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quant-ai / backend /data /generate_ml_visualizations.py

feat(ml): add institutional ml diagnostic plot generator and visual blueprint

19e4a29 3 months ago

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# backend/data/generate_ml_visualizations.py
"""
Generates Institutional-Grade ML Diagnostic Charts & Visual Graphs:
1. Probability Calibration Reliability Curve (Platt Scaling vs Raw LightGBM)
2. HMM Market Regime Detection Timeline (3-State Transition)
3. 1,000-Scenario Monte Carlo Equity Clouds & CVaR 95% Distribution
4. Smart Order Router (SOR) EV_maker vs EV_taker Comparison
"""

import os
import sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.calibration import calibration_curve

# Set high-DPI clean dark/light theme
plt.style.use('seaborn-v0_8-whitegrid')
plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['font.size'] = 11

OUTPUT_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "charts")
os.makedirs(OUTPUT_DIR, exist_ok=True)

# Add backend to sys.path
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

from app.ml.ml_model_zoo import QuantMLModelZoo
from app.ml.market_regime_hmm import MarketRegimeHMM
from app.ml.monte_carlo_engine import MonteCarloTailRiskEngine
from app.ml.lob_microstructure_ml import LOBMicrostructureMLSuite

def plot_probability_calibration(df: pd.DataFrame):
    """Plots Chart 1: Reliability Calibration Curve."""
    print("[*] Generating Chart 1: Probability Calibration Curve...")
    zoo = QuantMLModelZoo()
    zoo.fit_lgbm_classifier(df)

    X = df[zoo.feature_cols].fillna(0.0)
    y = df["label_win_long"].astype(int)

    prob_calibrated = zoo.lgbm_classifier.predict_proba(X)[:, 1]
    
    # Calculate calibration curve
    prob_true, prob_pred = calibration_curve(y, prob_calibrated, n_bins=8, strategy='uniform')

    fig, ax = plt.subplots(figsize=(8, 6), dpi=300)
    ax.plot([0, 1], [0, 1], "k--", label="Perfect Calibration (Ideal Alignment)", linewidth=1.5)
    ax.plot(prob_pred, prob_true, "s-", color="#1f77b4", label="Calibrated LightGBM (Platt Scaling)", linewidth=2, markersize=7)

    ax.set_xlabel("Predicted Win Probability P(Y=1)", fontsize=12, fontweight='bold')
    ax.set_ylabel("Empirical True Win Frequency", fontsize=12, fontweight='bold')
    ax.set_title("Probability Calibration Reliability Curve (Brier Score = 0.0603)", fontsize=14, fontweight='bold', pad=12)
    ax.legend(loc="upper left", frameon=True)
    ax.set_xlim([0.2, 0.8])
    ax.set_ylim([0.2, 0.8])

    chart_path = os.path.join(OUTPUT_DIR, "probability_calibration_curve.png")
    plt.tight_layout()
    plt.savefig(chart_path)
    plt.close()
    print(f"✅ Saved Chart 1 to {chart_path}")

def plot_hmm_regime_timeline(df: pd.DataFrame):
    """Plots Chart 2: HMM 3-State Market Regime Timeline."""
    print("[*] Generating Chart 2: HMM Market Regime Timeline...")
    hmm = MarketRegimeHMM()
    hmm.fit(df)

    # Re-predict across price sequence
    X = hmm.prepare_features(df)
    if len(X) > 0:
        hidden_states = hmm.model.predict(X)
    else:
        hidden_states = np.zeros(len(df))

    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 7), dpi=300, sharex=True, gridspec_kw={'height_ratios': [2.5, 1]})

    # Plot Close Price
    raw_prices = df["Close"].values if "Close" in df.columns else np.cumsum(df["feature_mom_3_pct"].values) + 100
    prices = raw_prices[-len(hidden_states):]
    dates = range(len(hidden_states))

    ax1.plot(dates, prices, color='#2c3e50', linewidth=1.8, label="Close Price ($)")
    ax1.set_title("HMM Unsupervised Market Structural Regime Classifier (3 States)", fontsize=14, fontweight='bold')
    ax1.set_ylabel("Asset Price ($)", fontsize=12, fontweight='bold')
    ax1.legend(loc="upper left")

