Augment pandas DataFrame with methods for machine learning
Project description
Pandas TA Quant
Not only a pure python re-implementation of the famous TA-Lib. 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))
Full List of indicators
To get a full list if indicators as DataFrame use df.ta.help.
Here is a non-complete ever-growing list:
| 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 |
| ta_sarimax | pandas_ta_quant.technical_analysis.forecast.predictive_indicator |
| ta_sharpe_ratio | pandas_ta_quant.technical_analysis.indicators.single_object |
| ta_sinusoidal_week | pandas_ta_quant.technical_analysis.indicators.time |
| ta_sinusoidal_week_day | pandas_ta_quant.technical_analysis.indicators.time |
| ta_slope | pandas_ta_quant.technical_analysis.indicators.single_object |
| ta_sma | pandas_ta_quant.technical_analysis.filters |
| ta_sma_price_ratio | pandas_ta_quant.technical_analysis.normalizer |
| ta_sortino_ratio | pandas_ta_quant.technical_analysis.indicators.single_object |
| ta_sparse_covariance | pandas_ta_quant.technical_analysis.covariances |
| ta_std_ret_bands | pandas_ta_quant.technical_analysis.bands |
| ta_std_ret_bands_indicator | pandas_ta_quant.technical_analysis.indicators.single_object |
| ta_stddev | pandas_ta_quant.technical_analysis.indicators.single_object |
| ta_tr | pandas_ta_quant.technical_analysis.indicators.multi_object |
| ta_trend_lines | pandas_ta_quant.technical_analysis.forecast.support |
| ta_trix | pandas_ta_quant.technical_analysis.indicators.single_object |
| ta_ultimate_osc | pandas_ta_quant.technical_analysis.indicators.multi_object |
| ta_up_down_volatility_ratio | pandas_ta_quant.technical_analysis.indicators.single_object |
| ta_volume_as_time | pandas_ta_quant.technical_analysis.encoders.volume |
| ta_wilders | pandas_ta_quant.technical_analysis.filters |
| ta_williams_R | pandas_ta_quant.technical_analysis.indicators.multi_object |
| ta_z_norm | pandas_ta_quant.technical_analysis.normalizer |
| ta_zscore | pandas_ta_quant.technical_analysis.indicators.single_object |
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
pandas-ta-quant-0.2.7.tar.gz
(38.5 kB
view hashes)
