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082a2835b6
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082a2835b6 | ||
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2f98463df8 |
@ -8,9 +8,12 @@ dependencies = [
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requirements.txt
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requirements.txt
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@ -200,10 +200,13 @@ dependencies = [
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{ name = "dash" },
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{ name = "gspread" },
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@ -212,10 +215,13 @@ requires-dist = [
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|
||||
def calc_trix(close):
|
||||
from ta.trend import TRIXIndicator
|
||||
trix = TRIXIndicator(close=close, window=15)
|
||||
return ('trix', trix.trix())
|
||||
|
||||
def calc_vortex(high, low, close):
|
||||
from ta.trend import VortexIndicator
|
||||
vortex = VortexIndicator(high=high, low=low, close=close, window=14)
|
||||
return [
|
||||
('vortex_pos', vortex.vortex_indicator_pos()),
|
||||
('vortex_neg', vortex.vortex_indicator_neg())
|
||||
]
|
||||
|
||||
def calc_kama(close):
|
||||
import pandas_ta as ta
|
||||
kama = ta.kama(close, length=10)
|
||||
return ('kama', kama)
|
||||
|
||||
def calc_force_index(close, volume):
|
||||
from ta.volume import ForceIndexIndicator
|
||||
fi = ForceIndexIndicator(close=close, volume=volume, window=13)
|
||||
return ('force_index', fi.force_index())
|
||||
|
||||
def calc_eom(high, low, volume):
|
||||
from ta.volume import EaseOfMovementIndicator
|
||||
eom = EaseOfMovementIndicator(high=high, low=low, volume=volume, window=14)
|
||||
return ('eom', eom.ease_of_movement())
|
||||
|
||||
def calc_mfi(high, low, close, volume):
|
||||
from ta.volume import MFIIndicator
|
||||
mfi = MFIIndicator(high=high, low=low, close=close, volume=volume, window=14)
|
||||
return ('mfi', mfi.money_flow_index())
|
||||
|
||||
def calc_adi(high, low, close, volume):
|
||||
from ta.volume import AccDistIndexIndicator
|
||||
adi = AccDistIndexIndicator(high=high, low=low, close=close, volume=volume)
|
||||
return ('adi', adi.acc_dist_index())
|
||||
|
||||
def calc_tema(close):
|
||||
import pandas_ta as ta
|
||||
tema = ta.tema(close, length=10)
|
||||
return ('tema', tema)
|
||||
|
||||
def calc_stochrsi(close):
|
||||
from ta.momentum import StochRSIIndicator
|
||||
stochrsi = StochRSIIndicator(close=close, window=14, smooth1=3, smooth2=3)
|
||||
return [
|
||||
('stochrsi', stochrsi.stochrsi()),
|
||||
('stochrsi_k', stochrsi.stochrsi_k()),
|
||||
('stochrsi_d', stochrsi.stochrsi_d())
|
||||
]
|
||||
|
||||
def calc_awesome_oscillator(high, low):
|
||||
from ta.momentum import AwesomeOscillatorIndicator
|
||||
ao = AwesomeOscillatorIndicator(high=high, low=low, window1=5, window2=34)
|
||||
return ('awesome_osc', ao.awesome_oscillator())
|
||||
|
||||
if __name__ == '__main__':
|
||||
IMPUTE_NANS = True # Set to True to impute NaNs, False to drop rows with NaNs
|
||||
csv_path = './data/btcusd_1-min_data.csv'
|
||||
csv_prefix = os.path.splitext(os.path.basename(csv_path))[0]
|
||||
|
||||
@ -516,8 +580,7 @@ if __name__ == '__main__':
|
||||
np.save(sub_feature_file, values.values)
|
||||
print(f'Saved feature: {sub_feature_file}')
|
||||
|
||||
# Prepare jobs for lags, rolling stats, log returns, and volatility
|
||||
feature_jobs = []
|
||||
# Prepare lags, rolling stats, log returns, and volatility features sequentially
|
||||
# Lags
|