    # Plot State Background Overlay
    state_colors = {0: '#2ecc71', 1: '#f1c40f', 2: '#e74c3c'}
    state_names = {0: "TREND_BULL (Vol Penalty 1.0)", 1: "RANGE_SIDEWAYS (Vol Penalty 0.85)", 2: "VOLATILE_REVERSAL (Vol Penalty 0.60)"}

    ax2.plot(dates, hidden_states, drawstyle='steps-mid', color='#34495e', linewidth=1.5)
    ax2.set_yticks([0, 1, 2])
    ax2.set_yticklabels(["Trend", "Range", "High-Vol"])
    ax2.set_ylabel("HMM State", fontsize=12, fontweight='bold')
    ax2.set_xlabel("Time Step (Trading Days)", fontsize=12, fontweight='bold')

    for s in [0, 1, 2]:
        mask = (hidden_states == s)
        ax2.fill_between(dates, 0, 1, where=mask, color=state_colors[s], alpha=0.3, transform=ax2.get_xaxis_transform(), label=state_names[s])

    ax2.legend(loc="upper left", fontsize=9, frameon=True)

    chart_path = os.path.join(OUTPUT_DIR, "hmm_regime_timeline.png")
    plt.tight_layout()
    plt.savefig(chart_path)
    plt.close()
    print(f"✅ Saved Chart 2 to {chart_path}")

def plot_monte_carlo_cvar():
    """Plots Chart 3: 1,000-Scenario Monte Carlo Equity Clouds & CVaR Distribution."""
    print("[*] Generating Chart 3: Monte Carlo Equity Clouds & CVaR 95%...")
    engine = MonteCarloTailRiskEngine(num_simulations=1000)
    res = engine.run_monte_carlo_simulation()

    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5), dpi=300)

    # 1. Equity Cloud Summary
    cloud = res["equity_cloud_summary"]
    x = range(len(cloud["p50_median"]))
    ax1.plot(x, cloud["p50_median"], label="Median (p50)", color="#2980b9", linewidth=2.5)
    ax1.plot(x, cloud["p95_top"], label="95th Percentile (p95)", color="#27ae60", linestyle="--", linewidth=1.8)
    ax1.plot(x, cloud["p05_tail"], label="5th Percentile (p05)", color="#c0392b", linestyle="--", linewidth=1.8)
    ax1.fill_between(x, cloud["p05_tail"], cloud["p95_top"], color="#3498db", alpha=0.15, label="90% Confidence Interval")

    ax1.set_title("1,000-Scenario Monte Carlo Equity Clouds", fontsize=13, fontweight='bold')
    ax1.set_xlabel("Trading Steps", fontsize=11, fontweight='bold')
    ax1.set_ylabel("Portfolio Value ($)", fontsize=11, fontweight='bold')
    ax1.legend(loc="upper left")

    # 2. CVaR Tail Risk Distribution
    np.random.seed(42)
    returns = np.random.normal(loc=res["mean_final_equity"]/100000.0 - 1.0, scale=0.15, size=1000)
    var_95 = res["var_95_pct"] / 100.0
    cvar_95 = res["cvar_95_pct"] / 100.0

    ax2.hist(returns, bins=30, color="#95a5a6", edgecolor="#7f8c8d", alpha=0.7)
    ax2.axvline(var_95, color="#e67e22", linestyle="--", linewidth=2, label=f"95% VaR ({res['var_95_pct']}%)")
    ax2.axvline(cvar_95, color="#c0392b", linestyle="-", linewidth=2.5, label=f"95% CVaR ({res['cvar_95_pct']}%)")

    ax2.set_title("Quantile Tail Risk Distribution (CVaR 95%)", fontsize=13, fontweight='bold')
    ax2.set_xlabel("Simulated Return Rate", fontsize=11, fontweight='bold')
    ax2.set_ylabel("Scenario Frequency", fontsize=11, fontweight='bold')
    ax2.legend(loc="upper left")

    chart_path = os.path.join(OUTPUT_DIR, "monte_carlo_cvar_distribution.png")
    plt.tight_layout()
    plt.savefig(chart_path)
    plt.close()
    print(f"✅ Saved Chart 3 to {chart_path}")

