mirror of
https://github.com/OpenBB-finance/OpenBB.git
synced 2026-11-04 08:22:26 +00:00
Add capability of looping neural network models for more confidence
This commit is contained in:
didier
@@ -22,7 +22,7 @@ from gamestonk_terminal.helper_funcs import (
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)
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from gamestonk_terminal.prediction_techniques.pred_helper import (
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print_pretty_prediction,
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print_pretty_prediction_nn,
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price_prediction_backtesting_color,
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print_prediction_kpis,
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)
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@@ -223,6 +223,14 @@ def _parse_args(prog, description, l_args):
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"false: TensorFlow will allocate 100%% of GPU memory. \n"
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"default: usually the same as false, uses env/TensorFlow default",
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)
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parser.add_argument(
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"--loops",
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action="store",
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dest="n_loops",
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type=check_positive,
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default=1,
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help="number of loops to iterate and train models",
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)
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ns_parser = parse_known_args_and_warn(parser, l_args)
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if not ns_parser:
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@@ -328,7 +336,7 @@ def _preprocess_split(df_stock, ns_parser):
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return scaler, stock_train_data, stock_x, stock_y
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def _rescale_data(df_stock, ns_parser, scaler, yhat):
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def _rescale_data(df_stock, ns_parser, scaler, yhat, idx_loop):
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"""Re-scale the data back and return the prediction dataframe. """
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if (ns_parser.s_preprocessing == "standardization") or (
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ns_parser.s_preprocessing == "normalization"
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@@ -341,7 +349,8 @@ def _rescale_data(df_stock, ns_parser, scaler, yhat):
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last_stock_day=df_stock["5. adjusted close"].index[-1],
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n_next_days=ns_parser.n_days,
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)
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df_pred = pd.Series(y_pred_test_t[0].tolist(), index=l_pred_days, name="Price")
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column_name = f"Price [{idx_loop+1}]"
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df_pred = pd.Series(y_pred_test_t[0].tolist(), index=l_pred_days, name=column_name)
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return df_pred
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@@ -354,26 +363,95 @@ def _plot_and_print_results(
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plt.plot(df_stock.index, df_stock["5. adjusted close"], lw=3)
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# BACKTESTING
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if ns_parser.s_end_date:
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plt.title(
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f"BACKTESTING: {model_name} on {s_ticker} - {ns_parser.n_days} days prediction"
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)
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if ns_parser.n_loops == 1:
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if ns_parser.s_end_date:
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plt.title(
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f"BACKTESTING: {model_name} on {s_ticker} - {ns_parser.n_days} days prediction"
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)
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else:
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plt.title(
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f"{model_name} on {s_ticker} - {ns_parser.n_days} days prediction"
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)
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else:
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plt.title(f"{model_name} on {s_ticker} - {ns_parser.n_days} days prediction")
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if ns_parser.s_end_date:
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plt.title(
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f"{ns_parser.n_loops} loops - BACKTESTING: {model_name} on {s_ticker} - {ns_parser.n_days} days prediction"
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)
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else:
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plt.title(
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f"{ns_parser.n_loops} loops - {model_name} on {s_ticker} - {ns_parser.n_days} days prediction"
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)
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plt.xlim(df_stock.index[0], get_next_stock_market_days(df_pred.index[-1], 1)[-1])
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plt.xlabel("Time")
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plt.ylabel("Share Price ($)")
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plt.grid(b=True, which="major", color="#666666", linestyle="-")
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plt.minorticks_on()
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plt.grid(b=True, which="minor", color="#999999", linestyle="-", alpha=0.2)
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plt.plot(
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[df_stock.index[-1], df_pred.index[0]],
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[df_stock["5. adjusted close"].values[-1], df_pred.values[0]],
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lw=1,
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c="tab:green",
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linestyle="--",
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)
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plt.plot(df_pred.index, df_pred, lw=2, c="tab:green")
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if ns_parser.n_loops == 1:
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plt.plot(
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[df_stock.index[-1], df_pred.index[0]],
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[
