bucket_interval=60, max_depth_shares=0.5) result = otp.run(data, start=otp.dt(2026, 7, 28), end=otp.dt(2026, 7, 29), timezone='UTC', symbols='BTCUSD') result ``` ```myst-ansi Time BID_SIZE BID_VWAP BEST_BID_PRICE \ 0 2026-07-28 00:01:00 0.22377 63496.083279 63704.61 1 2026-07-28 00:02:00 0.20724 63431.012768 63661.82 2 2026-07-28 00:03:00 0.21396 63430.612190 63659.39 3 2026-07-28 00:04:00 0.21634 63456.893842 63676.17 4 2026-07-28 00:05:00 0.23383 63463.745030 63684.12 ... ... ... ... ... 1435 2026-07-28 23:56:00 0.50000 63427.842243 63830.48 1436 2026-07-28 23:57:00 0.50000 63454.580934 63844.64 1437 2026-07-28 23:58:00 0.50000 63492.709298 63867.94 1438 2026-07-28 23:59:00 0.50000 63480.813434 63883.77 1439 2026-07-29 00:00:00 0.50000 63398.522961 63870.00 WORST_BID_PRICE NUM_BID_LEVELS ASK_SIZE ASK_VWAP BEST_ASK_PRICE \ 0 61456.58 27 0.21492 63941.122894 63729.88 1 60501.70 27 0.19659 63922.769244 63691.04 2 60501.70 29 0.19111 63926.366503 63687.06 3 60501.70 29 0.19814 63926.443466 63701.86 4 59985.88 34 0.21125 63926.795975 63715.60 ... ... ... ... ... ... 1435 61500.00 108 0.50000 64565.474223 63852.92 1436 61500.00 108 0.50000 63911.337560 63865.15 1437 61596.75 107 0.50000 63932.208524 63893.97 1438 61500.00 110 0.50000 64286.294234 63910.23 1439 61500.00 108 0.50000 64120.600046 63871.39 WORST_ASK_PRICE NUM_ASK_LEVELS 0 66042.64 27 1 65999.89 26 2 65994.78 28 3 65960.17 28 4 65938.23 30 ... ... ... 1435 67217.67 79 1436 63979.36 20 1437 63951.99 14 1438 66227.80 73 1439 66171.01 75 [1440 rows x 11 columns] ```