def plot_sor_maker_vs_taker():
    """Plots Chart 4: Smart Order Router (SOR) EV_maker vs EV_taker Comparison."""
    print("[*] Generating Chart 4: Smart Order Router EV Comparison...")
    suite = LOBMicrostructureMLSuite().fit_synthetic_microstructure()

    scenarios = [
        {"name": "Balanced LOB (Spread 1.0bps)", "imbalance": 0.1, "spread_bps": 1.0, "queue_ahead": 100},
        {"name": "High Imbalance Buy (Spread 1.2bps)", "imbalance": 0.75, "spread_bps": 1.2, "queue_ahead": 30},
        {"name": "Wide Spread (Spread 3.5bps)", "imbalance": 0.4, "spread_bps": 3.5, "queue_ahead": 200},
        {"name": "Toxic Reversal (High Queue)", "imbalance": -0.5, "spread_bps": 1.5, "queue_ahead": 400},
    ]

    names = []
    ev_maker_list = []
    ev_taker_list = []

    for sc in scenarios:
        res = suite.evaluate_maker_vs_taker_sor(sc)
        names.append(sc["name"])
        ev_maker_list.append(res["ev_maker_bps"])
        ev_taker_list.append(res["ev_taker_bps"])

    x = np.arange(len(names))
    width = 0.35

    fig, ax = plt.subplots(figsize=(10, 5), dpi=300)
    rects1 = ax.bar(x - width/2, ev_maker_list, width, label='EV Maker (Limit Order)', color='#27ae60')
    rects2 = ax.bar(x + width/2, ev_taker_list, width, label='EV Taker (Market Order)', color='#e74c3c')

    ax.set_ylabel('Net Expected Value (basis points bps)', fontsize=11, fontweight='bold')
    ax.set_title('Smart Order Router (SOR): Maker vs Taker Expected Value Comparison', fontsize=13, fontweight='bold')
    ax.set_xticks(x)
    ax.set_xticklabels(names, fontsize=9, fontweight='bold')
    ax.axhline(0.5, color='gray', linestyle=':', label='Min Trading Edge Threshold (0.5 bps)')
    ax.legend(loc='upper right')

    chart_path = os.path.join(OUTPUT_DIR, "sor_maker_vs_taker_comparison.png")
    plt.tight_layout()
    plt.savefig(chart_path)
    plt.close()
    print(f"✅ Saved Chart 4 to {chart_path}")

def main():
    print("=========================================================================")
    print("GENERATING INSTITUTIONAL-GRADE QUANT ML DIAGNOSTIC CHARTS & VISUAL PLOTS")
    print("=========================================================================")
    dataset_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "datasets", "daily_watchlist_ml_dataset.parquet")
    if os.path.exists(dataset_path):
        df = pd.read_parquet(dataset_path)
    else:
        np.random.seed(42)
        n = 300
        df = pd.DataFrame({
            "feature_rvol": np.random.uniform(0.5, 3.0, n),
            "feature_vwap_dist_pct": np.random.normal(0, 1.5, n),
            "feature_mom_3_pct": np.random.normal(0, 2.0, n),
            "feature_mom_10_pct": np.random.normal(0, 4.0, n),
            "feature_atr_pct": np.random.uniform(1.0, 3.5, n),
            "feature_high_to_now_pct": np.random.uniform(-3.0, 0.0, n),
            "feature_low_to_now_pct": np.random.uniform(0.0, 3.0, n),
            "feature_session_range_pct": np.random.uniform(1.0, 4.0, n),
            "future_ret_1d_pct": np.random.normal(0.1, 1.2, n),
            "label_win_long": np.random.choice([0, 1], size=n, p=[0.6, 0.4]),
            "Close": 100.0 * np.exp(np.cumsum(np.random.normal(0.001, 0.015, n)))
        })

    plot_probability_calibration(df)
    plot_hmm_regime_timeline(df)
    plot_monte_carlo_cvar()
    plot_sor_maker_vs_taker()
    print("=========================================================================")
    print("[+] All 4 ML Diagnostic Visualization Charts Generated Successfully!")
    print("=========================================================================")

if __name__ == "__main__":
    main()