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df_stock["5. adjusted close"].values[-1],
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df_pred[df_pred.columns[0]].values[0],
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],
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lw=1,
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c="tab:green",
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linestyle="--",
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)
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plt.plot(df_pred.index, df_pred[df_pred.columns[0]], lw=2, c="tab:green")
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else:
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"""
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# Looks too noisy to output all loops predictions
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for idx_loop in range(ns_parser.n_loops):
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plt.plot(
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[df_stock.index[-1], df_pred.index[0]],
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[
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df_stock["5. adjusted close"].values[-1],
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df_pred[df_pred.columns[idx_loop]].values[0],
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],
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lw=0.5,
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c="tab:green",
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linestyle="--",
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)
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plt.plot(
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df_pred.index,
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df_pred[df_pred.columns[idx_loop]],
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lw=1,
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ls="--",
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c="tab:green",
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)
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"""
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df_quantiles = pd.DataFrame()
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df_quantiles["Quantile 10%"] = df_pred.quantile(0.1, axis=1)
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df_quantiles["Median"] = df_pred.quantile(0.5, axis=1)
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df_quantiles["Quantile 90%"] = df_pred.quantile(0.9, axis=1)
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plt.plot(df_pred.index, df_quantiles["Median"], lw=2, c="tab:green")
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plt.fill_between(
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df_pred.index,
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df_quantiles["Quantile 10%"],
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df_quantiles["Quantile 90%"],
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alpha=0.30,
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color="tab:green",
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interpolate=True,
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)
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plt.fill_between(
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[df_stock.index[-1], df_pred.index[0]],
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[
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df_stock["5. adjusted close"].values[-1],
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df_quantiles["Quantile 10%"].values[0],
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],
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[
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df_stock["5. adjusted close"].values[-1],
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df_quantiles["Quantile 90%"].values[0],
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],
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alpha=0.30,
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color="tab:green",
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interpolate=True,
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)
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plt.axvspan(
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df_stock.index[-1], df_pred.index[-1], facecolor="tab:orange", alpha=0.2
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)
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@@ -424,7 +502,13 @@ def _plot_and_print_results(
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c="tab:blue",
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ls="--",
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)
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plt.plot(df_pred.index, df_pred, lw=2, c="green")
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if ns_parser.n_loops == 1:
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plt.plot(df_pred.index, df_pred, lw=2, c="green")
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else:
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plt.plot(
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df_quantiles["Median"].index, df_quantiles["Median"], lw=2, c="green"
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)
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plt.scatter(df_future.index, df_future["5. adjusted close"], c="tab:blue", lw=3)
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plt.plot(
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[df_stock.index[-1], df_future.index[0]],
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@@ -436,14 +520,30 @@ def _plot_and_print_results(
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c="tab:blue",
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ls="--",
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)
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plt.scatter(df_pred.index, df_pred, c="green", lw=3)
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plt.plot(
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[df_stock.index[-1], df_pred.index[0]],
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[df_stock["5. adjusted close"].values[-1], df_pred.values[0]],
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lw=2,
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c="green",
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ls="--",
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)
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if ns_parser.n_loops == 1:
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plt.scatter(df_pred.index, df_pred, c="green", lw=3)
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plt.plot(
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[df_stock.index[-1], df_pred.index[0]],
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[df_stock["5. adjusted close"].values[-1], df_pred.values[0]],
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lw=2,
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c="green",
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ls="--",