||||
for col in ohlcv_cols:
|
||||
for lag in range(1, lags + 1):
|
||||
@ -527,8 +590,11 @@ if __name__ == '__main__':
|
||||
print(f'C Loading cached feature: {feature_file}')
|
||||
features_dict[feature_name] = np.load(feature_file)
|
||||
else:
|
||||
print(f'Adding lag feature job: {feature_name}')
|
||||
feature_jobs.append((feature_name, compute_lag, col, lag))
|
||||
print(f'Computing lag feature: {feature_name}')
|
||||
result = compute_lag(df, col, lag)
|
||||
features_dict[feature_name] = result
|
||||
np.save(feature_file, result.values)
|
||||
print(f'Saved feature: {feature_file}')
|
||||
# Rolling statistics
|
||||
for col in ohlcv_cols:
|
||||
for window in window_sizes:
|
||||
@ -547,8 +613,11 @@ if __name__ == '__main__':
|
||||
print(f'D Loading cached feature: {feature_file}')
|
||||
features_dict[feature_name] = np.load(feature_file)
|
||||
else:
|
||||
print(f'Adding rolling stat feature job: {feature_name}')
|
||||
feature_jobs.append((feature_name, compute_rolling, col, stat, window))
|
||||
print(f'Computing rolling stat feature: {feature_name}')
|
||||
result = compute_rolling(df, col, stat, window)
|
||||
features_dict[feature_name] = result
|
||||
np.save(feature_file, result.values)
|
||||
print(f'Saved feature: {feature_file}')
|
||||
# Log returns for different horizons
|
||||
for horizon in [5, 15, 30]:
|
||||
feature_name = f'log_return_{horizon}'
|
||||
@ -557,8 +626,11 @@ if __name__ == '__main__':
|
||||
print(f'E Loading cached feature: {feature_file}')
|
||||
features_dict[feature_name] = np.load(feature_file)
|
||||
else:
|
||||
print(f'Adding log return feature job: {feature_name}')
|
||||
feature_jobs.append((feature_name, compute_log_return, horizon))
|
||||
print(f'Computing log return feature: {feature_name}')
|
||||
result = compute_log_return(df, horizon)
|
||||
features_dict[feature_name] = result
|
||||
np.save(feature_file, result.values)
|
||||
print(f'Saved feature: {feature_file}')
|
||||
# Volatility
|
||||
for window in window_sizes:
|
||||
feature_name = f'volatility_{window}'
|
||||
@ -567,22 +639,61 @@ if __name__ == '__main__':
|
||||
print(f'F Loading cached feature: {feature_file}')
|
||||
features_dict[feature_name] = np.load(feature_file)
|
||||
else:
|
||||
print(f'Adding volatility feature job: {feature_name}')
|
||||
feature_jobs.append((feature_name, compute_volatility, window))
|
||||
|
||||
# Sequential computation for all non-cached features
|
||||
if feature_jobs:
|
||||
print(f'Computing {len(feature_jobs)} features sequentially...')
|
||||
for job in feature_jobs:
|
||||
print(f'Computing feature job: {job[0]}')
|
||||
feature_name, result = run_feature_job(job, df)
|
||||
print(f'Computing volatility feature: {feature_name}')
|
||||
result = compute_volatility(df, window)
|
||||
features_dict[feature_name] = result
|
||||
feature_file = f'./data/{csv_prefix}_{feature_name}.npy'
|
||||
np.save(feature_file, result.values)
|
||||
print(f'Saved computed feature: {feature_file}')
|
||||
print('All features computed.')
|
||||
print(f'Saved feature: {feature_file}')
|
||||
|
||||
# --- Additional Technical Indicator Features ---
|
||||
# ADX
|
||||
adx_names = ['adx', 'adx_pos', 'adx_neg']
|
||||
adx_files = [f'./data/{csv_prefix}_{name}.npy' for name in adx_names]
|
||||
if all(os.path.exists(f) for f in adx_files):
|
||||
print('G Loading cached features: ADX')
|
||||
for name, f in zip(adx_names, adx_files):
|
||||
arr = np.load(f)
|
||||
features_dict[name] = pd.Series(arr, index=df.index)
|
||||
else:
|
||||
print('All features loaded from cache.')
