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trading-platform-ml-engine-v2/scripts/run_visualization_v2.py
rckrdmrd 75c4d07690 feat: Initial commit - ML Engine codebase 
Hierarchical ML Pipeline for trading predictions:
- Level 0: Attention Models (volatility/flow classification)
- Level 1: Base Models (XGBoost per symbol/timeframe)
- Level 2: Metamodels (XGBoost Stacking + Neural Gating)

Key components:
- src/pipelines/hierarchical_pipeline.py - Main prediction pipeline
- src/models/ - All ML model classes
- src/training/ - Training utilities
- src/api/ - FastAPI endpoints
- scripts/ - Training and evaluation scripts
- config/ - YAML configurations

Note: Trained models (*.joblib, *.pt) are gitignored.
      Regenerate with training scripts.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-18 04:27:40 -06:00

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#!/usr/bin/env python3
"""
ML Models Visualization Script V2
=================================
Visualizes predictions from reduced-features models for all symbols and timeframes.
Models Visualized:
1. RangePredictor (high/low) - Reduced features models
2. Volatility Attention Weights - Shows where model focuses
Supports:
- Multiple symbols: XAUUSD, EURUSD, BTCUSD
- Multiple timeframes: 5m, 15m
- Date range filtering
- Out-of-sample visualization (2025 data)
Usage:
python scripts/run_visualization_v2.py --symbol XAUUSD --timeframe 15m --start 2025-01-01 --end 2025-01-31
python scripts/run_visualization_v2.py --symbol XAUUSD --timeframe 5m --start 2025-01-01 --end 2025-01-31
python scripts/run_visualization_v2.py --all-symbols --all-timeframes
Author: ML-Specialist (NEXUS v4.0)
Version: 2.0.0
Created: 2026-01-05
"""
import sys
import os
# Add src to path
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', 'src'))
import numpy as np
import pandas as pd
from pathlib import Path
from datetime import datetime, timedelta
import argparse
from typing import Dict, List, Optional, Tuple, Any
import json
from loguru import logger
import joblib
# Local imports
from config.reduced_features import (
COLUMNS_TO_TRAIN,
generate_reduced_features,
get_feature_columns_without_ohlcv
)
from models.volatility_attention import (
compute_factor_median_range,
compute_move_multiplier,
weight_smooth,
compute_attention_weights,
VolatilityAttentionConfig
)
# Visualization libraries
try:
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib.patches import Rectangle, Patch
from matplotlib.lines import Line2D
HAS_MATPLOTLIB = True
except ImportError:
HAS_MATPLOTLIB = False
logger.warning("matplotlib not available - install with: pip install matplotlib")
try:
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import plotly.express as px
HAS_PLOTLY = True
except ImportError:
HAS_PLOTLY = False
logger.warning("plotly not available - install with: pip install plotly kaleido")
# ==============================================================================
# Constants
# ==============================================================================
SUPPORTED_SYMBOLS = ['XAUUSD', 'EURUSD', 'BTCUSD']
SUPPORTED_TIMEFRAMES = ['5m', '15m']
SYMBOL_CONFIGS = {
'XAUUSD': {'db_prefix': 'C:', 'base_price': 2650, 'pip_value': 0.01},
'EURUSD': {'db_prefix': 'C:', 'base_price': 1.10, 'pip_value': 0.0001},
'BTCUSD': {'db_prefix': 'X:', 'base_price': 95000, 'pip_value': 0.01}
}
HORIZONS = {'5m': 3, '15m': 3}
# ==============================================================================
# Data Loading
# ==============================================================================
def load_data_for_visualization(
symbol: str,
timeframe: str,
start_date: str,
end_date: str,
db_config_path: str = 'config/database.yaml'
) -> pd.DataFrame:
"""
Load data for visualization from database or sample.