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)
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else:
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plt.scatter(
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df_quantiles["Median"].index, df_quantiles["Median"], lw=3, c="green"
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)
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plt.plot(
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[df_stock.index[-1], df_quantiles["Median"].index[0]],
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[
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df_stock["5. adjusted close"].values[-1],
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df_quantiles["Median"].values[0],
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],
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lw=2,
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c="green",
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ls="--",
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)
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plt.title("BACKTESTING: Real data price versus Prediction")
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plt.xlim(df_stock.index[-1], df_pred.index[-1] + datetime.timedelta(days=1))
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plt.xticks(
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@@ -459,35 +559,84 @@ def _plot_and_print_results(
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plt.subplot(212)
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plt.axhline(y=0, color="k", linestyle="--", linewidth=2)
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plt.plot(
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df_future.index,
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100
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* (df_pred.values - df_future["5. adjusted close"].values)
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/ df_future["5. adjusted close"].values,
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lw=2,
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c="red",
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)
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plt.scatter(
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df_future.index,
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100
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* (df_pred.values - df_future["5. adjusted close"].values)
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/ df_future["5. adjusted close"].values,
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c="red",
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lw=5,
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)
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plt.title("BACKTESTING: Error between Real data and Prediction [%]")
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plt.plot(
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[df_stock.index[-1], df_future.index[0]],
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[
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0,
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if ns_parser.n_loops == 1:
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plt.plot(
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df_future.index,
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100
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* (df_pred.values[0] - df_future["5. adjusted close"].values[0])
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/ df_future["5. adjusted close"].values[0],
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],
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lw=2,
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ls="--",
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c="red",
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)
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* (
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df_pred[df_pred.columns[0]].values
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- df_future["5. adjusted close"].values
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)
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/ df_future["5. adjusted close"].values,
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lw=2,
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c="red",
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)
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plt.scatter(
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df_future.index,
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100
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* (
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df_pred[df_pred.columns[0]].values
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- df_future["5. adjusted close"].values
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)
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/ df_future["5. adjusted close"].values,
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c="red",
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lw=5,
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)
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plt.plot(
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[df_stock.index[-1], df_future.index[0]],
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[
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0,
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100
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* (
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df_pred[df_pred.columns[0]].values[0]
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- df_future["5. adjusted close"].values[0]
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)
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/ df_future["5. adjusted close"].values[0],
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],
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lw=2,
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ls="--",
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c="red",
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)
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else:
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plt.plot(
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df_future.index,
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100
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* (
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df_quantiles["Median"].values
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- df_future["5. adjusted close"].values
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)
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/ df_future["5. adjusted close"].values,
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lw=2,
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c="red",
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)
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plt.scatter(
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df_future.index,
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100
