# Pandas TA Quant
Not only a pure python re-implementation of the famous [TA-Lib][e1]. Additional indicators are available like covariance
measures or arma, garch and sarimax models. The library fully builds on top of pandas and pandas_ml_common, therefore
allows to deal with MultiIndex easily:
| Date | ('spy', 'Open') | ('spy', 'High') | ('spy', 'Low') | ('spy', 'Close') | ('spy', 'Volume') | ('spy', 'Dividends') | ('spy', 'Stock Splits') | ('gld', 'Open') | ('gld', 'High') | ('gld', 'Low') | ('gld', 'Close') | ('gld', 'Volume') | ('gld', 'Dividends') | ('gld', 'Stock Splits') |
|:--------------------|------------------:|------------------:|-----------------:|-------------------:|--------------------:|-----------------------:|--------------------------:|------------------:|------------------:|-----------------:|-------------------:|--------------------:|-----------------------:|--------------------------:|
| 2020-02-07 00:00:00 | 332.82 | 333.99 | 331.6 | 332.2 | 6.41394e+07 | 0 | 0 | 147.83 | 148.18 | 147.34 | 147.79 | 6.3793e+06 | 0 | 0 |
| 2020-02-10 00:00:00 | 331.23 | 334.75 | 331.19 | 334.68 | 4.207e+07 | 0 | 0 | 148.21 | 148.45 | 147.91 | 148.17 | 5.7936e+06 | 0 | 0 |
```
df = pd.read_pickle("../pandas_ta_quant_test/.data/spy_gld.pickle")
df._[["Close", df._["Close"].ta.sma(200)]].plot(figsize=(20,10))
```
![Plot][ghi1]
## Full List of indicators
| | module |
|:-------------------------------|:------------------------------------------------------------------|
| ta_adx | pandas_ta_quant.technical_analysis.indicators.multi_object |
| ta_all | pandas_ta_quant.technical_analysis.indicators |
| ta_apo | pandas_ta_quant.technical_analysis.indicators.single_object |
| ta_atr | pandas_ta_quant.technical_analysis.indicators.multi_object |
| ta_bbands | pandas_ta_quant.technical_analysis.bands |
| ta_bbands_indicator | pandas_ta_quant.technical_analysis.indicators.single_object |
| ta_bop | pandas_ta_quant.technical_analysis.indicators.multi_object |
| ta_candle_category | pandas_ta_quant.technical_analysis.encoders.candles |
| ta_candles_as_culb | pandas_ta_quant.technical_analysis.encoders.candles |
| ta_cci | pandas_ta_quant.technical_analysis.indicators.multi_object |
| ta_cross | pandas_ta_quant.technical_analysis.labels.discrete |
| ta_cross_over | pandas_ta_quant.technical_analysis.labels.discrete |
| ta_cross_under | pandas_ta_quant.technical_analysis.labels.discrete |
| ta_decimal_year | pandas_ta_quant.technical_analysis.indicators.time |
| ta_delta_hedged_price | pandas_ta_quant.technical_analysis.normalizer |
| ta_div | pandas_ta_quant.technical_analysis.math |
| ta_draw_down | pandas_ta_quant.technical_analysis.indicators.single_object |
| ta_edge_detect | pandas_ta_quant.technical_analysis.forecast.support |
| ta_ema | pandas_ta_quant.technical_analysis.filters |
| ta_ewma_covariance | pandas_ta_quant.technical_analysis.covariances |
| ta_fibbonaci_retracement | pandas_ta_quant.technical_analysis.forecast.support |
| ta_future_bband_quantile | pandas_ta_quant.technical_analysis.labels.discrete |
| ta_future_crossings | pandas_ta_quant.technical_analysis.labels.discrete |
| ta_future_multi_bband_quantile | pandas_ta_quant.technical_analysis.labels.discrete |
| ta_future_multi_ma_quantile | pandas_ta_quant.technical_analysis.labels.discrete |
| ta_future_pct_to_current_mean | pandas_ta_quant.technical_analysis.labels.continuous |
| ta_gaf | pandas_ta_quant.technical_analysis.encoders.gramian_angular_field |
| ta_gap | pandas_ta_quant.technical_analysis.indicators.multi_object |
| ta_garch11 | pandas_ta_quant.technical_analysis.forecast.volatility |
| ta_has_opening_gap | pandas_ta_quant.technical_analysis.labels.discrete |
