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fix streak signal, reweight predictions, and reorder UI

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- Fix streak signal: was giving 100% to streak chair after normalization
  (non-streak chairs were 0), now properly distributes probability with
  streak chair getting less as streak grows (actual mean reversion)
- Change recent window from 20 to 50 games
- Reweight signals based on backtest: base_rate 0.15→0.20 (best performer),
  recent 0.10→0.15, streak 0.10→0.05, balance 0.15→0.10
- Move Live Market Sentiment above Signal Breakdown
This commit is contained in:
Junaid Saeed Uppal
2026-02-26 10:36:53 +05:00
parent 9762c0f9bf
commit 2c7cd67ab7
2 changed files with 32 additions and 52 deletions
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53
app/db.py
View File

@@ -1031,12 +1031,12 @@ def _bayesian_prediction(winners, markov1, markov2):
key2 = f"{winners[-2]}{winners[-1]}" key2 = f"{winners[-2]}{winners[-1]}"
m2 = markov2.get(key2, {c: 1 / 3 for c in CHAIR_LABELS}) m2 = markov2.get(key2, {c: 1 / 3 for c in CHAIR_LABELS})
# Signal 4: Recent 20-game frequency — 15% # Signal 4: Recent 50-game frequency — 15%
recent = winners[-20:] if len(winners) >= 20 else winners recent = winners[-50:] if len(winners) >= 50 else winners
recent_total = len(recent) recent_total = len(recent)
rec = {c: recent.count(c) / recent_total for c in CHAIR_LABELS} rec = {c: recent.count(c) / recent_total for c in CHAIR_LABELS}
# Signal 5: Streak momentum/regression — 10% # Signal 5: Streak regression — 5%
streak_chair = winners[-1] streak_chair = winners[-1]
streak_len = 0 streak_len = 0
for w in reversed(winners): for w in reversed(winners):
@@ -1045,20 +1045,11 @@ def _bayesian_prediction(winners, markov1, markov2):
else: else:
break break
# Regression to mean: longer streaks → lower probability of continuation # Regression to mean: longer streaks → lower probability of continuation
streak = {} cont_prob = max(0.1, 1 / 3 - streak_len * 0.05)
for c in CHAIR_LABELS: other_prob = (1 - cont_prob) / 2
if c == streak_chair: streak = {c: (cont_prob if c == streak_chair else other_prob) for c in CHAIR_LABELS}
streak[c] = max(0.1, 1 / 3 - streak_len * 0.05)
else:
streak[c] = 0
# Normalize streak signal
s_total = sum(streak.values())
if s_total > 0:
streak = {c: streak[c] / s_total for c in CHAIR_LABELS}
else:
streak = {c: 1 / 3 for c in CHAIR_LABELS}
# Signal 6: Balance / Mean Reversion — 15% # Signal 6: Balance / Mean Reversion — 10%
# Look at last 50 games, invert frequencies to favor under-represented chairs # Look at last 50 games, invert frequencies to favor under-represented chairs
window = min(50, len(winners)) window = min(50, len(winners))
recent_50 = winners[-window:] recent_50 = winners[-window:]
@@ -1067,8 +1058,8 @@ def _bayesian_prediction(winners, markov1, markov2):
bal_total = sum(balance.values()) bal_total = sum(balance.values())
balance = {c: balance[c] / bal_total for c in CHAIR_LABELS} balance = {c: balance[c] / bal_total for c in CHAIR_LABELS}
weights = {"base_rate": 0.15, "markov_1": 0.25, "markov_2": 0.25, "recent_20": 0.10, "streak": 0.10, "balance": 0.15} weights = {"base_rate": 0.20, "markov_1": 0.25, "markov_2": 0.25, "recent_50": 0.15, "streak": 0.05, "balance": 0.10}