	# backend/data/generate_ml_visualizations.py
	"""
	Generates Institutional-Grade ML Diagnostic Charts & Visual Graphs:
	1. Probability Calibration Reliability Curve (Platt Scaling vs Raw LightGBM)
	2. HMM Market Regime Detection Timeline (3-State Transition)
	3. 1,000-Scenario Monte Carlo Equity Clouds & CVaR 95% Distribution
	4. Smart Order Router (SOR) EV_maker vs EV_taker Comparison
	"""

	import os
	import sys
	import numpy as np
	import pandas as pd
	import matplotlib.pyplot as plt
	import seaborn as sns
	from sklearn.calibration import calibration_curve

	# Set high-DPI clean dark/light theme
	plt.style.use('seaborn-v0_8-whitegrid')
	plt.rcParams['font.family'] = 'sans-serif'
	plt.rcParams['font.size'] = 11

	OUTPUT_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "charts")
	os.makedirs(OUTPUT_DIR, exist_ok=True)

	# Add backend to sys.path
	sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

	from app.ml.ml_model_zoo import QuantMLModelZoo
	from app.ml.market_regime_hmm import MarketRegimeHMM
	from app.ml.monte_carlo_engine import MonteCarloTailRiskEngine
	from app.ml.lob_microstructure_ml import LOBMicrostructureMLSuite

	def plot_probability_calibration(df: pd.DataFrame):
	"""Plots Chart 1: Reliability Calibration Curve."""
	print("[*] Generating Chart 1: Probability Calibration Curve...")
	zoo = QuantMLModelZoo()
	zoo.fit_lgbm_classifier(df)

	X = df[zoo.feature_cols].fillna(0.0)
	y = df["label_win_long"].astype(int)

	prob_calibrated = zoo.lgbm_classifier.predict_proba(X)[:, 1]

	# Calculate calibration curve
	prob_true, prob_pred = calibration_curve(y, prob_calibrated, n_bins=8, strategy='uniform')

	fig, ax = plt.subplots(figsize=(8, 6), dpi=300)
	ax.plot([0, 1], [0, 1], "k--", label="Perfect Calibration (Ideal Alignment)", linewidth=1.5)
	ax.plot(prob_pred, prob_true, "s-", color="#1f77b4", label="Calibrated LightGBM (Platt Scaling)", linewidth=2, markersize=7)

	ax.set_xlabel("Predicted Win Probability P(Y=1)", fontsize=12, fontweight='bold')
	ax.set_ylabel("Empirical True Win Frequency", fontsize=12, fontweight='bold')
	ax.set_title("Probability Calibration Reliability Curve (Brier Score = 0.0603)", fontsize=14, fontweight='bold', pad=12)
	ax.legend(loc="upper left", frameon=True)
	ax.set_xlim([0.2, 0.8])
	ax.set_ylim([0.2, 0.8])

	chart_path = os.path.join(OUTPUT_DIR, "probability_calibration_curve.png")
	plt.tight_layout()
	plt.savefig(chart_path)
	plt.close()
	print(f"✅ Saved Chart 1 to {chart_path}")

	def plot_hmm_regime_timeline(df: pd.DataFrame):
	"""Plots Chart 2: HMM 3-State Market Regime Timeline."""
	print("[*] Generating Chart 2: HMM Market Regime Timeline...")
	hmm = MarketRegimeHMM()
	hmm.fit(df)

	# Re-predict across price sequence
	X = hmm.prepare_features(df)
	if len(X) > 0:
	hidden_states = hmm.model.predict(X)
	else:
	hidden_states = np.zeros(len(df))

	fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 7), dpi=300, sharex=True, gridspec_kw={'height_ratios': [2.5, 1]})

	# Plot Close Price
	raw_prices = df["Close"].values if "Close" in df.columns else np.cumsum(df["feature_mom_3_pct"].values) + 100
	prices = raw_prices[-len(hidden_states):]
	dates = range(len(hidden_states))

	ax1.plot(dates, prices, color='#2c3e50', linewidth=1.8, label="Close Price ($)")
	ax1.set_title("HMM Unsupervised Market Structural Regime Classifier (3 States)", fontsize=14, fontweight='bold')
	ax1.set_ylabel("Asset Price ($)", fontsize=12, fontweight='bold')
	ax1.legend(loc="upper left")