|
||||
print('Calculating multi-column indicator: adx')
|
||||
result = calc_adx(df['High'], df['Low'], df['Close'])
|
||||
for subname, values in result:
|
||||
sub_feature_file = f'./data/{csv_prefix}_{subname}.npy'
|
||||
features_dict[subname] = values
|
||||
np.save(sub_feature_file, values.values)
|
||||
print(f'Saved feature: {sub_feature_file}')
|
||||
|
||||
# Force Index
|
||||
feature_file = f'./data/{csv_prefix}_force_index.npy'
|
||||
if os.path.exists(feature_file):
|
||||
print(f'K Loading cached feature: {feature_file}')
|
||||
arr = np.load(feature_file)
|
||||
features_dict['force_index'] = pd.Series(arr, index=df.index)
|
||||
else:
|
||||
print('Calculating feature: force_index')
|
||||
_, values = calc_force_index(df['Close'], df['Volume'])
|
||||
features_dict['force_index'] = values
|
||||
np.save(feature_file, values.values)
|
||||
print(f'Saved feature: {feature_file}')
|
||||
|
||||
# Supertrend indicators
|
||||
for period, multiplier in [(12, 3.0), (10, 1.0), (11, 2.0)]:
|
||||
st_name = f'supertrend_{period}_{multiplier}'
|
||||
st_trend_name = f'supertrend_trend_{period}_{multiplier}'
|
||||
st_file = f'./data/{csv_prefix}_{st_name}.npy'
|
||||
st_trend_file = f'./data/{csv_prefix}_{st_trend_name}.npy'
|
||||
if os.path.exists(st_file) and os.path.exists(st_trend_file):
|
||||
print(f'L Loading cached features: {st_file}, {st_trend_file}')
|
||||
features_dict[st_name] = pd.Series(np.load(st_file), index=df.index)
|
||||
features_dict[st_trend_name] = pd.Series(np.load(st_trend_file), index=df.index)
|
||||
else:
|
||||
print(f'Calculating Supertrend indicator: {st_name}')
|
||||
st = ta.supertrend(df['High'], df['Low'], df['Close'], length=period, multiplier=multiplier)
|
||||
features_dict[st_name] = st[f'SUPERT_{period}_{multiplier}']
|
||||
features_dict[st_trend_name] = st[f'SUPERTd_{period}_{multiplier}']
|
||||
np.save(st_file, features_dict[st_name].values)
|
||||
np.save(st_trend_file, features_dict[st_trend_name].values)
|
||||
print(f'Saved features: {st_file}, {st_trend_file}')
|
||||
|
||||
# Concatenate all new features at once
|
||||
print('Concatenating all new features to DataFrame...')
|
||||
@ -602,130 +713,94 @@ if __name__ == '__main__':
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# Drop intermediate features_df to free memory
|
||||
print('Dropping intermediate features_df to free memory...')
|
||||
del features_df
|
||||
import gc
|
||||
gc.collect()
|
||||
|
||||
feature_end_time = time.time()
|
||||
print(f'Feature computation completed in {feature_end_time - feature_start_time:.2f} seconds.')
|
||||
|
||||
# Add Supertrend indicators (custom)
|
||||
print('Preparing data for Supertrend calculation...')
|
||||
st_df = df.rename(columns={'High': 'high', 'Low': 'low', 'Close': 'close'})
|
||||
|
||||
print('Calculating Supertrend indicators...')
|
||||
supertrend = Supertrends(st_df)
|
||||
st_results = supertrend.calculate_supertrend_indicators()
|
||||
for idx, st in enumerate(st_results):
|
||||
period = st['params']['period']
|
||||
multiplier = st['params']['multiplier']
|
||||
# Skip useless supertrend features
|
||||
if (period == 10 and multiplier == 1.0) or (period == 11 and multiplier == 2.0):
|
||||
continue
|
||||
print(f'Adding Supertrend features: supertrend_{period}_{multiplier} and supertrend_trend_{period}_{multiplier}')
|
||||
df[f'supertrend_{period}_{multiplier}'] = st['results']['supertrend']
|
||||
df[f'supertrend_trend_{period}_{multiplier}'] = st['results']['trend']
|
||||
|
||||
# Add time features (exclude 'dayofweek')
|
||||
print('Adding hour feature...')