Args:
symbol: Trading symbol
timeframe: Timeframe
start_date: Start date
end_date: End date
db_config_path: Database config path
Returns:
DataFrame with OHLCV data
"""
try:
from data.database import MySQLConnection
db = MySQLConnection(db_config_path)
config = SYMBOL_CONFIGS.get(symbol, {'db_prefix': 'C:'})
db_symbol = f"{config['db_prefix']}{symbol}"
query = """
SELECT
date_agg as time,
open, high, low, close, volume
FROM tickers_agg_data
WHERE ticker = :symbol
AND date_agg >= :start_date
AND date_agg <= :end_date
ORDER BY date_agg ASC
"""
params = {
'symbol': db_symbol,
'start_date': start_date,
'end_date': end_date
}
df = db.execute_query(query, params)
if df.empty:
logger.warning(f"No data found for {symbol} in {start_date} to {end_date}")
return create_sample_visualization_data(symbol, timeframe, start_date, end_date)
df['time'] = pd.to_datetime(df['time'])
df.set_index('time', inplace=True)
df = df.sort_index()
# Resample if needed
if timeframe != '5m':
tf_map = {'15m': '15min', '30m': '30min', '1H': '1H'}
offset = tf_map.get(timeframe, timeframe)
df = df.resample(offset).agg({
'open': 'first',
'high': 'max',
'low': 'min',
'close': 'last',
'volume': 'sum'
}).dropna()
logger.info(f"Loaded {len(df)} records for {symbol} {timeframe}")
return df
except Exception as e:
logger.warning(f"Database load failed: {e}")
return create_sample_visualization_data(symbol, timeframe, start_date, end_date)
def create_sample_visualization_data(
symbol: str,
timeframe: str,
start_date: str,
end_date: str
) -> pd.DataFrame:
"""Create sample data for demonstration."""
logger.info(f"Creating sample visualization data for {symbol} {timeframe}...")
np.random.seed(42)
tf_map = {'5m': '5min', '15m': '15min', '30m': '30min', '1H': '1H'}
freq = tf_map.get(timeframe, '15min')
dates = pd.date_range(start=start_date, end=end_date, freq=freq)
n = len(dates)
config = SYMBOL_CONFIGS.get(symbol, {'base_price': 100})
base_price = config.get('base_price', 100)
# Generate realistic price movement
returns = np.random.randn(n) * 0.001
price = base_price * np.exp(np.cumsum(returns))
# Vary volatility by session
volatility = np.where(
(dates.hour >= 13) & (dates.hour < 16),
0.003, # High volatility during overlap
0.001 # Normal volatility
)
df = pd.DataFrame({
'open': price * (1 + np.random.randn(n) * volatility),
'high': price * (1 + np.abs(np.random.randn(n)) * volatility * 2),
'low': price * (1 - np.abs(np.random.randn(n)) * volatility * 2),
'close': price * (1 + np.random.randn(n) * volatility),
'volume': np.random.randint(1000, 50000, n)
}, index=dates)
# Ensure OHLC consistency
df['high'] = df[['open', 'high', 'close']].max(axis=1)
df['low'] = df[['open', 'low', 'close']].min(axis=1)
return df
# ==============================================================================
# Model Loading and Prediction
# ==============================================================================
def load_reduced_features_models(
symbol: str,
timeframe: str,
model_dir: str = 'models/reduced_features_models'
) -> Dict[str, Any]:
"""
Load reduced features models for a symbol/timeframe.
Args:
symbol: Trading symbol
timeframe: Timeframe
model_dir: Model directory
Returns:
Dictionary with models and metadata
"""
model_path = Path(model_dir)
if not model_path.exists():
logger.warning(f"Model directory not found: {model_path}")
return {}
horizon = HORIZONS.get(timeframe, 3)
key_high = f"{symbol}_{timeframe}_high_h{horizon}"
key_low = f"{symbol}_{timeframe}_low_h{horizon}"
models = {}
# Try to load models
for key in [key_high, key_low]:
model_file = model_path / f"{key}.joblib"
if model_file.exists():
models[key] = joblib.load(model_file)
logger.info(f"Loaded model: {key}")
else:
logger.warning(f"Model not found: {model_file}")
# Load metadata
metadata_file = model_path / 'metadata.joblib'
if metadata_file.exists():
models['metadata'] = joblib.load(metadata_file)
return models
def predict_with_models(
df: pd.DataFrame,
models: Dict[str, Any],
symbol: str,
timeframe: str
) -> Dict[str, np.ndarray]:
"""
Generate predictions using loaded models.