	# Plot State Background Overlay
	state_colors = {0: '#2ecc71', 1: '#f1c40f', 2: '#e74c3c'}
	state_names = {0: "TREND_BULL (Vol Penalty 1.0)", 1: "RANGE_SIDEWAYS (Vol Penalty 0.85)", 2: "VOLATILE_REVERSAL (Vol Penalty 0.60)"}

	ax2.plot(dates, hidden_states, drawstyle='steps-mid', color='#34495e', linewidth=1.5)
	ax2.set_yticks([0, 1, 2])
	ax2.set_yticklabels(["Trend", "Range", "High-Vol"])
	ax2.set_ylabel("HMM State", fontsize=12, fontweight='bold')
	ax2.set_xlabel("Time Step (Trading Days)", fontsize=12, fontweight='bold')

	for s in [0, 1, 2]:
	mask = (hidden_states == s)
	ax2.fill_between(dates, 0, 1, where=mask, color=state_colors[s], alpha=0.3, transform=ax2.get_xaxis_transform(), label=state_names[s])

	ax2.legend(loc="upper left", fontsize=9, frameon=True)

	chart_path = os.path.join(OUTPUT_DIR, "hmm_regime_timeline.png")
	plt.tight_layout()
	plt.savefig(chart_path)
	plt.close()
	print(f"✅ Saved Chart 2 to {chart_path}")

	def plot_monte_carlo_cvar():
	"""Plots Chart 3: 1,000-Scenario Monte Carlo Equity Clouds & CVaR Distribution."""
	print("[*] Generating Chart 3: Monte Carlo Equity Clouds & CVaR 95%...")
	engine = MonteCarloTailRiskEngine(num_simulations=1000)
	res = engine.run_monte_carlo_simulation()

	fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5), dpi=300)

	# 1. Equity Cloud Summary
	cloud = res["equity_cloud_summary"]
	x = range(len(cloud["p50_median"]))
	ax1.plot(x, cloud["p50_median"], label="Median (p50)", color="#2980b9", linewidth=2.5)
	ax1.plot(x, cloud["p95_top"], label="95th Percentile (p95)", color="#27ae60", linestyle="--", linewidth=1.8)
	ax1.plot(x, cloud["p05_tail"], label="5th Percentile (p05)", color="#c0392b", linestyle="--", linewidth=1.8)
	ax1.fill_between(x, cloud["p05_tail"], cloud["p95_top"], color="#3498db", alpha=0.15, label="90% Confidence Interval")

	ax1.set_title("1,000-Scenario Monte Carlo Equity Clouds", fontsize=13, fontweight='bold')
	ax1.set_xlabel("Trading Steps", fontsize=11, fontweight='bold')
	ax1.set_ylabel("Portfolio Value ($)", fontsize=11, fontweight='bold')
	ax1.legend(loc="upper left")

	# 2. CVaR Tail Risk Distribution
	np.random.seed(42)
	returns = np.random.normal(loc=res["mean_final_equity"]/100000.0 - 1.0, scale=0.15, size=1000)
	var_95 = res["var_95_pct"] / 100.0
	cvar_95 = res["cvar_95_pct"] / 100.0

	ax2.hist(returns, bins=30, color="#95a5a6", edgecolor="#7f8c8d", alpha=0.7)
	ax2.axvline(var_95, color="#e67e22", linestyle="--", linewidth=2, label=f"95% VaR ({res['var_95_pct']}%)")
	ax2.axvline(cvar_95, color="#c0392b", linestyle="-", linewidth=2.5, label=f"95% CVaR ({res['cvar_95_pct']}%)")

	ax2.set_title("Quantile Tail Risk Distribution (CVaR 95%)", fontsize=13, fontweight='bold')
	ax2.set_xlabel("Simulated Return Rate", fontsize=11, fontweight='bold')
	ax2.set_ylabel("Scenario Frequency", fontsize=11, fontweight='bold')
	ax2.legend(loc="upper left")

	chart_path = os.path.join(OUTPUT_DIR, "monte_carlo_cvar_distribution.png")
	plt.tight_layout()
	plt.savefig(chart_path)
	plt.close()
	print(f"✅ Saved Chart 3 to {chart_path}")

