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Jun 11, 2023 •28 likes •388 views

DataCamp Machine Learning for Finance in Python MACHINE LEARNING FOR FINANCE IN PYTHON Machine learning for finance Nathan George Data Science Professor DataCamp Machine Learning for Finance in Python Machine Learning in Finance source:

  1. DataCamp Machine Learning for Finance in Python MACHINE LEARNING FOR FINANCE IN PYTHON Machine learning for finance Nathan George Data Science Professor

  2. DataCamp Machine Learning for Finance in Python Machine Learning in Finance source: https://www.zacks.com/stock/quote/AMD JPM report: http://valuesimplex.com/articles/JPM.pdf

  3. DataCamp Machine Learning for Finance in Python

  4. DataCamp Machine Learning for Finance in Python

  5. DataCamp Machine Learning for Finance in Python Understanding the data print(amd_df.head()) Adj_Close Adj_Volume Date 1999-03-10 8.690 4871800.0 1999-03-11 8.500 3566600.0 1999-03-12 8.250 4126800.0 1999-03-15 8.155 3006400.0 1999-03-16 8.500 3511400.0

  6. DataCamp Machine Learning for Finance in Python

  7. DataCamp Machine Learning for Finance in Python EDA plots amd_df['Adj_Close'].plot() plt.clf() # clears the plot area plt.show() vol = amd_df['Adj_Volume'] vol.plot.hist(bins=50) plt.show()

  8. DataCamp Machine Learning for Finance in Python Price changes amd_df['10d_close_pct'] = amd_df['Adj_Close'].pct_change(10) amd_df['10d_close_pct'].plot.hist(bins=50) plt.show()

  9. DataCamp Machine Learning for Finance in Python Shift data amd_df['10d_future_close'] = amd_df['Adj_Close'].shift(-10) amd_df['10d_future_close_pct'] = amd_df['10d_future_close'].pct_change(10)

  10. DataCamp Machine Learning for Finance in Python Correlations corr = amd_df.corr() print(corr) 10d_future_close_pct 10d_future_close 10d_close_pct \ 10d_future_close_pct 1.000000 0.070742 0.030402 10d_future_close 0.070742 1.000000 0.082828 10d_close_pct 0.030402 0.082828 1.000000 Adj_Close -0.083982 0.979345 0.073843 Adj_Volume -0.024456 -0.122473 0.044537 Adj_Close Adj_Volume 10d_future_close_pct -0.083982 -0.024456 10d_future_close 0.979345 -0.122473 10d_close_pct 0.073843 0.044537 Adj_Close 1.000000 -0.119437 Adj_Volume -0.119437 1.000000

  11. DataCamp Machine Learning for Finance in Python

  12. DataCamp Machine Learning for Finance in Python

  13. DataCamp Machine Learning for Finance in Python

  14. DataCamp Machine Learning for Finance in Python MACHINE LEARNING FOR FINANCE IN PYTHON Let's do some EDA!

  15. DataCamp Machine Learning for Finance in Python MACHINE LEARNING FOR FINANCE IN PYTHON Data transforms, features, and targets Nathan George Data Science Professor

  16. DataCamp Machine Learning for Finance in Python Making features and targets features = amd_df[['10d_close_pct', 'Adj_Volume']] targets = amd_df['10d_future_close_pct'] print(type(features)) pandas.core.series.DataFrame print(type(targets)) pandas.core.series.Series

  17. DataCamp Machine Learning for Finance in Python

  18. DataCamp Machine Learning for Finance in Python Moving averages Moving averages: use n past days to get average common values for n : 14, 50, 200

  19. DataCamp Machine Learning for Finance in Python

  20. DataCamp Machine Learning for Finance in Python

  21. DataCamp Machine Learning for Finance in Python RSI equation

  22. DataCamp Machine Learning for Finance in Python Calculating SMA and RSI import talib amd_df['ma200'] = talib.SMA(amd_df['Adj_Close'].values, timeperiod=200) amd_df['rsi200'] = talib.RSI(amd_df['Adj_Close'].values, timeperiod=200)

  23. DataCamp Machine Learning for Finance in Python Finally, our features feature_names = ['10d_close_pct', 'ma200', 'rsi200'] features = amd_df[feature_names] targets = amd_df['10d_future_close_pct'] feature_target_df = amd_df[feature_names + '10d_future_close_pct']

  24. DataCamp Machine Learning for Finance in Python Check correlations import seaborn as sns corr = feature_target_df.corr() sns.heatmap(corr, annot=True)

  25. DataCamp Machine Learning for Finance in Python

  26. DataCamp Machine Learning for Finance in Python MACHINE LEARNING FOR FINANCE IN PYTHON Let's create features and targets!

  27. DataCamp Machine Learning for Finance in Python MACHINE LEARNING FOR FINANCE IN PYTHON Linear modeling with financial data Nathan George Data Science Professor

  28. DataCamp Machine Learning for Finance in Python

  29. DataCamp Machine Learning for Finance in Python Make train and test sets import statsmodels.api as sm linear_features = sm.add_constant(features) train_size = int(0.85 * targets.shape[0]) train_features = linear_features[:train_size] train_targets = targets[:train_size] test_features = linear_features[train_size:] test_targets = targets[train_size:] some_list[start:stop:step]

  30. DataCamp Machine Learning for Finance in Python Linear modeling model = sm.OLS(train_targets, train_features) results = model.fit()

  31. DataCamp Machine Learning for Finance in Python Linear modeling print(results.summary())

  32. DataCamp Machine Learning for Finance in Python Linear modeling OLS Regression Results =========================================================================== Dep. Variable: 10d_future_pct R-squared: 0.157 Model: OLS Adj. R-squared: 0.146 Method: Least Squares F-statistic: 15.55 Date: Thu, 19 Apr 2018 Prob (F-statistic): 4.79e-14 Time: 11:41:05 Log-Likelihood: 336.53 No. Observations: 425 AIC: -661.1 Df Residuals: 419 BIC: -636.8 Df Model: 5 Covariance Type: nonrobust =========================================================================== coef std err t P>|t| [0.025 0.975] ---------------------------------------------------------------------------- const 1.3305 0.323 4.117 0.000 0.695 1.966 10d_close_pct 0.0906 0.098 0.927 0.355 -0.102 0.283 ma14 0.3313 0.209 1.585 0.114 -0.080 0.742 rsi14 -0.0013 0.001 -1.044 0.297 -0.004 0.001 ma200 -0.4090 0.053 -7.712 0.000 -0.513 -0.305 rsi200 -0.0224 0.003 -6.610 0.000 -0.029 -0.016 =========================================================================== Omnibus: 3.571 Durbin-Watson: 0.209 Prob(Omnibus): 0.168 Jarque-Bera (JB): 3.323 Skew: 0.202 Prob(JB): 0.190 Kurtosis: 3.159 Cond. No. 5.47e+03 ===========================================================================

  33. DataCamp Machine Learning for Finance in Python p-values print(results.pvalues) const 4.630428e-05 10d_close_pct 3.546748e-01 ma14 1.136941e-01 rsi14 2.968699e-01 ma200 9.126405e-14 rsi200 1.169324e-10

  34. DataCamp Machine Learning for Finance in Python

  35. DataCamp Machine Learning for Finance in Python

  36. DataCamp Machine Learning for Finance in Python MACHINE LEARNING FOR FINANCE IN PYTHON Time to fit a linear model!

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