pandas-ta-quant-0.2.2.tar.gz

# 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 |