Args:
df: DataFrame with OHLCV data
models: Loaded models dictionary
symbol: Trading symbol
timeframe: Timeframe
Returns:
Dictionary with predictions
"""
predictions = {}
# Generate features
features = generate_reduced_features(df)
feature_cols = get_feature_columns_without_ohlcv()
available_cols = [c for c in feature_cols if c in features.columns]
if not available_cols:
logger.warning("No feature columns available for prediction")
return predictions
X = features[available_cols].values
horizon = HORIZONS.get(timeframe, 3)
key_high = f"{symbol}_{timeframe}_high_h{horizon}"
key_low = f"{symbol}_{timeframe}_low_h{horizon}"
if key_high in models:
predictions['delta_high'] = models[key_high].predict(X)
logger.info(f"Generated {len(predictions['delta_high'])} high predictions")
if key_low in models:
predictions['delta_low'] = models[key_low].predict(X)
logger.info(f"Generated {len(predictions['delta_low'])} low predictions")
# Compute volatility attention weights
try:
config = VolatilityAttentionConfig(factor_window=100, w_max=3.0, beta=4.0)
predictions['attention_weights'] = compute_attention_weights(df, config)
logger.info("Computed attention weights")
except Exception as e:
logger.warning(f"Could not compute attention weights: {e}")
return predictions
# ==============================================================================
# Visualization Functions
# ==============================================================================
def create_visualization(
df: pd.DataFrame,
predictions: Dict[str, np.ndarray],
symbol: str,
timeframe: str,
output_path: Path,
start_date: str,
end_date: str,
output_format: str = 'both'
):
"""
Create visualizations for model predictions.
Args:
df: OHLCV DataFrame
predictions: Model predictions
symbol: Trading symbol
timeframe: Timeframe
output_path: Output directory
start_date: Start date
end_date: End date
output_format: 'matplotlib', 'plotly', or 'both'
"""
date_str = f"{start_date.replace('-', '')}_{end_date.replace('-', '')}"
if output_format in ['matplotlib', 'both'] and HAS_MATPLOTLIB:
create_matplotlib_visualization(
df, predictions, symbol, timeframe, output_path, date_str
)
if output_format in ['plotly', 'both'] and HAS_PLOTLY:
create_plotly_visualization(
df, predictions, symbol, timeframe, output_path, date_str
)
def create_matplotlib_visualization(
df: pd.DataFrame,
predictions: Dict,
symbol: str,
timeframe: str,
output_path: Path,
date_str: str
):
"""Create matplotlib visualization."""
fig, axes = plt.subplots(3, 1, figsize=(16, 12), sharex=True,
gridspec_kw={'height_ratios': [3, 1, 1]})
fig.suptitle(f'{symbol} - {timeframe} Reduced Features Model Predictions\n'
f'Period: {date_str.replace("_", " to ")}', fontsize=14)
# ---- Subplot 1: Price with Predictions ----
ax1 = axes[0]
# Plot candlesticks
for idx, (time, row) in enumerate(df.iterrows()):
color = 'green' if row['close'] >= row['open'] else 'red'
ax1.add_patch(Rectangle(
(mdates.date2num(time) - 0.0002, min(row['open'], row['close'])),
0.0004, abs(row['close'] - row['open']) or 0.1,
facecolor=color, edgecolor=color, alpha=0.8
))
ax1.plot([mdates.date2num(time), mdates.date2num(time)],
[row['low'], row['high']], color=color, linewidth=0.5)
# Plot predictions
if 'delta_high' in predictions and 'delta_low' in predictions:
close_prices = df['close'].values