signals = {"base_rate": base, "markov_1": m1, "markov_2": m2, "recent_20": rec, "streak": streak, "balance": balance} signals = {"base_rate": base, "markov_1": m1, "markov_2": m2, "recent_50": rec, "streak": streak, "balance": balance}
combined = {c: 0 for c in CHAIR_LABELS} combined = {c: 0 for c in CHAIR_LABELS}
for sig_name, weight in weights.items(): for sig_name, weight in weights.items():
@@ -1189,7 +1180,7 @@ def _backtest_theories(winners):
if len(winners) <= warmup: if len(winners) <= warmup:
return {"error": "Not enough data for backtesting"} return {"error": "Not enough data for backtesting"}
theories = ["base_rate", "markov_1", "markov_2", "recent_20", "streak", "balance", "combined"] theories = ["base_rate", "markov_1", "markov_2", "recent_50", "streak", "balance", "combined"]
full_hits = {t: 0 for t in theories} full_hits = {t: 0 for t in theories}
semi_hits = {t: 0 for t in theories} semi_hits = {t: 0 for t in theories}
total_tested = 0 total_tested = 0
@@ -1217,8 +1208,8 @@ def _backtest_theories(winners):
m2_probs = m2.get(key2, {c: 1 / 3 for c in CHAIR_LABELS}) m2_probs = m2.get(key2, {c: 1 / 3 for c in CHAIR_LABELS})
m2_ranked = sorted(CHAIR_LABELS, key=lambda c: m2_probs.get(c, 0), reverse=True) m2_ranked = sorted(CHAIR_LABELS, key=lambda c: m2_probs.get(c, 0), reverse=True)
# Recent-20 # Recent-50
recent = history[-20:] if len(history) >= 20 else history recent = history[-50:] if len(history) >= 50 else history
rec = {c: recent.count(c) / len(recent) for c in CHAIR_LABELS} rec = {c: recent.count(c) / len(recent) for c in CHAIR_LABELS}
rec_ranked = sorted(CHAIR_LABELS, key=lambda c: rec[c], reverse=True) rec_ranked = sorted(CHAIR_LABELS, key=lambda c: rec[c], reverse=True)
@@ -1230,17 +1221,9 @@ def _backtest_theories(winners):
streak_len += 1 streak_len += 1
else: else:
break break
streak_probs = {} cont_prob = max(0.1, 1 / 3 - streak_len * 0.05)
for c in CHAIR_LABELS: other_prob = (1 - cont_prob) / 2
if c == streak_chair: streak_probs = {c: (cont_prob if c == streak_chair else other_prob) for c in CHAIR_LABELS}
streak_probs[c] = max(0.1, 1 / 3 - streak_len * 0.05)
else:
streak_probs[c] = 0
s_total = sum(streak_probs.values())
if s_total > 0:
streak_probs = {c: streak_probs[c] / s_total for c in CHAIR_LABELS}
else:
streak_probs = {c: 1 / 3 for c in CHAIR_LABELS}
streak_ranked = sorted(CHAIR_LABELS, key=lambda c: streak_probs[c], reverse=True) streak_ranked = sorted(CHAIR_LABELS, key=lambda c: streak_probs[c], reverse=True)
# Balance / Mean Reversion # Balance / Mean Reversion
@@ -1254,8 +1237,8 @@ def _backtest_theories(winners):
# Combined Bayesian # Combined Bayesian
combined = {c: 0 for c in CHAIR_LABELS} combined = {c: 0 for c in CHAIR_LABELS}
weights = {"base_rate": 0.15, "markov_1": 0.25, "markov_2": 0.25, "recent_20": 0.10, "streak": 0.10, "balance": 0.15} weights = {"base_rate": 0.20, "markov_1": 0.25, "markov_2": 0.25, "recent_50": 0.15, "streak": 0.05, "balance": 0.10}
signals = {"base_rate": base, "markov_1": m1_probs, "markov_2": m2_probs, "recent_20": rec, "streak": streak_probs, "balance": bal_probs} signals = {"base_rate": base, "markov_1": m1_probs, "markov_2": m2_probs, "recent_50": rec, "streak": streak_probs, "balance": bal_probs}
for sig_name, weight in weights.items(): for sig_name, weight in weights.items():
for c in CHAIR_LABELS: for c in CHAIR_LABELS:
combined[c] += weight * signals[sig_name].get(c, 1 / 3) combined[c] += weight * signals[sig_name].get(c, 1 / 3)
@@ -1263,7 +1246,7 @@ def _backtest_theories(winners):
