text stringlengths 0 93.6k |
|---|
fontsize=8, |
alpha=0.7, |
visible=False, |
) |
annotations.append(ann) |
ax1.set_title(f"{symbol} Stock Price Prediction") |
ax1.set_xlabel("Date") |
ax1.set_ylabel("Price") |
ax1.legend() |
# Add hover annotation |
hover_annot = ax1.annotate( |
"", |
xy=(0, 0), |
xytext=(10, 10), |
textcoords="offset points", |
bbox=dict(boxstyle="round", fc="w"), |
arrowprops=dict(arrowstyle="->"), |
) |
hover_annot.set_visible(False) |
def update_hover_annot(event): |
vis = hover_annot.get_visible() |
if event.inaxes == ax1: |
x, y = event.xdata, event.ydata |
date = num2date(x).strftime("%Y-%m-%d") |
hover_annot.xy = (x, y) |
hover_annot.set_text(f"Date: {date}\nPrice: ${y:.2f}") |
hover_annot.set_visible(True) |
fig.canvas.draw_idle() |
elif vis: |
hover_annot.set_visible(False) |
fig.canvas.draw_idle() |
# Connect the hover event |
fig.canvas.mpl_connect("motion_notify_event", update_hover_annot) |
# Add zoom event handler |
def on_zoom(event): |
ax1 = event.inaxes |
if ax1 is None: |
return |
xlim = ax1.get_xlim() |
ylim = ax1.get_ylim() |
# Calculate the zoom level based on the x-axis range |
zoom_level = (plot_data.index[-1] - plot_data.index[0]).days / ( |
xlim[1] - xlim[0] |
).days |
# Adjust annotation visibility based on zoom level |
for ann in annotations: |
ann.set_visible( |
zoom_level > 5 |
) # Show annotations when zoomed in more than 5x |
fig.canvas.draw_idle() |
# Connect the zoom event handler |
fig.canvas.mpl_connect("motion_notify_event", on_zoom) |
# Model performance summary table |
ax2.axis("off") |
table = ax2.table( |
cellText=stats_df.values, |
colLabels=stats_df.columns, |
cellLoc="center", |
loc="center", |
) |
table.auto_set_font_size(False) |
table.set_fontsize(9) |
table.scale(1, 1.5) |
# Lower the title and add more space between plot and table |
ax2.set_title("Model Performance Summary", pad=60) |
# Implement trading strategy |
strategy_returns = implement_trading_strategy( |
plot_data["Close"].values, ensemble_predictions.flatten() |
) |
strategy_sharpe_ratio = ( |
np.mean(strategy_returns) / np.std(strategy_returns) * np.sqrt(252) |
) |
print(f"Trading Strategy Sharpe Ratio: {strategy_sharpe_ratio:.4f}") |
# Calculate cumulative returns of the trading strategy |
cumulative_returns = (1 + strategy_returns).cumprod() - 1 |
# Add new subplot for trading strategy performance |
ax3.plot( |
plot_data.index[-len(cumulative_returns) :], |
cumulative_returns, |
label="Strategy Cumulative Returns", |
color="purple", |
) |
ax3.set_title(f"{symbol} Trading Strategy Performance") |
ax3.set_xlabel("Date") |
ax3.set_ylabel("Cumulative Returns") |
ax3.legend() |
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