# Polars Extension for Ta-Lib
## Getting Started
``` bash
pip install polars_talib
```
and
```
import polars
import polars_talib as plta
```
## Usage
### single symbol usage
``` python
df.with_columns(
pl.col("close").ta.ema(5).alias("ema5"),
pl.col("close").ta.macd(12, 26, 9).struct.field("macd"),
pl.col("close").ta.macd(12, 26, 9).struct.field("macdsignal"),
pl.col("open").ta.cdl2crows(pl.col("high"), pl.col("low"), pl.col("close")).alias("cdl2crows"),
pl.col("close").ta.wclprice("high", "low").alias("wclprice"),
)
```
### multiple symbol usage using over syntax
``` python
df.with_columns(
pl.col("close").ta.ema(5).over("symbol").alias("ema5"),
pl.col("close").ta.macd(12, 26, 9).over("symbol").struct.field("macd"),
pl.col("close").ta.macd(12, 26, 9).over("symbol").struct.field("macdsignal"),
pl.col("open").ta.cdl2crows(
pl.col("high"), pl.col("low"), pl.col("close")
).over("symbol").alias("cdl2crows"),
pl.col("close").ta.wclprice("high", "low").over("symbol").alias("wclprice"),
)
```
### usage just like talib.abstract with more flexible
``` python
df.with_columns(
plta.ht_dcperiod(),
plta.ht_dcperiod(pl.col("close")),
plta.aroon(),
plta.aroon(pl.col("high"), pl.col("low"), timeperiod=10),
plta.wclprice(),
plta.wclprice(
pl.col("high"), pl.col("low"), pl.col("close"),
timeperiod=10
),
)
```
## Performance
### Polars with polars_talib
``` python
%%timeit
df = p.with_columns(
plta.sma(timeperiod=5).over("Symbol").alias("sma5"),
plta.macd(fastperiod=10, slowperiod=20, signalperiod=5).over("Symbol").alias("macd"),
plta.stoch(pl.col("high"), pl.col("low"), pl.col("close"), fastk_period=14, slowk_period=7, slowd_period=7).over("Symbol").alias("stoch"),
plta.wclprice().over("Symbol").alias("wclprice"),
).with_columns(
pl.col("macd").struct.field("macd"),
pl.col("macd").struct.field("macdsignal"),
pl.col("macd").struct.field("macdhist"),
pl.col("stoch").struct.field("slowk"),
pl.col("stoch").struct.field("slowd"),
).select(
pl.exclude("stoch")
).filter(
pl.col("Symbol") == "AAPL"
).collect()
```
135 ms ± 5.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
### Pandas with talib
```python
%%timeit
df["sma5"] = df.groupby("Ticker")["close"].transform(lambda x: ta.SMA(x, timeperiod=5))
df["macd"] = df.groupby("Ticker")["close"].transform(lambda x: ta.MACD(x, fastperiod=10, slowperiod=20, signalperiod=5)[0])
df["macdsignal"] = df.groupby("Ticker")["close"].transform(lambda x: ta.MACD(x, fastperiod=10, slowperiod=20, signalperiod=5)[1])
df["macdhist"] = df.groupby("Ticker")["close"].transform(lambda x: ta.MACD(x, fastperiod=10, slowperiod=20, signalperiod=5)[2])
df["slowk"] = df.groupby("Ticker").apply(lambda x: ta.STOCH(x, fastk_period=14, slowk_period=7, slowd_period=7)).droplevel(0)["slowk"]
df["slowd"] = df.groupby("Ticker").apply(lambda x: ta.STOCH(x, fastk_period=14, slowk_period=7, slowd_period=7)).droplevel(0)["slowd"]
df["wclprice"] = df.groupby("Ticker").apply(lambda x: ta.WCLPRICE(x)).droplevel(0)
df.loc["AAPL"]
```
19.2 s ± 367 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
It's about 150x faster, see more detail in [basic.ipynb](./examples/basic.ipynb)