|
||||
df['Timestamp'] = pd.to_datetime(df['Timestamp'], errors='coerce')
|
||||
df['hour'] = df['Timestamp'].dt.hour
|
||||
|
||||
# Drop NaNs after all feature engineering
|
||||
print('Dropping NaNs after feature engineering...')
|
||||
df = df.dropna().reset_index(drop=True)
|
||||
# Handle NaNs after all feature engineering
|
||||
if IMPUTE_NANS:
|
||||
print('Imputing NaNs after feature engineering (using mean imputation)...')
|
||||
numeric_cols = df.select_dtypes(include=[np.number]).columns
|
||||
for col in numeric_cols:
|
||||
df[col] = df[col].fillna(df[col].mean())
|
||||
# If you want to impute non-numeric columns differently, add logic here
|
||||
else:
|
||||
print('Dropping NaNs after feature engineering...')
|
||||
df = df.dropna().reset_index(drop=True)
|
||||
|
||||
# Exclude 'Timestamp', 'Close', 'log_return', and any future target columns from features
|
||||
print('Selecting feature columns...')
|
||||
exclude_cols = ['Timestamp', 'Close', 'log_return', 'log_return_5', 'log_return_15', 'log_return_30']
|
||||
feature_cols = [col for col in df.columns if col not in exclude_cols]
|
||||
print('Features used for training:', feature_cols)
|
||||
|
||||
# Print the features used for training
|
||||
print("Features used for training:", feature_cols)
|
||||
# Prepare CSV for results
|
||||
results_csv = './data/leave_one_out_results.csv'
|
||||
if not os.path.exists(results_csv):
|
||||
with open(results_csv, 'w', newline='') as f:
|
||||
writer = csv.writer(f)
|
||||
writer.writerow(['left_out_feature', 'used_features', 'rmse', 'mae', 'r2', 'mape', 'directional_accuracy'])
|
||||
|
||||
# Drop excluded columns to save memory
|
||||
print('Dropping excluded columns to save memory...')
|
||||
df = df[feature_cols + ['log_return', 'Timestamp']]
|
||||
total_features = len(feature_cols)
|
||||
never_leave_out = {'Open', 'High', 'Low', 'Close', 'Volume'}
|
||||
for idx, left_out in enumerate(feature_cols):
|
||||
if left_out in never_leave_out:
|
||||
continue
|
||||
used = [f for f in feature_cols if f != left_out]
|
||||
print(f'\n=== Leave-one-out {idx+1}/{total_features}: left out {left_out} ===')
|
||||
try:
|
||||
# Prepare X and y for this combination
|
||||
X = df[used].values.astype(np.float32)
|
||||
y = df["log_return"].values.astype(np.float32)
|
||||
split_idx = int(len(X) * 0.8)
|
||||
X_train, X_test = X[:split_idx], X[split_idx:]
|
||||
y_train, y_test = y[:split_idx], y[split_idx:]
|
||||
test_timestamps = df['Timestamp'].values[split_idx:]
|
||||
|
||||
print('Preparing X and y...')
|
||||
X = df[feature_cols].values.astype(np.float32)
|
||||
y = df['log_return'].values.astype(np.float32)
|
||||
|
||||
split_idx = int(len(X) * 0.8)
|
||||
print(f'Splitting data: {split_idx} train, {len(X) - split_idx} test')
|
||||
X_train, X_test = X[:split_idx], X[split_idx:]
|
||||
y_train, y_test = y[:split_idx], y[split_idx:]
|
||||
test_timestamps = df['Timestamp'].values[split_idx:]
|
||||
model = CustomXGBoostGPU(X_train, X_test, y_train, y_test)
|
||||
booster = model.train()
|
||||
model.save_model(f'./data/xgboost_model_wo_{left_out}.json')
|
||||
|
||||
print('Initializing model...')