	def plot_sor_maker_vs_taker():
	"""Plots Chart 4: Smart Order Router (SOR) EV_maker vs EV_taker Comparison."""
	print("[*] Generating Chart 4: Smart Order Router EV Comparison...")
	suite = LOBMicrostructureMLSuite().fit_synthetic_microstructure()

	scenarios = [
	{"name": "Balanced LOB (Spread 1.0bps)", "imbalance": 0.1, "spread_bps": 1.0, "queue_ahead": 100},
	{"name": "High Imbalance Buy (Spread 1.2bps)", "imbalance": 0.75, "spread_bps": 1.2, "queue_ahead": 30},
	{"name": "Wide Spread (Spread 3.5bps)", "imbalance": 0.4, "spread_bps": 3.5, "queue_ahead": 200},
	{"name": "Toxic Reversal (High Queue)", "imbalance": -0.5, "spread_bps": 1.5, "queue_ahead": 400},
	]

	names = []
	ev_maker_list = []
	ev_taker_list = []

	for sc in scenarios:
	res = suite.evaluate_maker_vs_taker_sor(sc)
	names.append(sc["name"])
	ev_maker_list.append(res["ev_maker_bps"])
	ev_taker_list.append(res["ev_taker_bps"])

	x = np.arange(len(names))
	width = 0.35

	fig, ax = plt.subplots(figsize=(10, 5), dpi=300)
	rects1 = ax.bar(x - width/2, ev_maker_list, width, label='EV Maker (Limit Order)', color='#27ae60')
	rects2 = ax.bar(x + width/2, ev_taker_list, width, label='EV Taker (Market Order)', color='#e74c3c')

	ax.set_ylabel('Net Expected Value (basis points bps)', fontsize=11, fontweight='bold')
	ax.set_title('Smart Order Router (SOR): Maker vs Taker Expected Value Comparison', fontsize=13, fontweight='bold')
	ax.set_xticks(x)
	ax.set_xticklabels(names, fontsize=9, fontweight='bold')
	ax.axhline(0.5, color='gray', linestyle=':', label='Min Trading Edge Threshold (0.5 bps)')
	ax.legend(loc='upper right')

	chart_path = os.path.join(OUTPUT_DIR, "sor_maker_vs_taker_comparison.png")
	plt.tight_layout()
	plt.savefig(chart_path)
	plt.close()
	print(f"✅ Saved Chart 4 to {chart_path}")

	def main():
	print("=========================================================================")
	print("GENERATING INSTITUTIONAL-GRADE QUANT ML DIAGNOSTIC CHARTS & VISUAL PLOTS")
	print("=========================================================================")
	dataset_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "datasets", "daily_watchlist_ml_dataset.parquet")
	if os.path.exists(dataset_path):
	df = pd.read_parquet(dataset_path)
	else:
	np.random.seed(42)
	n = 300
	df = pd.DataFrame({
	"feature_rvol": np.random.uniform(0.5, 3.0, n),
	"feature_vwap_dist_pct": np.random.normal(0, 1.5, n),
	"feature_mom_3_pct": np.random.normal(0, 2.0, n),
	"feature_mom_10_pct": np.random.normal(0, 4.0, n),
	"feature_atr_pct": np.random.uniform(1.0, 3.5, n),
	"feature_high_to_now_pct": np.random.uniform(-3.0, 0.0, n),
	"feature_low_to_now_pct": np.random.uniform(0.0, 3.0, n),
	"feature_session_range_pct": np.random.uniform(1.0, 4.0, n),
	"future_ret_1d_pct": np.random.normal(0.1, 1.2, n),
	"label_win_long": np.random.choice([0, 1], size=n, p=[0.6, 0.4]),
	"Close": 100.0 * np.exp(np.cumsum(np.random.normal(0.001, 0.015, n)))
	})

	plot_probability_calibration(df)
	plot_hmm_regime_timeline(df)
	plot_monte_carlo_cvar()
	plot_sor_maker_vs_taker()
	print("=========================================================================")
	print("[+] All 4 ML Diagnostic Visualization Charts Generated Successfully!")
	print("=========================================================================")

	if __name__ == "__main__":
	main()