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Fix KNN using adjusted close data

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didier
2021-02-13 19:15:59 +00:00
parent 74f3d1fe64
commit f0572dc5bc
1 changed files with 5 additions and 5 deletions
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10
prediction_techniques/knn.py
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@@ -28,19 +28,19 @@ def k_nearest_neighbors(l_args, s_ticker, s_interval, df_stock):
return
# Split training data
stock_x, stock_y = splitTrain.split_train(df_stock['4. close'].values, ns_parser.n_inputs, ns_parser.n_days, ns_parser.n_jumps)
stock_x, stock_y = splitTrain.split_train(df_stock['5. adjusted close'].values, ns_parser.n_inputs, ns_parser.n_days, ns_parser.n_jumps)
# Machine Learning model
knn = neighbors.KNeighborsRegressor(n_neighbors=ns_parser.n_neighbors)
knn.fit(stock_x, stock_y)
# Prediction data
l_predictions = knn.predict(df_stock['4. close'].values[-ns_parser.n_inputs:].reshape(1, -1))[0]
l_pred_days = get_next_stock_market_days(last_stock_day=df_stock['4. close'].index[-1], n_next_days=ns_parser.n_days)
l_predictions = knn.predict(df_stock['5. adjusted close'].values[-ns_parser.n_inputs:].reshape(1, -1))[0]
l_pred_days = get_next_stock_market_days(last_stock_day=df_stock['5. adjusted close'].index[-1], n_next_days=ns_parser.n_days)
df_pred = pd.Series(l_predictions, index=l_pred_days, name='Price')
# Plotting
plt.plot(df_stock.index, df_stock['4. close'], lw=2)
plt.plot(df_stock.index, df_stock['5. adjusted close'], lw=2)
plt.title(f"{ns_parser.n_neighbors}-Nearest Neighbors on {s_ticker} - {ns_parser.n_days} days prediction")
plt.xlim(df_stock.index[0], get_next_stock_market_days(df_pred.index[-1], 1)[-1])
plt.xlabel('Time')
@@ -48,7 +48,7 @@ def k_nearest_neighbors(l_args, s_ticker, s_interval, df_stock):
plt.grid(b=True, which='major', color='#666666', linestyle='-')
plt.minorticks_on()
plt.grid(b=True, which='minor', color='#999999', linestyle='-', alpha=0.2)
plt.plot([df_stock.index[-1], df_pred.index[0]], [df_stock['4. close'].values[-1], df_pred.values[0]], lw=1, c='tab:green', linestyle='--')
plt.plot([df_stock.index[-1], df_pred.index[0]], [df_stock['5. adjusted close'].values[-1], df_pred.values[0]], lw=1, c='tab:green', linestyle='--')
plt.plot(df_pred.index, df_pred, lw=2, c='tab:green')
plt.axvspan(df_stock.index[-1], df_pred.index[-1], facecolor='tab:orange', alpha=0.2)
xmin, xmax, ymin, ymax = plt.axis()