|
||||
model = CustomXGBoostGPU(X_train, X_test, y_train, y_test)
|
||||
|
||||
print('Training model...')
|
||||
booster = model.train()
|
||||
|
||||
print('Training complete.')
|
||||
test_preds = model.predict(X_test)
|
||||
rmse = np.sqrt(mean_squared_error(y_test, test_preds))
|
||||
|
||||
# Save the trained model
|
||||
model.save_model('./data/xgboost_model.json')
|
||||
print('Model saved to ./data/xgboost_model.json')
|
||||
# Reconstruct price series from log returns
|
||||
if 'Close' in df.columns:
|
||||
close_prices = df['Close'].values
|
||||
else:
|
||||
close_prices = pd.read_csv(csv_path)['Close'].values
|
||||
start_price = close_prices[split_idx]
|
||||
actual_prices = [start_price]
|
||||
for r_ in y_test:
|
||||
actual_prices.append(actual_prices[-1] * np.exp(r_))
|
||||
actual_prices = np.array(actual_prices[1:])
|
||||
predicted_prices = [start_price]
|
||||
for r_ in test_preds:
|
||||
predicted_prices.append(predicted_prices[-1] * np.exp(r_))
|
||||
predicted_prices = np.array(predicted_prices[1:])
|
||||
|
||||
if hasattr(model, 'params'):
|
||||
print("Model hyperparameters:", model.params)
|
||||
if hasattr(model, 'model') and hasattr(model.model, 'get_score'):
|
||||
import operator
|
||||
importances = model.model.get_score(importance_type='weight')
|
||||
# Map f0, f1, ... to actual feature names
|
||||
feature_map = {f"f{idx}": name for idx, name in enumerate(feature_cols)}
|
||||
sorted_importances = sorted(importances.items(), key=operator.itemgetter(1), reverse=True)
|
||||
print('Feature importances (sorted, with names):')
|
||||
for feat, score in sorted_importances:
|
||||
print(f'{feature_map.get(feat, feat)}: {score}')
|
||||
mae = mean_absolute_error(actual_prices, predicted_prices)
|
||||
r2 = r2_score(actual_prices, predicted_prices)
|
||||
direction_actual = np.sign(np.diff(actual_prices))
|
||||
direction_pred = np.sign(np.diff(predicted_prices))
|
||||
directional_accuracy = (direction_actual == direction_pred).mean()
|
||||
mape = np.mean(np.abs((actual_prices - predicted_prices) / actual_prices)) * 100
|
||||
|
||||
print('Making predictions for first 5 test samples...')
|
||||
preds = model.predict(X_test[:5])
|
||||
print('Predictions for first 5 test samples:', preds)
|
||||
print('Actual values for first 5 test samples:', y_test[:5])
|
||||
# Save results to CSV
|
||||
with open(results_csv, 'a', newline='') as f:
|
||||
writer = csv.writer(f)
|
||||
writer.writerow([left_out, "|".join(used), rmse, mae, r2, mape, directional_accuracy])
|
||||
print(f'Left out {left_out}: RMSE={rmse:.4f}, MAE={mae:.4f}, R2={r2:.4f}, MAPE={mape:.2f}%, DirAcc={directional_accuracy*100:.2f}%')
|
||||
|
||||
print('Making predictions for all test samples...')
|
||||
test_preds = model.predict(X_test)
|
||||
rmse = np.sqrt(mean_squared_error(y_test, test_preds))
|
||||
print(f'RMSE on test set: {rmse:.4f}')
|
||||
# Plotting for this run
|
||||
plot_prefix = f'loo_{left_out}'
|
||||
print('Plotting distribution of absolute prediction errors...')
|
||||
plot_prediction_error_distribution(predicted_prices, actual_prices, prefix=plot_prefix)
|
||||
|
||||
print('Saving y_test and test_preds to disk...')
|
||||
np.save('./data/y_test.npy', y_test)
|
||||
np.save('./data/test_preds.npy', test_preds)
|
||||
|
||||
# Reconstruct price series from log returns
|
||||
print('Reconstructing price series from log returns...')