n_preds = min(len(predictions['delta_high']), len(df))
upper_band = close_prices[:n_preds] + predictions['delta_high'][:n_preds]
lower_band = close_prices[:n_preds] - predictions['delta_low'][:n_preds]
ax1.fill_between(df.index[:n_preds], lower_band, upper_band,
alpha=0.2, color='blue', label='Predicted Range')
ax1.plot(df.index[:n_preds], upper_band, 'b--', linewidth=0.8, alpha=0.7)
ax1.plot(df.index[:n_preds], lower_band, 'b--', linewidth=0.8, alpha=0.7)
# Plot actual high/low
ax1.plot(df.index, df['high'], 'g-', linewidth=0.5, alpha=0.5, label='Actual High')
ax1.plot(df.index, df['low'], 'r-', linewidth=0.5, alpha=0.5, label='Actual Low')
ax1.set_ylabel('Price')
ax1.legend(loc='upper left')
ax1.grid(True, alpha=0.3)
# ---- Subplot 2: Attention Weights ----
ax2 = axes[1]
if 'attention_weights' in predictions:
n = min(len(predictions['attention_weights']), len(df))
weights = predictions['attention_weights'][:n]
colors = ['green' if w > 1.5 else 'orange' if w > 1 else 'gray' for w in weights]
ax2.bar(df.index[:n], weights, width=0.0005, color=colors, alpha=0.7)
ax2.axhline(y=1.5, color='green', linestyle='--', linewidth=1, label='High Attention')
ax2.axhline(y=1.0, color='black', linestyle='-', linewidth=0.5)
legend_elements = [
Patch(facecolor='green', alpha=0.7, label='High Attention (>1.5)'),
Patch(facecolor='orange', alpha=0.7, label='Moderate (1-1.5)'),
Patch(facecolor='gray', alpha=0.7, label='Low (<1)')
]
ax2.legend(handles=legend_elements, loc='upper right', fontsize=8)
else:
ax2.text(0.5, 0.5, 'Attention weights not available',
transform=ax2.transAxes, ha='center', va='center')
ax2.set_ylabel('Attention Weight')
ax2.set_ylim(0, 4)
ax2.grid(True, alpha=0.3)
# ---- Subplot 3: Prediction Errors ----
ax3 = axes[2]
if 'delta_high' in predictions and 'delta_low' in predictions:
close_prices = df['close'].values
n_preds = min(len(predictions['delta_high']), len(df) - 3)
# Compute actual deltas (shifted by horizon)
horizon = HORIZONS.get(timeframe, 3)
actual_high = np.zeros(n_preds)
actual_low = np.zeros(n_preds)
for i in range(n_preds - horizon):
future_slice = slice(i+1, i+1+horizon)
actual_high[i] = df['high'].iloc[future_slice].max() - close_prices[i]
actual_low[i] = close_prices[i] - df['low'].iloc[future_slice].min()
# Plot errors
error_high = predictions['delta_high'][:n_preds] - actual_high
error_low = predictions['delta_low'][:n_preds] - actual_low
ax3.fill_between(df.index[:n_preds], error_high, alpha=0.5, color='blue', label='High Error')
ax3.fill_between(df.index[:n_preds], -error_low, alpha=0.5, color='red', label='Low Error')
ax3.axhline(y=0, color='black', linestyle='-', linewidth=0.5)
ax3.set_ylabel('Prediction Error')
ax3.set_xlabel('Time')
ax3.legend(loc='upper right')
ax3.grid(True, alpha=0.3)
# Format x-axis
ax3.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d %H:%M'))
plt.xticks(rotation=45)
plt.tight_layout()
# Save
output_file = output_path / f"{symbol}_{timeframe}_predictions_{date_str}.png"
plt.savefig(output_file, dpi=150, bbox_inches='tight')
logger.info(f"Saved matplotlib chart to {output_file}")
plt.close(fig)
def create_plotly_visualization(
df: pd.DataFrame,
predictions: Dict,
symbol: str,
timeframe: str,
output_path: Path,
date_str: str
):
"""Create plotly interactive visualization."""