## Supported Indicators and Functions
``` python
import polars_talib as plta
# list of functions
plta.get_functions()
# dict of functions by group
plta.get_function_groups()
```
### Indicator Groups
* Overlap Studies
* Momentum Indicators
* Volume Indicators
* Volatility Indicators
* Price Transform
* Cycle Indicators
* Pattern Recognition
##### Overlap Studies
```
bbands Bollinger Bands
dema Double Exponential Moving Average
ema Exponential Moving Average
ht_trendline Hilbert Transform - Instantaneous Trendline
kama Kaufman Adaptive Moving Average
ma Moving average
mama MESA Adaptive Moving Average
mavp Moving average with variable period
midpoint MidPoint over period
midprice Midpoint Price over period
sar Parabolic SAR
sarext Parabolic SAR - Extended
sma Simple Moving Average
t3 Triple Exponential Moving Average (T3)
tema Triple Exponential Moving Average
trima Triangular Moving Average
wma Weighted Moving Average
```
##### Momentum Indicators
```
adx Average Directional Movement Index
adxr Average Directional Movement Index Rating
apo Absolute Price Oscillator
aroon Aroon
aroonosc Aroon Oscillator
bop Balance Of Power
cci Commodity Channel Index
cmo Chande Momentum Oscillator
dx Directional Movement Index
macd Moving Average Convergence/Divergence
macdext MACD with controllable MA type
macdfix Moving Average Convergence/Divergence Fix 12/26
mfi Money Flow Index
minus_di Minus Directional Indicator
minus_dm Minus Directional Movement
mom Momentum
plus_di Plus Directional Indicator
plus_dm Plus Directional Movement
ppo Percentage Price Oscillator
roc Rate of change : ((price/prevPrice)-1)*100
rocp Rate of change Percentage: (price-prevPrice)/prevPrice
rocr Rate of change ratio: (price/prevPrice)
rocr100 Rate of change ratio 100 scale: (price/prevPrice)*100
rsi Relative Strength Index
stoch Stochastic
stochf Stochastic Fast
stochrsi Stochastic Relative Strength Index
trix 1-day Rate-Of-Change (ROC) of a Triple Smooth EMA
ultosc Ultimate Oscillator
willr Williams' %R
```
##### Volume Indicators
```
ad Chaikin A/D Line
adosc Chaikin A/D Oscillator
obv On Balance Volume
```
##### Cycle Indicators
```
ht_dcperiod Hilbert Transform - Dominant Cycle Period
ht_dcphase Hilbert Transform - Dominant Cycle Phase
ht_phasor Hilbert Transform - Phasor Components
ht_sine Hilbert Transform - SineWave
ht_trendmode Hilbert Transform - Trend vs Cycle Mode
```
##### Price Transform
```
avgprice Average Price
medprice Median Price
typprice Typical Price
wclprice Weighted Close Price
```
##### Volatility Indicators
```
atr Average True Range
natr Normalized Average True Range
trange True Range
```
##### Pattern Recognition
```
cdl2crows Two Crows
cdl3blackcrows Three Black Crows
cdl3inside Three Inside Up/Down
cdl3linestrike Three-Line Strike
cdl3outside Three Outside Up/Down
cdl3starsinsoutH Three Stars In The South
cdl3whitesoldieRS Three Advancing White Soldiers
cdlabandonedbabY Abandoned Baby
cdladvanceblock Advance Block
cdlbelthold Belt-hold
cdlbreakaway Breakaway
cdlclosingmarubOZU Closing Marubozu
cdlconcealbabysWALL Concealing Baby Swallow
cdlcounterattacK Counterattack
cdldarkcloudcovER Dark Cloud Cover
cdldoji Doji