|
||||
# Get the last available Close price before the test set
|
||||
# The DataFrame df has been reset, so use split_idx to get the right row
|
||||
if 'Close' in df.columns:
|
||||
close_prices = df['Close'].values
|
||||
else:
|
||||
# Reload original CSV to get Close prices if not present
|
||||
close_prices = pd.read_csv(csv_path)['Close'].values
|
||||
start_price = close_prices[split_idx] # This is the price at the split point
|
||||
# Actual prices
|
||||
actual_prices = [start_price]
|
||||
for r in y_test:
|
||||
actual_prices.append(actual_prices[-1] * np.exp(r))
|
||||
actual_prices = np.array(actual_prices[1:])
|
||||
# Predicted prices
|
||||
predicted_prices = [start_price]
|
||||
for r in test_preds:
|
||||
predicted_prices.append(predicted_prices[-1] * np.exp(r))
|
||||
predicted_prices = np.array(predicted_prices[1:])
|
||||
|
||||
print('Plotting predicted vs actual prices...')
|
||||
plot_predicted_vs_actual_prices(actual_prices, predicted_prices, test_timestamps)
|
||||
|
||||
print("Final features used for training:", feature_cols)
|
||||
|
||||
print("Shape of X:", X.shape)
|
||||
print("First row of X:", X[0])
|
||||
print("stoch_k in feature_cols?", "stoch_k" in feature_cols)
|
||||
if "stoch_k" in feature_cols:
|
||||
idx = feature_cols.index("stoch_k")
|
||||
print("First 10 values of stoch_k:", X[:10, idx])
|
||||
print('Plotting directional accuracy...')
|
||||
plot_direction_transition_heatmap(actual_prices, predicted_prices, prefix=plot_prefix)
|
||||
except Exception as e:
|
||||
print(f'Leave-one-out failed for {left_out}: {e}')
|
||||
print(f'All leave-one-out runs completed. Results saved to {results_csv}')
|
||||
sys.exit(0)
|
||||
@ -24,7 +24,7 @@ def display_actual_vs_predicted(y_test, test_preds, timestamps, n_plot=200):
|
||||
hovermode='closest'
|
||||
)
|
||||
fig = go.Figure(data=data, layout=layout)
|
||||
pyo.plot(fig)
|
||||
pyo.plot(fig, auto_open=False)
|
||||
|
||||
def plot_target_distribution(y_train, y_test):
|
||||
import plotly.offline as pyo
|
||||
@ -50,7 +50,7 @@ def plot_target_distribution(y_train, y_test):
|
||||
barmode='overlay'
|
||||
)
|
||||
fig = go.Figure(data=data, layout=layout)
|
||||
pyo.plot(fig)
|
||||
pyo.plot(fig, auto_open=False)
|
||||
|
||||
def plot_predicted_vs_actual_log_returns(y_test, test_preds, timestamps=None, n_plot=200):
|
||||
import plotly.offline as pyo
|
||||
@ -78,7 +78,7 @@ def plot_predicted_vs_actual_log_returns(y_test, test_preds, timestamps=None, n_
|
||||
hovermode='closest'
|
||||
)
|
||||
fig_line = go.Figure(data=data_line, layout=layout_line)
|
||||
pyo.plot(fig_line, filename='log_return_line_plot.html')
|
||||
pyo.plot(fig_line, filename='charts/log_return_line_plot.html', auto_open=False)
|
||||
|
||||
# Scatter plot: Predicted vs Actual
|
||||
trace_scatter = go.Scatter(
|
||||
@ -108,7 +108,7 @@ def plot_predicted_vs_actual_log_returns(y_test, test_preds, timestamps=None, n_
|
||||
hovermode='closest'
|
||||
)
|
||||
fig_scatter = go.Figure(data=data_scatter, layout=layout_scatter)
|
||||
