def build_econometric_validation_html(dm_results, var_results, psr_results=None, dsr_results=None, pt_results=None, lb_results=None): validation_html = "" if dm_results or var_results or psr_results or dsr_results: val_rows = "" if dm_results: dm_pass = dm_results.get('significant', False) and dm_results.get('winner') not in ['Naive Mean', 'Model 2'] dm_col = "#3fb950" if dm_pass else "#e3b341" if dm_pass: dm_diag = "Model successfully outperforms a naive historical baseline with statistical significance." elif not dm_results.get('significant', False): dm_diag = "Model fails to statistically distinguish itself from a simple historical average." else: dm_diag = "Model underperforms the naive historical baseline." val_rows += ( f'

' f'Predictive Alpha (DM Test) ⓘ

' f'

{"PASS" if dm_pass else "INCONCLUSIVE"}

' f'

p = {dm_results.get("p_value", 1.0):.4f}

' f'

{dm_diag}

' ) if var_results: var_pass = var_results.get('overall_pass', False) var_col = "#3fb950" if var_pass else "#f85149" uc_p = var_results.get('unconditional_coverage', {}).get('p_value', 0.0) ind_p = var_results.get('independence', {}).get('p_value', 0.0) diag_text = var_results.get('diagnostic', '') val_rows += ( f'

' f'Tail Risk Validity (VaR) ⓘ

' f'

{"PASS" if var_pass else "FAIL"}

' f'

Coverage p = {uc_p:.4f} | Independence p = {ind_p:.4f}

' + (f'

{diag_text}

' if diag_text and not var_pass else '') + '

' ) if psr_results: psr_pass = psr_results.get('p_value', 1.0) < 0.05 psr_col = "#3fb950" if psr_pass else "#e3b341" if psr_pass: psr_diag = "Sharpe ratio is statistically robust even after accounting for non-normal skew and fat tails." else: psr_diag = "Sharpe ratio is not statistically significant; outperformance may be driven by extreme outliers or luck." val_rows += ( f'

' f'Probabilistic Sharpe (PSR) ⓘ

' f'

{"PASS" if psr_pass else "INCONCLUSIVE"}

' f'

p = {psr_results.get("p_value", 1.0):.4f} (Obs: {psr_results.get("observed_sharpe", 0.0):.2f})

' f'

{psr_diag}

' ) if dsr_results: dsr_pass = dsr_results.get('p_value', 1.0) < 0.05 dsr_col = "#3fb950" if dsr_pass else "#e3b341" if dsr_pass: dsr_diag = "Strategy is robust against overfitting and multiple testing bias (data mining)." else: dsr_diag = "Cannot rule out multiple testing bias; strategy might be overfitted to historical data." val_rows += ( f'

' f'Deflated Sharpe (DSR) ⓘ

' f'

{"PASS" if dsr_pass else "INCONCLUSIVE"}

' f'

p = {dsr_results.get("p_value", 1.0):.4f}

' f'

{dsr_diag}

' ) if pt_results: pt_pass = pt_results.get('significant', False) pt_col = "#3fb950" if pt_pass else "#e3b341" val_rows += ( f'

' f'Directional Accuracy (PT Test) ⓘ

' f'

{"PASS" if pt_pass else "INCONCLUSIVE"}

' f'

p = {pt_results.get("p_value", 1.0):.4f}

' ) if lb_results: lb_pass = not lb_results.get('significant', True) # Null hypothesis is NO autocorrelation. We want p > 0.05 lb_col = "#3fb950" if lb_pass else "#e3b341" val_rows += ( f'

' f'GARCH Autocorrelation (Ljung-Box) ⓘ

' f'

{"PASS" if lb_pass else "FAIL"}

' f'

p = {lb_results.get("p_value", 0.0):.4f}

' ) verdicts = [] if dm_results: if dm_results.get('significant', False) and dm_results.get('winner') not in ['Naive Mean', 'Model 2']: verdicts.append("Your model statistically outperforms the naive historical baseline at 95% confidence.") else: verdicts.append("The model does not show statistically significant outperformance over a naive historical mean.") if var_results: if var_results.get('overall_pass', False): verdicts.append("Tail risk metrics (VaR) hold up well against real-world volatility clustering.") else: verdicts.append("VaR breaches cluster significantly; consider widening your rebalance frequency or reducing risk limits.") if psr_results and psr_results.get('p_value', 1.0) < 0.05: verdicts.append("The Sharpe ratio is robust even under non-normal return distributions.") verdict_html = "" if verdicts: verdict_html = ( f'

' f'Diagnostic Verdict: {" ".join(verdicts)}' f'

' ) validation_html = ( '

Advanced Econometric Validation

' f'{verdict_html}' f'

{val_rows}

' ) return validation_html