cdldojistar Doji Star
cdldragonflydojI Dragonfly Doji
cdlengulfing Engulfing Pattern
cdleveningdojisTAR Evening Doji Star
cdleveningstar Evening Star
cdlgapsidesidewHITE Up/Down-gap side-by-side white lines
cdlgravestonedoJI Gravestone Doji
cdlhammer Hammer
cdlhangingman Hanging Man
cdlharami Harami Pattern
cdlharamicross Harami Cross Pattern
cdlhighwave High-Wave Candle
cdlhikkake Hikkake Pattern
cdlhikkakemod Modified Hikkake Pattern
cdlhomingpigeon Homing Pigeon
cdlidentical3crOWS Identical Three Crows
cdlinneck In-Neck Pattern
cdlinvertedhammER Inverted Hammer
cdlkicking Kicking
cdlkickingbylenGTH Kicking - bull/bear determined by the longer marubozu
cdlladderbottom Ladder Bottom
cdllongleggeddoJI Long Legged Doji
cdllongline Long Line Candle
cdlmarubozu Marubozu
cdlmatchinglow Matching Low
cdlmathold Mat Hold
cdlmorningdojisTAR Morning Doji Star
cdlmorningstar Morning Star
cdlonneck On-Neck Pattern
cdlpiercing Piercing Pattern
cdlrickshawman Rickshaw Man
cdlrisefall3metHODS Rising/Falling Three Methods
cdlseparatingliNES Separating Lines
cdlshootingstar Shooting Star
cdlshortline Short Line Candle
cdlspinningtop Spinning Top
cdlstalledpatteRN Stalled Pattern
cdlsticksandwicH Stick Sandwich
cdltakuri Takuri (Dragonfly Doji with very long lower shadow)
cdltasukigap Tasuki Gap
cdlthrusting Thrusting Pattern
cdltristar Tristar Pattern
cdlunique3river Unique 3 River
cdlupsidegap2crOWS Upside Gap Two Crows
cdlxsidegap3metHODS Upside/Downside Gap Three Methods
```
##### Statistic Functions
```
beta Beta
correl Pearson's Correlation Coefficient (r)
linearreg Linear Regression
linearreg_angle Linear Regression Angle
linearreg_intercept Linear Regression Intercept
linearreg_slope Linear Regression Slope
stddev Standard Deviation
tsf Time Series Forecast
var Variance
```
Raw data
{
"_id": null,
"home_page": null,
"name": "polars-talib",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.7",
"maintainer_email": null,
"keywords": "polars-extension, talib, technical-analysis, TA-Lib, polars, rust, polars-expr",
"author": null,
"author_email": "yvictor <yvictor3141@gmail.com>",
"download_url": null,
"platform": null,
"description": "# Polars Extension for Ta-Lib\n\n\n## Getting Started\n\n``` bash\npip install polars_talib\n```\n\nand\n\n```\nimport polars\nimport polars_talib as plta\n```\n\n## Usage\n\n### single symbol usage\n``` python\ndf.with_columns(\n pl.col(\"close\").ta.ema(5).alias(\"ema5\"),\n pl.col(\"close\").ta.macd(12, 26, 9).struct.field(\"macd\"),\n pl.col(\"close\").ta.macd(12, 26, 9).struct.field(\"macdsignal\"),\n pl.col(\"open\").ta.cdl2crows(pl.col(\"high\"), pl.col(\"low\"), pl.col(\"close\")).alias(\"cdl2crows\"),\n pl.col(\"close\").ta.wclprice(\"high\", \"low\").alias(\"wclprice\"),\n)\n```\n\n### multiple symbol usage using over syntax\n``` python\ndf.with_columns(\n pl.col(\"close\").ta.ema(5).over(\"symbol\").alias(\"ema5\"),\n pl.col(\"close\").ta.macd(12, 26, 9).over(\"symbol\").struct.field(\"macd\"),\n pl.col(\"close\").ta.macd(12, 26, 9).over(\"symbol\").struct.field(\"macdsignal\"),\n pl.col(\"open\").ta.cdl2crows(\n pl.col(\"high\"), pl.col(\"low\"), pl.col(\"close\")\n ).over(\"symbol\").alias(\"cdl2crows\"),\n pl.col(\"close\").ta.wclprice(\"high\", \"low\").over(\"symbol\").alias(\"wclprice\"),\n)\n```\n\n### usage just like talib.abstract with more flexible\n``` python\ndf.with_columns(\n plta.ht_dcperiod(),\n plta.ht_dcperiod(pl.col(\"close\")),\n plta.aroon(),\n plta.aroon(pl.col(\"high\"), pl.col(\"low\"), timeperiod=10),\n plta.wclprice(),\n plta.wclprice(\n pl.col(\"high\"), pl.col(\"low\"), pl.col(\"close\"), \n timeperiod=10\n ),\n)\n```\n## Performance\n\n### Polars with polars_talib\n``` python\n%%timeit\ndf = p.with_columns(\n plta.sma(timeperiod=5).over(\"Symbol\").alias(\"sma5\"),\n plta.macd(fastperiod=10, slowperiod=20, signalperiod=5).over(\"Symbol\").alias(\"macd\"),\n plta.stoch(pl.col(\"high\"), pl.col(\"low\"), pl.col(\"close\"), fastk_period=14, slowk_period=7, slowd_period=7).over(\"Symbol\").alias(\"stoch\"),\n plta.wclprice().over(\"Symbol\").alias(\"wclprice\"),\n).with_columns(\n pl.col(\"macd\").struct.field(\"macd\"),\n pl.col(\"macd\").struct.field(\"macdsignal\"),\n pl.col(\"macd\").struct.field(\"macdhist\"),\n pl.col(\"stoch\").struct.field(\"slowk\"),\n pl.col(\"stoch\").struct.field(\"slowd\"),\n).select(\n pl.exclude(\"stoch\")\n).filter(\n pl.col(\"Symbol\") == \"AAPL\"\n).collect()\n```\n\n135 ms \u00b1 5.6 ms per loop (mean \u00b1 std. dev. of 7 runs, 1 loop each)\n\n### Pandas with talib\n```python\n%%timeit\ndf[\"sma5\"] = df.groupby(\"Ticker\")[\"close\"].transform(lambda x: ta.SMA(x, timeperiod=5))\ndf[\"macd\"] = df.groupby(\"Ticker\")[\"close\"].transform(lambda x: ta.MACD(x, fastperiod=10, slowperiod=20, signalperiod=5)[0])\ndf[\"macdsignal\"] = df.groupby(\"Ticker\")[\"close\"].transform(lambda x: ta.MACD(x, fastperiod=10, slowperiod=20, signalperiod=5)[1])\ndf[\"macdhist\"] = df.groupby(\"Ticker\")[\"close\"].transform(lambda x: ta.MACD(x, fastperiod=10, slowperiod=20, signalperiod=5)[2])\ndf[\"slowk\"] = df.groupby(\"Ticker\").apply(lambda x: ta.STOCH(x, fastk_period=14, slowk_period=7, slowd_period=7)).droplevel(0)[\"slowk\"] \ndf[\"slowd\"] = df.groupby(\"Ticker\").apply(lambda x: ta.STOCH(x, fastk_period=14, slowk_period=7, slowd_period=7)).droplevel(0)[\"slowd\"]\ndf[\"wclprice\"] = df.groupby(\"Ticker\").apply(lambda x: ta.WCLPRICE(x)).droplevel(0)\ndf.loc[\"AAPL\"]\n```\n19.2 s \u00b1 367 ms per loop (mean \u00b1 std. dev. of 7 runs, 1 loop each)\n\nIt's about 150x faster, see more detail in [basic.ipynb](./examples/basic.ipynb)\n\n## Supported Indicators and Functions\n\n``` python\nimport polars_talib as plta\n\n# list of functions\nplta.get_functions()\n\n# dict of functions by group\nplta.get_function_groups()\n```\n\n\n\n### Indicator Groups\n\n* Overlap Studies\n* Momentum Indicators\n* Volume Indicators\n* Volatility Indicators\n* Price Transform\n* Cycle Indicators\n* Pattern Recognition\n\n##### Overlap Studies\n```\nbbands Bollinger Bands\ndema Double Exponential Moving Average\nema Exponential Moving Average\nht_trendline Hilbert Transform - Instantaneous Trendline\nkama Kaufman Adaptive Moving Average\nma Moving average\nmama MESA Adaptive Moving Average\nmavp Moving average