pyo.plot(fig_scatter, filename='log_return_scatter_plot.html')
|
||||
pyo.plot(fig_scatter, filename='charts/log_return_scatter_plot.html', auto_open=False)
|
||||
|
||||
def plot_predicted_vs_actual_prices(actual_prices, predicted_prices, timestamps=None, n_plot=200):
|
||||
import plotly.offline as pyo
|
||||
@ -136,7 +136,7 @@ def plot_predicted_vs_actual_prices(actual_prices, predicted_prices, timestamps=
|
||||
hovermode='closest'
|
||||
)
|
||||
fig_line = go.Figure(data=data_line, layout=layout_line)
|
||||
pyo.plot(fig_line, filename='price_line_plot.html')
|
||||
pyo.plot(fig_line, filename='charts/price_line_plot.html', auto_open=False)
|
||||
|
||||
# Scatter plot: Predicted vs Actual
|
||||
trace_scatter = go.Scatter(
|
||||
@ -166,4 +166,153 @@ def plot_predicted_vs_actual_prices(actual_prices, predicted_prices, timestamps=
|
||||
hovermode='closest'
|
||||
)
|
||||
fig_scatter = go.Figure(data=data_scatter, layout=layout_scatter)
|
||||
pyo.plot(fig_scatter, filename='price_scatter_plot.html')
|
||||
pyo.plot(fig_scatter, filename='charts/price_scatter_plot.html', auto_open=False)
|
||||
|
||||
def plot_prediction_error_distribution(predicted_prices, actual_prices, nbins=100, prefix=""):
|
||||
"""
|
||||
Plots the distribution of signed prediction errors between predicted and actual prices,
|
||||
coloring negative errors (under-prediction) and positive errors (over-prediction) differently.
|
||||
"""
|
||||
import plotly.offline as pyo
|
||||
import plotly.graph_objs as go
|
||||
errors = np.array(predicted_prices) - np.array(actual_prices)
|
||||
|
||||
# Separate negative and positive errors
|
||||
neg_errors = errors[errors < 0]
|
||||
pos_errors = errors[errors >= 0]
|
||||
|
||||
# Calculate common bin edges
|
||||
min_error = np.min(errors)
|
||||
max_error = np.max(errors)
|
||||
bin_edges = np.linspace(min_error, max_error, nbins + 1)
|
||||
xbins = dict(start=min_error, end=max_error, size=(max_error - min_error) / nbins)
|
||||
|
||||
trace_neg = go.Histogram(
|
||||
x=neg_errors,
|
||||
opacity=0.75,
|
||||
marker=dict(color='blue'),
|
||||
name='Negative Error (Under-prediction)',
|
||||
xbins=xbins
|
||||
)
|
||||
trace_pos = go.Histogram(
|
||||
x=pos_errors,
|
||||
opacity=0.75,
|
||||
marker=dict(color='orange'),
|
||||
name='Positive Error (Over-prediction)',
|
||||
xbins=xbins
|
||||
)
|
||||
layout = go.Layout(
|
||||
title='Distribution of Prediction Errors (Signed)',
|
||||
xaxis=dict(title='Prediction Error (Predicted - Actual)'),
|
||||
yaxis=dict(title='Frequency'),
|
||||
barmode='overlay',
|
||||
bargap=0.05
|
||||
)
|
||||
fig = go.Figure(data=[trace_neg, trace_pos], layout=layout)
|
||||
filename = f'charts/{prefix}_prediction_error_distribution.html'
|
||||
pyo.plot(fig, filename=filename, auto_open=False)
|
||||
|
||||
def plot_directional_accuracy(actual_prices, predicted_prices, timestamps=None, n_plot=200):
|
||||
"""
|
||||
Plots the directional accuracy of predictions compared to actual price movements.
|
||||
Shows whether the predicted direction matches the actual direction of price movement.