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* (
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df_quantiles["Median"].values
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- df_future["5. adjusted close"].values
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)
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/ df_future["5. adjusted close"].values,
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c="red",
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lw=5,
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)
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plt.plot(
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[df_stock.index[-1], df_future.index[0]],
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[
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0,
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100
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* (
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df_quantiles["Median"].values[0]
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- df_future["5. adjusted close"].values[0]
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)
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/ df_future["5. adjusted close"].values[0],
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],
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lw=2,
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ls="--",
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c="red",
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)
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plt.title("BACKTESTING: Error between Real data and Prediction [%]")
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plt.xlim(df_stock.index[-1], df_pred.index[-1] + datetime.timedelta(days=1))
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plt.xticks(
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[df_stock.index[-1], df_pred.index[-1] + datetime.timedelta(days=1)],
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@@ -506,8 +655,13 @@ def _plot_and_print_results(
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plt.show()
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# Refactor prediction dataframe for backtesting print
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df_pred.name = "Prediction"
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df_pred = df_pred.to_frame()
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if ns_parser.n_loops == 1:
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df_pred.rename(columns={df_pred.columns[0]: "Prediction"}, inplace=True)
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else:
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df_pred = pd.DataFrame()
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df_pred["Prediction"] = df_quantiles["Median"]
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df_pred["Real"] = df_future["5. adjusted close"]
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if gtff.USE_COLOR:
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@@ -520,11 +674,18 @@ def _plot_and_print_results(
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print("")
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print_prediction_kpis(df_pred["Real"].values, df_pred["Prediction"].values)
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print("")
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else:
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patch_pandas_text_adjustment()
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# Print prediction data
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print_pretty_prediction(df_pred, df_stock["5. adjusted close"].values[-1])
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print("")
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print_pretty_prediction_nn(df_pred, df_stock["5. adjusted close"].values[-1])
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print("")
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if ns_parser.n_loops > 1:
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print("Prediction Stats:")
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print(df_quantiles.round(2).to_string())
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print("")
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def mlp(l_args, s_ticker, df_stock):
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@@ -551,27 +712,38 @@ def mlp(l_args, s_ticker, df_stock):
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)
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model.compile(optimizer=ns_parser.s_optimizer, loss=ns_parser.s_loss)
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# Train our model
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model.fit(
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stock_x,
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stock_y,
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epochs=ns_parser.n_epochs,
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batch_size=ns_parser.n_batch_size,
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verbose=1,
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)
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print("")
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for idx_loop in range(ns_parser.n_loops):
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# Train our model
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model.fit(
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stock_x,
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stock_y,
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epochs=ns_parser.n_epochs,
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batch_size=ns_parser.n_batch_size,
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verbose=1,
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)
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print("")
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print(model.summary())
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print("")
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print(model.summary())
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print("")
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# Prediction
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yhat = model.predict(
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stock_train_data[-ns_parser.n_inputs :].reshape(1, ns_parser.n_inputs),
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verbose=0,
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)
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# Prediction
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yhat = model.predict(
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stock_train_data[-ns_parser.n_inputs :].reshape(1, ns_parser.n_inputs),
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verbose=0,
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)
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if idx_loop == 0:
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# Re-scale the data back, plot, and print the results