with variable period\nmidpoint MidPoint over period\nmidprice Midpoint Price over period\nsar Parabolic SAR\nsarext Parabolic SAR - Extended\nsma Simple Moving Average\nt3 Triple Exponential Moving Average (T3)\ntema Triple Exponential Moving Average\ntrima Triangular Moving Average\nwma Weighted Moving Average\n```\n\n##### Momentum Indicators\n```\nadx Average Directional Movement Index\nadxr Average Directional Movement Index Rating\napo Absolute Price Oscillator\naroon Aroon\naroonosc Aroon Oscillator\nbop Balance Of Power\ncci Commodity Channel Index\ncmo Chande Momentum Oscillator\ndx Directional Movement Index\nmacd Moving Average Convergence/Divergence\nmacdext MACD with controllable MA type\nmacdfix Moving Average Convergence/Divergence Fix 12/26\nmfi Money Flow Index\nminus_di Minus Directional Indicator\nminus_dm Minus Directional Movement\nmom Momentum\nplus_di Plus Directional Indicator\nplus_dm Plus Directional Movement\nppo Percentage Price Oscillator\nroc Rate of change : ((price/prevPrice)-1)*100\nrocp Rate of change Percentage: (price-prevPrice)/prevPrice\nrocr Rate of change ratio: (price/prevPrice)\nrocr100 Rate of change ratio 100 scale: (price/prevPrice)*100\nrsi Relative Strength Index\nstoch Stochastic\nstochf Stochastic Fast\nstochrsi Stochastic Relative Strength Index\ntrix 1-day Rate-Of-Change (ROC) of a Triple Smooth EMA\nultosc Ultimate Oscillator\nwillr Williams' %R\n```\n\n##### Volume Indicators\n```\nad Chaikin A/D Line\nadosc Chaikin A/D Oscillator\nobv On Balance Volume\n```\n\n##### Cycle Indicators\n```\nht_dcperiod Hilbert Transform - Dominant Cycle Period\nht_dcphase Hilbert Transform - Dominant Cycle Phase\nht_phasor Hilbert Transform - Phasor Components\nht_sine Hilbert Transform - SineWave\nht_trendmode Hilbert Transform - Trend vs Cycle Mode\n```\n\n##### Price Transform\n```\navgprice Average Price\nmedprice Median Price\ntypprice Typical Price\nwclprice Weighted Close Price\n```\n\n##### Volatility Indicators\n```\natr Average True Range\nnatr Normalized Average True Range\ntrange True Range\n```\n\n##### Pattern Recognition\n```\ncdl2crows Two Crows\ncdl3blackcrows Three Black Crows\ncdl3inside Three Inside Up/Down\ncdl3linestrike Three-Line Strike\ncdl3outside Three Outside Up/Down\ncdl3starsinsoutH Three Stars In The South\ncdl3whitesoldieRS Three Advancing White Soldiers\ncdlabandonedbabY Abandoned Baby\ncdladvanceblock Advance Block\ncdlbelthold Belt-hold\ncdlbreakaway Breakaway\ncdlclosingmarubOZU Closing Marubozu\ncdlconcealbabysWALL Concealing Baby Swallow\ncdlcounterattacK Counterattack\ncdldarkcloudcovER Dark Cloud Cover\ncdldoji Doji\ncdldojistar Doji Star\ncdldragonflydojI Dragonfly Doji\ncdlengulfing Engulfing Pattern\ncdleveningdojisTAR Evening Doji Star\ncdleveningstar Evening Star\ncdlgapsidesidewHITE Up/Down-gap side-by-side white lines\ncdlgravestonedoJI Gravestone Doji\ncdlhammer Hammer\ncdlhangingman Hanging Man\ncdlharami Harami Pattern\ncdlharamicross Harami Cross Pattern\ncdlhighwave High-Wave Candle\ncdlhikkake Hikkake Pattern\ncdlhikkakemod Modified Hikkake Pattern\ncdlhomingpigeon Homing Pigeon\ncdlidentical3crOWS