注意：本篇为 DolphinDB 与 Python 函数库的不完全映射。如发现错误或需要补充相关内容，可以在下方评论或联系我们！联系方式可参考 DolphinDB技术支持攻略。
以下函数选取自 2.00 版本 用户手册。
| DolphinDB 函数 | Python 函数 |
|---|---|
| med | numpy.median |
| var | numpy.var(ddof=1) |
| varp | numpy.var |
| ewmVar | pandas.DataFrame.ewm.var |
| covar | pandas.Series.cov |
| ewmCov | pandas.DataFrame.ewm.cov |
| covarMatrix | numpy.cov |
| wcovar | numpy.cov(fweights) |
| std | numpy.std(ddof=1) |
| stdp | numpy.std |
| ewmStd | pandas.ewmstd |
| percentile | numpy.percentile / pandas.Series.percentile |
| percentileRank | scipy.stats.percentileofscore |
| quantile | numpy.quantile / pandas.Series.quantile |
| quantileSeries | numpy.quantile |
| corr | pandas.Series.corr |
| corrMatrix | numpy.corrcoef |
| ewmCorr | pandas.DataFrame.ewm.corr |
| max | pandas.DataFrame.max / pandas.Series.max / numpy.max |
| min | pandas.DataFrame.min / pandas.Series.min / numpy.min |
| mean | pandas.DataFrame.mean / pandas.Series.mean / numpy.mean |
| ewmMean | pandas.DataFrame.ewm.mean |
| avg | pandas.DataFrame.mean / pandas.Series.mean / numpy.mean |
| wavg | np.averge(weight) |
| acf | statsmodels.api.tsa.acf |
| autocorr | |
| isPeak | |
| isValley | |
| sum | pandas.DataFrame.sum / pandas.Series.sum / numpy.sum |
| sum2 | |
| sum3 | |
| sum4 | |
| contextSum | |
| contextSum2 | |
| sem | pandas.DataFrame.sem / pandas.Series.sem / scipy.stats.sem |
| mad (mean / median) | mean: pandas.DataFrame.mad / pandas.Series.mad |
| kurtosis | pandas.DataFrame.kurt(kurtosis) / pandas.Series.kurt(kurtosis) / scipy. stats.kurtosis |
| skew | pandas.DataFrame.skew / pandas.Series.kurt(skew) / scipy.stats.skew |
| beta(X, Y) | sklearn.linear_model.LinearRegression().fit(Y, X).coef_ |
| mutualInfo | sklearn.metrics.mutual_info_score |
| spearmanr(X, Y) | scipy.stats.spearmanr(X, Y)[0] |
| euclidean | scipy.spatial.distance.euclidean |
| tanimoto | textdistance |
| DolphinDB 函数 | Python 函数 |
|---|---|
| cdfBeta(a, b, X) | scipy.stats.beta.cdf(X, a, b) |
| cdfBinomial(trials, p, X) | scipy.stats.binom.cdf(X, trials, p) |
| cdfChiSquare(df, X) | scipy.stats.chi2.cdf(x, df) |
| cdfExp(mean, X) | scipy.stats.expon.cdf(x, scale=mean) |
| cdfF(dfn, dfd, X) | scipy.stats.f.cdf(X, dfn, dfd) |
| cdfGamma(shape, scale, X) | scipy.stats.gamma.cdf(X, shape, scale=scale) |
| cdfKolmogorov | |
| cdfLogistic(mean, scale, X) | scipy.stats.logistic.cdf(X, loc=mean,scale=scale) |
| cdfNormal(mean,stdev,X) | scipy.stats.norm.cdf(X, loc=mean, scale=stdev) |
| cdfPoisson(mean, X) | scipy.stats.poisson.cdf(X, mu=mean) |
| cdfStudent(df, X) | scipy.stats.t.cdf(X, df) |
| cdfUniform(lower, upper, X) | scipy.stats.uniform.cdf(X, loc=lower, scale=upper-lower) |
| cdfWeibull(alpha, beta, X) | scipy.stats.weibull_min.cdf(X, alpha, scale=beta) |
| cdfZipf(num, exponent, X) | scipy.stats.zipfian.cdf(X, exponent, num) |
| invBeta | scipy.stats.beta.ppf(X, a, b) |
| invBinomial | scipy.stats.binom.ppf(X, trials, p) |
| invChiSquare | scipy.stats.chi2.ppf(x, df) |
| invExp | scipy.stats.expon.ppf(x, scale=mean) |
| invF | scipy.stats.f.ppf(X, dfn, dfd) |
| invGamma | scipy.stats.gamma.ppf(X, shape, scale=scale) |
| invLogistic | scipy.stats.logistic.ppf(X, loc=mean,scale=scale) |
| invNormal | scipy.stats.norm.ppf(X, loc=mean, scale=stdev) |
| invPoisson | scipy.stats.poisson.ppf(X, mu=mean) |
| invStudent | scipy.stats.t.ppf(X, df) |
| invUniform | scipy.stats.uniform.ppf(X, loc=lower, scale=upper-lower) |
| invWeibull | scipy.stats.weibull_min.ppf(X, alpha, scale=beta) |
| randBeta | numpy.random.beta |
| randBinomial | numpy.random.binomial |
| randChiSquare | numpy.random.chisquare |
| randExp | numpy.random.exponential |
| randF | numpy.random.f |
| randGamma | numpy.random.gamma |
| randLogistic | numpy.random.logistic |
| randNormal | numpy.random.normal |
| randMultivariateNormal | numpy.random.multivariate_normal |
| randPoisson | numpy.random.poisson |