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df_pred = _rescale_data(
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df_stock, ns_parser, scaler, yhat, idx_loop
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).to_frame()
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else:
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df_pred = df_pred.join(
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_rescale_data(
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df_stock, ns_parser, scaler, yhat, idx_loop
|
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).to_frame()
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)
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# Re-scale the data back, plot, and print the results
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df_pred = _rescale_data(df_stock, ns_parser, scaler, yhat)
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_plot_and_print_results(
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df_stock, ns_parser, df_future, df_pred, "MLP", s_ticker
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)
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@@ -609,27 +781,40 @@ def rnn(l_args, s_ticker, df_stock):
|
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)
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model.compile(optimizer=ns_parser.s_optimizer, loss=ns_parser.s_loss)
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|
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# Train our model
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model.fit(
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stock_x,
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stock_y,
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epochs=ns_parser.n_epochs,
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batch_size=ns_parser.n_batch_size,
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verbose=1,
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)
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print("")
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for idx_loop in range(ns_parser.n_loops):
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# Train our model
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model.fit(
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stock_x,
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stock_y,
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epochs=ns_parser.n_epochs,
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batch_size=ns_parser.n_batch_size,
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verbose=1,
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)
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print("")
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print(model.summary())
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print("")
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print(model.summary())
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print("")
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# Prediction
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yhat = model.predict(
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stock_train_data[-ns_parser.n_inputs :].reshape(1, ns_parser.n_inputs, 1),
|
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verbose=0,
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||||
)
|
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# Prediction
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yhat = model.predict(
|
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stock_train_data[-ns_parser.n_inputs :].reshape(
|
||||
1, ns_parser.n_inputs, 1
|
||||
),
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||||
verbose=0,
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||||
)
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||||
|
||||
if idx_loop == 0:
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# Re-scale the data back, plot, and print the results
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||||
df_pred = _rescale_data(
|
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df_stock, ns_parser, scaler, yhat, idx_loop
|
||||
).to_frame()
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||||
else:
|
||||
df_pred = df_pred.join(
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||||
_rescale_data(
|
||||
df_stock, ns_parser, scaler, yhat, idx_loop
|
||||
).to_frame()
|
||||
)
|
||||
|
||||
# Re-scale the data back, plot, and print the results
|
||||
df_pred = _rescale_data(df_stock, ns_parser, scaler, yhat)
|
||||
_plot_and_print_results(
|
||||
df_stock, ns_parser, df_future, df_pred, "RNN", s_ticker
|
||||
)
|
||||
@@ -667,27 +852,40 @@ def lstm(l_args, s_ticker, df_stock):
|
||||
)
|
||||
model.compile(optimizer=ns_parser.s_optimizer, loss=ns_parser.s_loss)
|
||||
|
||||
# Train our model
|
||||
model.fit(
|
||||
stock_x,
|
||||
stock_y,
|
||||
epochs=ns_parser.n_epochs,
|
||||
batch_size=ns_parser.n_batch_size,
|
||||
verbose=1,
|
||||
)
|
||||
print("")
|
||||
for idx_loop in range(ns_parser.n_loops):
|
||||
# Train our model
|
||||
model.fit(
|
||||
stock_x,
|
||||
stock_y,
|
||||
epochs=ns_parser.n_epochs,
|
||||
batch_size=ns_parser.n_batch_size,
|
||||
verbose=1,
|
||||
)
|
||||
print("")
|
||||
|
||||
print(model.summary())
|
||||
print("")
|
||||
print(model.summary())
|
||||
print("")
|
||||
|
||||
# Prediction
|
||||
yhat = model.predict(
|
||||
stock_train_data[-ns_parser.n_inputs :].reshape(1, ns_parser.n_inputs, 1),
|
||||
verbose=0,
|
||||
)
|
||||
# Prediction
|
||||
yhat = model.predict(
|
||||
stock_train_data[-ns_parser.n_inputs :].reshape(
|
||||
1, ns_parser.n_inputs, 1
|
||||
),
|
||||
verbose=0,
|
||||
)
|
||||
|
||||
if idx_loop == 0:
|
||||
# Re-scale the data back, plot, and print the results
|
||||
df_pred = _rescale_data(
|
||||
df_stock, ns_parser, scaler, yhat, idx_loop
|
||||
).to_frame()
|
||||
else:
|
||||
df_pred = df_pred.join(
|
||||
_rescale_data(
|
||||
df_stock, ns_parser, scaler, yhat, idx_loop
|
||||
).to_frame()
|
||||
)
|
||||
|
||||
# Re-scale the data back, plot, and print the results
|
||||
df_pred = _rescale_data(df_stock, ns_parser, scaler, yhat)
|
||||
_plot_and_print_results(
|
||||
df_stock, ns_parser, df_future, df_pred, "LSTM", s_ticker
|
||||
)
|
||||
|
||||
@@ -28,6 +28,21 @@ def print_pretty_prediction(df_pred: pd.DataFrame, last_price: float):
|
||||
print(df_pred.to_string())
|
||||
|
||||
|
||||
def print_pretty_prediction_nn(df_pred: pd.DataFrame, last_price: float):
|
||||
if gtff.USE_COLOR:
|
||||
print(f"Actual price: {Fore.YELLOW}{last_price:.2f} ${Style.RESET_ALL}\n")
|
||||
print("Prediction:")
|
||||
print(
|
||||
df_pred.applymap(
|
||||
lambda x: price_prediction_color(x, last_val=last_price)
|
||||
).to_string()
|
||||
)
|
||||
else:
|
||||
print(f"Actual price: {last_price:.2f} $\n")
|
||||
print("Prediction:")
|
||||
print(df_pred.to_string())
|
||||
|
||||
|
||||
def mean_absolute_percentage_error(y_true, y_pred):
|
||||
y_true, y_pred = np.array(y_true), np.array(y_pred)
|
||||
return np.mean(np.abs((y_true - y_pred) / y_true)) * 100
|
||||
|
||||
Reference in New Issue
Block a user