| ta_hmm | pandas_ta_quant.technical_analysis.forecast.predictive_indicator |
| ta_inverse | pandas_ta_quant.technical_analysis.encoders.resample |
| ta_inverse_gasf | pandas_ta_quant.technical_analysis.encoders.gramian_angular_field |
| ta_is_opening_gap_closed | pandas_ta_quant.technical_analysis.labels.discrete |
| ta_log_returns | pandas_ta_quant.technical_analysis.normalizer |
| ta_ma_decompose | pandas_ta_quant.technical_analysis.indicators.single_object |
| ta_ma_ratio | pandas_ta_quant.technical_analysis.normalizer |
| ta_macd | pandas_ta_quant.technical_analysis.indicators.single_object |
| ta_mean_returns | pandas_ta_quant.technical_analysis.indicators.single_object |
| ta_mgarch_covariance | pandas_ta_quant.technical_analysis.covariances |
| ta_mom | pandas_ta_quant.technical_analysis.indicators.single_object |
| ta_moving_covariance | pandas_ta_quant.technical_analysis.covariances |
| ta_multi_bbands | pandas_ta_quant.technical_analysis.filters |
| ta_multi_ma | pandas_ta_quant.technical_analysis.filters |
| ta_ncdf_compress | pandas_ta_quant.technical_analysis.normalizer |
| ta_normalize_row | pandas_ta_quant.technical_analysis.normalizer |
| ta_ohl_trend_lines | pandas_ta_quant.technical_analysis.forecast.support |
| ta_one_hot | pandas_ta_quant.technical_analysis.encoders.one_hot |
| ta_one_hot_encode_discrete | pandas_ta_quant.technical_analysis.encoders.one_hot |
| ta_performance | pandas_ta_quant.technical_analysis.normalizer |
| ta_poly_coeff | pandas_ta_quant.technical_analysis.indicators.single_object |
| ta_ppo | pandas_ta_quant.technical_analysis.indicators.single_object |
| ta_realative_candles | pandas_ta_quant.technical_analysis.encoders.candles |
| ta_rescale | pandas_ta_quant.technical_analysis.normalizer |
| ta_returns | pandas_ta_quant.technical_analysis.normalizer |
| ta_rnn | pandas_ta_quant.technical_analysis.encoders.auto_regression |
| ta_roc | pandas_ta_quant.technical_analysis.indicators.single_object |
| ta_rsi | pandas_ta_quant.technical_analysis.indicators.single_object |
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pandas-ta-quant-0.2.6.tar.gz (71个子文件)
pandas-ta-quant-0.2.6
pandas_ta_quant_test
__init__.py 39B
test_patching.py 468B
test__utils.py 1KB
check_links.py 173B
portfolio
__init__.py 39B
test__price.py 1KB
test__portfolio.py 6KB
test_notebooks.py 350B
test_multiindex_rows.py 600B
technical_analysis
__init__.py 0B
test_draw_down.py 629B
forecast
__init__.py 0B
test_forcasters.py 386B
test_vorlatility.py 407B
test_support_resistence.py 309B
encoders
__init__.py 0B
test__candles.py 836B
test__time.py 1KB
test_utils.py 2KB
test_covariances.py 1KB
test_fiters.py 667B
test_normalizer.py 1KB
indicators
__init__.py 0B
test__multi_index_columns.py 377B
test_the_all_indicator.py 1021B
test__features.py 8KB
test_meta.py 585B
config.py 1KB
noxfile.py 2KB
pandas_ta_quant.egg-info
SOURCES.txt 3KB
top_level.txt 37B
PKG-INFO 12KB
requires.txt 298B
dependency_links.txt 1B
setup.py 2KB
PKG-INFO 12KB
pandas_ta_quant
__init__.py 497B
_decorators.py 2KB
portfolio
__init__.py 62B
price.py 5KB
portfolio.py 14KB
pandas_patch.py 1KB
_utils.py 4KB
technical_analysis
__init__.py 237B
math.py 253B
normalizer.py 2KB
covariances.py 2KB
forecast
__init__.py 85B
predictive_indicator.py 2KB
support.py 5KB
volatility.py 2KB
encoders
__init__.py 70B
candles.py 4KB
volume.py 393B
resample.py 318B
edge_detect.py 2KB
bands.py 2KB
indicators
__init__.py 3KB
multi_object.py 7KB
time.py 1010B
single_object.py 10KB
meta.py 530B
filters.py 2KB
requirements.frozen.txt 131B
licence.txt 1KB
requirements.txt 201B
dev-requirements.txt 75B
MANIFEST.in 45B
setup.cfg 79B
Readme.md 10KB
dev-requirements.frozen.txt 134B
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