| randStudent | numpy.random.standard_t |
| rand | numpy.random.rand |
| randDiscrete | |
| randUniform | numpy.random.uniform |
| randWeibull | numpy.random.weibull |
| chiSquareTest | scipy.stats.chisquare |
| fTest | scipy.stats.f_oneway |
| zTest | statsmodels.stats.weightstats.ztest |
| tTest | scipy.stats.ttest_ind |
| ksTest | scipy.stats.ks_2samp |
| shapiroTest | scipy.stats.shapiro |
| mannWhitneyUTest | scipy.stats.mannwhitneyu |
| norm | np.random.normal |
| DolphinDB 函数 | Python 函数 |
|---|---|
| winsorize | scipy.stats.mstats.winsorize |
| resample | pandas.Series.resample / pandas.DataFrame.resample |
| spline | |
| neville | |
| dividedDifference | |
| loess | |
| copy | pandas.Series.copy / pandas.DataFrame.copy |
| stl | statsmodels.tsa.seasonal.STL |
| stat | pandas.Series.describe / pandas.DataFrame.describe 类似 |
| trueRange | talib.TRANGE |
| manova | statsmodels.multivariate.manova.MANOVA |
| anova | statsmodels.api.stats.anova_lm |
| zigzag | |
| zscore | scipy.stats.zscore(ddof=1) |
| crossStat | |
| adaBoostClassifier | sklearn.ensemble.AdaBoostClassifier |
| adaBoostRegressor | sklearn.ensemble.AdaBoostRegressor |
| randomForestClassifier | sklearn.ensemble.RandomForestClassifier |
| randomForestRegressor | sklearn.ensemble.RandomForestRegressor |
| gaussianNB | sklearn.naive_bayes.GaussianNB |
| multinomialNB | sklearn.naive_bayes.MultinomialNB |
| logisticRegression | sklearn.linear_model.LogisticRegression |
| glm | |
| gmm | sklearn.mixture.GaussianMixture |
| kmeans | sklearn.cluster.k_means |
| knn | sklearn.neighbors.KNeighborsClassifier |
| elasticNet | sklearn.linear_model.ElasticNet |
| lasso | sklearn.linear_model.Lasso |
| ridge | sklearn.linear_model.Ridge |
| linearTimeTrend | |
| pca | sklearn.decomposition.PCA |
| olsolsEx | statsmodels.regression.linear_model.OLS |
| wls | statsmodels.regression.linear_model.WLS |
| residual |
| DolphinDB 函数 | Python 函数 |
|---|---|
| all | all |
| any | any |
| hasNull | |
| isNothing | |
| isNull | pandas.DataFrame.isnull/pandas.DataFrame.isna |
| isValid | pandas.DataFrame.notnull/pandas.DataFrame.notna |
| isVoid | |
| in | in |
| between | pandas.Series.between |
| isSpace | Series.str.isspace |
| isAlNum | Series.str.isalnum |
| isAlpha | Series.str.isalpha |
| isNumeric | Series.str.isnumeric |
| isDecimal | Series.str.isdecimal |
| isDigit | Series.str.isdigit |
| isLower | Series.str.islower |
| isUpper | Series.str.isupper |
| isTitle | Series.str.istitle |
| startsWith | pandas.Series.str.startswith |
| endsWith | pandas.Series.str.endswith |
| regexFind | pandas.Series.str.find |
| isDuplicated | pandas.Series.duplicated /pandas.DataFrame.duplicated |
| isSorted | |
| isMonotonicIncreasing | pandas.Series.is_monotonic_decreasing |
| isMonotonicDecreasing | pandas.Series.is_monotonic_increasing |
| iif | |
| ifNull | |
| ifValid | |
| mask | pandas.DataFrame.mask / pandas.Series.mask |
| DolphinDB 函数 | Python 函数 |
|---|---|
| bfill/bfill! | pandas.DataFrame.bfill |
| ffill/ffill! | DataFrame.ffill |
| interpolate | DataFrame.interpolate |
| lfill/lfill! | DataFrame.interpolate(method='linear') |
| nullFill/nullFill! | DataFrame.fillna |
| fill! | obj[index]=value |
| DolphinDB 函数 | Python 函数 |
|---|---|
| sort/sort! | pandas.Series.sort_values |
| isort/isort! | numpy.argsort |
| isortTop | |
| rank | pandas.Series.rank/pandas.DataFrame.rank |
| denseRank | pandas.Series.rank(method='dense')/pandas.DataFrame.rank(method='dense') |
| DolphinDB 函数 | Python 函数 |
|---|---|
| ma | talib.MA |
| ema | talib.EMA |
| wma | talib.WMA |
| sma | talib.SMA |
| trima | talib.TRIMA |
| tema | talib.TEMA |
| dema | talib.DEMA |
| gema | |
| kama | talib.KAMA |
| wilder | |
| t3 | talib.T3 |
| linearTimeTrend | talib.LINEARREG_SLOPE / talib.LINEARREG_INTERCEPT |
以上列出的 TA-lib 函数为 DolphinDB 的内置函数。
更多 TA-lib 指标函数请参考 DolphinDB 的 ta 模块。