|
||||
|
||||
Args:
|
||||
actual_prices: Array of actual price values
|
||||
predicted_prices: Array of predicted price values
|
||||
timestamps: Optional array of timestamps for x-axis
|
||||
n_plot: Number of points to plot (default 200, plots last n_plot points)
|
||||
"""
|
||||
import plotly.graph_objs as go
|
||||
import plotly.offline as pyo
|
||||
import numpy as np
|
||||
|
||||
# Calculate price changes
|
||||
actual_changes = np.diff(actual_prices)
|
||||
predicted_changes = np.diff(predicted_prices)
|
||||
|
||||
# Determine if directions match
|
||||
actual_direction = np.sign(actual_changes)
|
||||
predicted_direction = np.sign(predicted_changes)
|
||||
correct_direction = actual_direction == predicted_direction
|
||||
|
||||
# Get last n_plot points
|
||||
actual_changes = actual_changes[-n_plot:]
|
||||
predicted_changes = predicted_changes[-n_plot:]
|
||||
correct_direction = correct_direction[-n_plot:]
|
||||
|
||||
if timestamps is not None:
|
||||
x_values = timestamps[1:] # Skip first since we took diff
|
||||
x_values = x_values[-n_plot:] # Get last n_plot points
|
||||
else:
|
||||
x_values = list(range(len(actual_changes)))
|
||||
|
||||
# Create traces for correct and incorrect predictions
|
||||
correct_trace = go.Scatter(
|
||||
x=np.array(x_values)[correct_direction],
|
||||
y=actual_changes[correct_direction],
|
||||
mode='markers',
|
||||
name='Correct Direction',
|
||||
marker=dict(color='green', size=8)
|
||||
)
|
||||
|
||||
incorrect_trace = go.Scatter(
|
||||
x=np.array(x_values)[~correct_direction],
|
||||
y=actual_changes[~correct_direction],
|
||||
mode='markers',
|
||||
name='Incorrect Direction',
|
||||
marker=dict(color='red', size=8)
|
||||
)
|
||||
|
||||
# Calculate accuracy percentage
|
||||
accuracy = np.mean(correct_direction) * 100
|
||||
|
||||
layout = go.Layout(
|
||||
title=f'Directional Accuracy (Overall: {accuracy:.1f}%)',
|
||||
xaxis=dict(title='Time' if timestamps is not None else 'Sample'),
|
||||
yaxis=dict(title='Price Change'),
|
||||
showlegend=True
|
||||
)
|
||||
|
||||
fig = go.Figure(data=[correct_trace, incorrect_trace], layout=layout)
|
||||
pyo.plot(fig, filename='charts/directional_accuracy.html', auto_open=False)
|
||||
|
||||
def plot_direction_transition_heatmap(actual_prices, predicted_prices, prefix=""):
|
||||
"""
|
||||
Plots a heatmap showing the frequency of each (actual, predicted) direction pair.
|
||||
"""
|
||||
import numpy as np
|
||||
import plotly.graph_objs as go
|
||||
import plotly.offline as pyo
|
||||
|
||||
# Calculate directions
|
||||
actual_direction = np.sign(np.diff(actual_prices))
|
||||
predicted_direction = np.sign(np.diff(predicted_prices))
|
||||
|
||||
# Build 3x3 matrix: rows=actual, cols=predicted, values=counts
|
||||
# Map -1 -> 0, 0 -> 1, 1 -> 2 for indexing
|
||||
mapping = {-1: 0, 0: 1, 1: 2}
|
||||
matrix = np.zeros((3, 3), dtype=int)
|
||||
for a, p in zip(actual_direction, predicted_direction):
|
||||
matrix[mapping[a], mapping[p]] += 1
|
||||
|
||||
# Axis labels
|
||||
directions = ['Down (-1)', 'No Change (0)', 'Up (+1)']
|
||||
|
||||
# Plot heatmap
|
||||
heatmap = go.Heatmap(
|
||||
z=matrix,
|
||||
x=directions, # predicted
|
||||
y=directions, # actual
|
||||
colorscale='Viridis',
|
||||
colorbar=dict(title='Count')
|
||||
)
|
||||
layout = go.Layout(
|
||||
title='Direction Prediction Transition Matrix',
|
||||
xaxis=dict(title='Predicted Direction'),
|
||||
yaxis=dict(title='Actual Direction')
|
||||
)
|
||||
fig = go.Figure(data=[heatmap], layout=layout)
|
||||
filename = f'charts/{prefix}_direction_transition_heatmap.html'
|
||||
pyo.plot(fig, filename=filename, auto_open=False)
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user