Identical Three Crows\ncdlinneck In-Neck Pattern\ncdlinvertedhammER Inverted Hammer\ncdlkicking Kicking\ncdlkickingbylenGTH Kicking - bull/bear determined by the longer marubozu\ncdlladderbottom Ladder Bottom\ncdllongleggeddoJI Long Legged Doji\ncdllongline Long Line Candle\ncdlmarubozu Marubozu\ncdlmatchinglow Matching Low\ncdlmathold Mat Hold\ncdlmorningdojisTAR Morning Doji Star\ncdlmorningstar Morning Star\ncdlonneck On-Neck Pattern\ncdlpiercing Piercing Pattern\ncdlrickshawman Rickshaw Man\ncdlrisefall3metHODS Rising/Falling Three Methods\ncdlseparatingliNES Separating Lines\ncdlshootingstar Shooting Star\ncdlshortline Short Line Candle\ncdlspinningtop Spinning Top\ncdlstalledpatteRN Stalled Pattern\ncdlsticksandwicH Stick Sandwich\ncdltakuri Takuri (Dragonfly Doji with very long lower shadow)\ncdltasukigap Tasuki Gap\ncdlthrusting Thrusting Pattern\ncdltristar Tristar Pattern\ncdlunique3river Unique 3 River\ncdlupsidegap2crOWS Upside Gap Two Crows\ncdlxsidegap3metHODS Upside/Downside Gap Three Methods\n```\n\n##### Statistic Functions\n```\nbeta Beta\ncorrel Pearson's Correlation Coefficient (r)\nlinearreg Linear Regression\nlinearreg_angle Linear Regression Angle\nlinearreg_intercept Linear Regression Intercept\nlinearreg_slope Linear Regression Slope\nstddev Standard Deviation\ntsf Time Series Forecast\nvar Variance\n```\n",
"bugtrack_url": null,
"license": null,
"summary": "Polars extension for Ta-Lib: Support Ta-Lib functions in Polars expressions",
"version": "0.1.5",
"project_urls": null,
"split_keywords": [
"polars-extension",
" talib",
" technical-analysis",
" ta-lib",
" polars",
" rust",
" polars-expr"
],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "c95a7fb40579f8f37e86f4f90eb319a32760c0e4c0d919acb419916493533a06",
"md5": "a159525a358d204037d0ace838a23586",
"sha256": "1af11b5dbc6b50d69af4ec809cb2ca12378fe8d7b91d0871f59de5d23948634f"
},
"downloads": -1,
"filename": "polars_talib-0.1.5-cp37-abi3-macosx_10_12_x86_64.whl",
"has_sig": false,
"md5_digest": "a159525a358d204037d0ace838a23586",
"packagetype": "bdist_wheel",
"python_version": "cp37",
"requires_python": ">=3.7",
"size": 3727507,
"upload_time": "2025-01-24T12:02:37",
"upload_time_iso_8601": "2025-01-24T12:02:37.683774Z",
"url": "https://files.pythonhosted.org/packages/c9/5a/7fb40579f8f37e86f4f90eb319a32760c0e4c0d919acb419916493533a06/polars_talib-0.1.5-cp37-abi3-macosx_10_12_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "1da908c203d8e256d3490f960e390ec4046d7079df027eef13bae7c14976a0fd",
"md5": "03cb51d65769725fc1cc9afd77728c70",
"sha256": "990a488c17e96d8c816e01cef0d8eed925291a9da607081c38fc767ef665af42"
},
"downloads": -1,
"filename": "polars_talib-0.1.5-cp37-abi3-macosx_11_0_arm64.whl",
"has_sig": false,
"md5_digest": "03cb51d65769725fc1cc9afd77728c70",
"packagetype": "bdist_wheel",
"python_version": "cp37",
"requires_python": ">=3.7",
"size": 3551492,
"upload_time": "2025-01-24T12:02:42",
"upload_time_iso_8601": "2025-01-24T12:02:42.662200Z",
"url": "https://files.pythonhosted.org/packages/1d/a9/08c203d8e256d3490f960e390ec4046d7079df027eef13bae7c14976a0fd/polars_talib-0.1.5-cp37-abi3-macosx_11_0_arm64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "3989db0dcfd565b9089c6acf15f976a81c6010a5c8c3b4c5f713cc80846a4430",