fig = make_subplots(
rows=3, cols=1,
shared_xaxes=True,
vertical_spacing=0.05,
row_heights=[0.5, 0.25, 0.25],
subplot_titles=(
f'{symbol} - {timeframe} Price & Predictions',
'Volatility Attention Weights',
'Prediction Analysis'
)
)
# ---- Row 1: Candlestick with Predictions ----
fig.add_trace(
go.Candlestick(
x=df.index,
open=df['open'],
high=df['high'],
low=df['low'],
close=df['close'],
name='OHLC',
increasing_line_color='green',
decreasing_line_color='red'
),
row=1, col=1
)
if 'delta_high' in predictions and 'delta_low' in predictions:
close_prices = df['close'].values
n_preds = min(len(predictions['delta_high']), len(df))
upper_band = close_prices[:n_preds] + predictions['delta_high'][:n_preds]
lower_band = close_prices[:n_preds] - predictions['delta_low'][:n_preds]
fig.add_trace(
go.Scatter(
x=df.index[:n_preds], y=upper_band,
mode='lines', name='Predicted High',
line=dict(color='blue', dash='dash', width=1),
opacity=0.7
),
row=1, col=1
)
fig.add_trace(
go.Scatter(
x=df.index[:n_preds], y=lower_band,
mode='lines', name='Predicted Low',
line=dict(color='blue', dash='dash', width=1),
fill='tonexty', fillcolor='rgba(0,0,255,0.1)',
opacity=0.7
),
row=1, col=1
)
# ---- Row 2: Attention Weights ----
if 'attention_weights' in predictions:
n = min(len(predictions['attention_weights']), len(df))
weights = predictions['attention_weights'][:n]
colors = ['green' if w > 1.5 else 'orange' if w > 1 else 'gray' for w in weights]
fig.add_trace(
go.Bar(
x=df.index[:n],
y=weights,
marker_color=colors,
name='Attention Weight',
opacity=0.7
),
row=2, col=1
)
fig.add_hline(y=1.5, line_dash="dash", line_color="green", row=2, col=1)
fig.add_hline(y=1.0, line_color="black", line_width=0.5, row=2, col=1)
# ---- Row 3: Prediction Statistics ----
if 'delta_high' in predictions and 'delta_low' in predictions:
n_preds = min(len(predictions['delta_high']), len(df))
# Asymmetry ratio
asymmetry = predictions['delta_high'][:n_preds] / (predictions['delta_low'][:n_preds] + 1e-10)
colors = ['green' if a > 1.2 else 'red' if a < 0.8 else 'gray' for a in asymmetry]
fig.add_trace(
go.Bar(
x=df.index[:n_preds],
y=asymmetry,
marker_color=colors,
name='High/Low Asymmetry',
opacity=0.7
),
row=3, col=1
)
fig.add_hline(y=1.2, line_dash="dash", line_color="green", row=3, col=1)
fig.add_hline(y=0.8, line_dash="dash", line_color="red", row=3, col=1)
fig.add_hline(y=1.0, line_color="black", line_width=0.5, row=3, col=1)
# Update layout
fig.update_layout(
title=f'{symbol} - {timeframe} Reduced Features Model Analysis',
height=1000,
showlegend=True,
xaxis_rangeslider_visible=False,
template='plotly_white'
)
fig.update_yaxes(title_text="Price", row=1, col=1)
fig.update_yaxes(title_text="Attention", range=[0, 4], row=2, col=1)
fig.update_yaxes(title_text="Asymmetry", row=3, col=1)
fig.update_xaxes(title_text="Time", row=3, col=1)
# Save HTML
output_file = output_path / f"{symbol}_{timeframe}_predictions_{date_str}.html"
fig.write_html(str(output_file))
logger.info(f"Saved plotly chart to {output_file}")
# Try to save PNG
try:
png_file = output_path / f"{symbol}_{timeframe}_predictions_{date_str}_plotly.png"
fig.write_image(str(png_file), width=1600, height=1000)
logger.info(f"Saved PNG chart to {png_file}")
except Exception as e:
logger.warning(f"Could not save PNG: {e}")
# ==============================================================================
# Main Function
# ==============================================================================
def run_visualization(
symbol: str,
timeframe: str,
start_date: str,
end_date: str,
model_dir: str = 'models/reduced_features_models',
output_dir: str = 'charts',
output_format: str = 'both',
db_config_path: str = 'config/database.yaml'
) -> Dict:
"""
Run visualization for a symbol/timeframe.
Args:
symbol: Trading symbol
timeframe: Timeframe
start_date: Start date
end_date: End date
model_dir: Model directory
output_dir: Output directory
output_format: Output format
db_config_path: Database config path
Returns:
Summary dictionary
"""
logger.info("=" * 60)
logger.info("REDUCED FEATURES MODEL VISUALIZATION V2")
logger.info(f"Symbol: {symbol}")
logger.info(f"Timeframe: {timeframe}")
logger.info(f"Period: {start_date} to {end_date}")
logger.info("=" * 60)
# Create output directory
output_path = Path(output_dir) / symbol / timeframe
output_path.mkdir(parents=True, exist_ok=True)
# Load data
df = load_data_for_visualization(
symbol, timeframe, start_date, end_date, db_config_path
)
if df.empty:
logger.error("No data available for visualization")
return {'error': 'No data'}
logger.info(f"Loaded {len(df)} records")
# Load models
models = load_reduced_features_models(symbol, timeframe, model_dir)
if not models:
logger.warning("No models loaded - using sample predictions")
# Generate sample predictions for demo
predictions = {
'delta_high': np.random.uniform(0, 10, len(df)),
'delta_low': np.random.uniform(0, 10, len(df))
}
else:
# Generate predictions
predictions = predict_with_models(df, models, symbol, timeframe)
# Create visualizations
create_visualization(
df, predictions, symbol, timeframe,
output_path, start_date, end_date, output_format
)
# Summary
summary = {
'symbol': symbol,
'timeframe': timeframe,
'period': {'start': start_date, 'end': end_date},
'data_points': len(df),
'models_loaded': list(models.keys()) if models else [],
'predictions_generated': list(predictions.keys()),
'output_path': str(output_path)
}
# Save summary
summary_file = output_path / f"summary_{start_date}_{end_date}.json"
with open(summary_file, 'w') as f:
json.dump(summary, f, indent=2, default=str)
logger.info(f"\nVisualization complete! Charts saved to {output_path}")
return summary
# ==============================================================================
# CLI Entry Point
# ==============================================================================
def main():
parser = argparse.ArgumentParser(
description='Visualize reduced-features model predictions',
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
Examples:
# Visualize XAUUSD 5m for January 2025
python scripts/run_visualization_v2.py --symbol XAUUSD --timeframe 5m --start 2025-01-01 --end 2025-01-31
# Visualize XAUUSD 15m for January 2025
python scripts/run_visualization_v2.py --symbol XAUUSD --timeframe 15m --start 2025-01-01 --end 2025-01-31
# All symbols and timeframes
python scripts/run_visualization_v2.py --all-symbols --all-timeframes
# Only matplotlib output
python scripts/run_visualization_v2.py --symbol XAUUSD --format matplotlib
"""
)
parser.add_argument('--symbol', default='XAUUSD',
help='Trading symbol (default: XAUUSD)')
parser.add_argument('--timeframe', default='15m',
help='Timeframe: 5m or 15m (default: 15m)')
parser.add_argument('--start', default='2025-01-01',
help='Start date YYYY-MM-DD (default: 2025-01-01)')
parser.add_argument('--end', default='2025-01-31',
help='End date YYYY-MM-DD (default: 2025-01-31)')
parser.add_argument('--format', default='both',
choices=['matplotlib', 'plotly', 'both'],
help='Output format (default: both)')
parser.add_argument('--model-dir', default='models/reduced_features_models',
help='Model directory')
parser.add_argument('--output-dir', default='charts',
help='Output directory for charts')
parser.add_argument('--all-symbols', action='store_true',
help='Run for all symbols')
parser.add_argument('--all-timeframes', action='store_true',
help='Run for all timeframes')
parser.add_argument('--db-config', default='config/database.yaml',
help='Database config file')
args = parser.parse_args()
# Determine symbols and timeframes
symbols = SUPPORTED_SYMBOLS if args.all_symbols else [args.symbol]
timeframes = SUPPORTED_TIMEFRAMES if args.all_timeframes else [args.timeframe]
# Change to script directory
script_dir = Path(__file__).parent.parent
os.chdir(script_dir)
# Run visualizations
results = []
for symbol in symbols:
for timeframe in timeframes:
logger.info(f"\nProcessing {symbol} {timeframe}...")
try:
result = run_visualization(
symbol=symbol,
timeframe=timeframe,
start_date=args.start,
end_date=args.end,
model_dir=args.model_dir,
output_dir=args.output_dir,
output_format=args.format,
db_config_path=args.db_config
)
results.append(result)
except Exception as e:
logger.error(f"Failed: {e}")
import traceback
traceback.print_exc()
# Final summary
print("\n" + "=" * 60)
print("VISUALIZATION SUMMARY")
print("=" * 60)
for r in results:
if 'error' not in r:
print(f" {r['symbol']} {r['timeframe']}: {r['data_points']} points -> {r['output_path']}")
print("=" * 60)
if __name__ == "__main__":
main()
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