"md5": "03f0ee0d4591e01f610a0da70867a407",
"sha256": "d794e0e4a4f0c28870adc8f0b7b9a89761d232db8c3534d7f8ef382cfcc6fab0"
},
"downloads": -1,
"filename": "polars_talib-0.1.5-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"has_sig": false,
"md5_digest": "03f0ee0d4591e01f610a0da70867a407",
"packagetype": "bdist_wheel",
"python_version": "cp37",
"requires_python": ">=3.7",
"size": 4486597,
"upload_time": "2025-01-24T12:02:44",
"upload_time_iso_8601": "2025-01-24T12:02:44.750017Z",
"url": "https://files.pythonhosted.org/packages/39/89/db0dcfd565b9089c6acf15f976a81c6010a5c8c3b4c5f713cc80846a4430/polars_talib-0.1.5-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "0d1b442bcdef40486474215e49fdb5eac58c474e32e465390ef08521568f9bf6",
"md5": "d7c437c4f8abec9c747c28109afa2d9b",
"sha256": "da82f845f5f30de0108d1490eaf91c7a7e55d603f75cebd0112f56cb0d82b4fe"
},
"downloads": -1,
"filename": "polars_talib-0.1.5-cp37-abi3-manylinux_2_38_aarch64.whl",
"has_sig": false,
"md5_digest": "d7c437c4f8abec9c747c28109afa2d9b",
"packagetype": "bdist_wheel",
"python_version": "cp37",
"requires_python": ">=3.7",
"size": 4074481,
"upload_time": "2025-01-24T12:02:46",
"upload_time_iso_8601": "2025-01-24T12:02:46.729357Z",
"url": "https://files.pythonhosted.org/packages/0d/1b/442bcdef40486474215e49fdb5eac58c474e32e465390ef08521568f9bf6/polars_talib-0.1.5-cp37-abi3-manylinux_2_38_aarch64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "06bd2f53bb4cf3d2037ee43a7b85c4e984caeebb69ca17d76146832e04d69230",
"md5": "6a0f653168f8c018f6c9fb35292d00f2",
"sha256": "61638172eefa7e8cf531ce88ddbf11ccf2fdea4ef06246b89126e026c055dcc8"
},
"downloads": -1,
"filename": "polars_talib-0.1.5-cp37-abi3-win32.whl",
"has_sig": false,
"md5_digest": "6a0f653168f8c018f6c9fb35292d00f2",
"packagetype": "bdist_wheel",
"python_version": "cp37",
"requires_python": ">=3.7",
"size": 2944592,
"upload_time": "2025-01-24T12:02:49",
"upload_time_iso_8601": "2025-01-24T12:02:49.224991Z",
"url": "https://files.pythonhosted.org/packages/06/bd/2f53bb4cf3d2037ee43a7b85c4e984caeebb69ca17d76146832e04d69230/polars_talib-0.1.5-cp37-abi3-win32.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "51b88084249359790c98f463569e98331fd5686d82fce7e50790333e49bda609",
"md5": "728fc1d968f51edefdd95e78959ca7d1",
"sha256": "7fb2d64d3b92eb2625ac667e5395b1baf704ed6ee8a1e250eaacbde47d8d10d0"
},
"downloads": -1,
"filename": "polars_talib-0.1.5-cp37-abi3-win_amd64.whl",
"has_sig": false,
"md5_digest": "728fc1d968f51edefdd95e78959ca7d1",
"packagetype": "bdist_wheel",
"python_version": "cp37",
"requires_python": ">=3.7",
"size": 3470437,
"upload_time": "2025-01-24T12:02:50",
"upload_time_iso_8601": "2025-01-24T12:02:50.983861Z",
"url": "https://files.pythonhosted.org/packages/51/b8/8084249359790c98f463569e98331fd5686d82fce7e50790333e49bda609/polars_talib-0.1.5-cp37-abi3-win_amd64.whl",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-01-24 12:02:37",
"github": false,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"lcname": "polars-talib"
}
JSON