ranked = { ranked = {
"base_rate": base_ranked, "markov_1": m1_ranked, "markov_2": m2_ranked, "base_rate": base_ranked, "markov_1": m1_ranked, "markov_2": m2_ranked,
"recent_20": rec_ranked, "streak": streak_ranked, "balance": bal_ranked, "recent_50": rec_ranked, "streak": streak_ranked, "balance": bal_ranked,
"combined": combined_ranked, "combined": combined_ranked,
} }
for t in theories: for t in theories:

29
static/predictions.html
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@@ -253,19 +253,8 @@ body { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-
<div id="impact-content"></div> <div id="impact-content"></div>
</div> </div>
<div class="panel" style="margin-top:8px">
<div class="panel-title">Signal Breakdown</div>
<table class="signal-table" id="signal-table">
<thead><tr><th>Signal</th><th>Weight</th><th>A</th><th>B</th><th>C</th></tr></thead>
<tbody></tbody>
</table>
</div>
</div>
<!-- Live Trends: Whale + Public --> <!-- Live Trends: Whale + Public -->
<div class="section"> <div class="trends-grid" style="margin-top:16px">
<div class="section-title">Live Market Sentiment</div>
<div class="trends-grid">
<div class="trend-panel"> <div class="trend-panel">
<div class="panel-title">Whale Trend (Top 5 Bettors)</div> <div class="panel-title">Whale Trend (Top 5 Bettors)</div>
<div id="whale-trend"><div class="trend-empty">Waiting for bets...</div></div> <div id="whale-trend"><div class="trend-empty">Waiting for bets...</div></div>
@@ -278,7 +267,15 @@ body { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-
</div> </div>
</div> </div>
<!-- Historical: did crowd pick the winner? --> <!-- Historical: did crowd pick the winner? -->
<div id="crowd-stats" class="crowd-stats"></div> <div id="crowd-stats" class="crowd-stats" style="margin-top:12px"></div>
<div class="panel" style="margin-top:12px">
<div class="panel-title">Signal Breakdown</div>
<table class="signal-table" id="signal-table">
<thead><tr><th>Signal</th><th>Weight</th><th>A</th><th>B</th><th>C</th></tr></thead>
<tbody></tbody>
</table>
</div>
</div> </div>
<!-- Last 50 Predictions vs Actual --> <!-- Last 50 Predictions vs Actual -->
@@ -480,7 +477,7 @@ function renderPrediction(data) {
// Signal table // Signal table
const tbody = $('signal-table').querySelector('tbody'); const tbody = $('signal-table').querySelector('tbody');
const sigNames = {'base_rate':'Base Rate','markov_1':'Markov-1','markov_2':'Markov-2','recent_20':'Recent 20','streak':'Streak','balance':'Balance'}; const sigNames = {'base_rate':'Base Rate','markov_1':'Markov-1','markov_2':'Markov-2','recent_50':'Recent 50','streak':'Streak','balance':'Balance'};
tbody.innerHTML = Object.entries(data.signals).map(([key, sig]) => tbody.innerHTML = Object.entries(data.signals).map(([key, sig]) =>
`<tr><td style="text-align:left">${sigNames[key]||key}</td><td>${(sig.weight*100).toFixed(0)}%</td>` + `<tr><td style="text-align:left">${sigNames[key]||key}</td><td>${(sig.weight*100).toFixed(0)}%</td>` +
CHAIRS.map(c => `<td style="color:${CHAIR_COLORS[c]}">${pct(sig.probs[c])}</td>`).join('') + '</tr>' CHAIRS.map(c => `<td style="color:${CHAIR_COLORS[c]}">${pct(sig.probs[c])}</td>`).join('') + '</tr>'
@@ -1051,7 +1048,7 @@ function renderRunsTest(runs) {
function renderBacktest(bt) { function renderBacktest(bt) {
if (bt.error) { $('backtest-cards').innerHTML = `<div style="color:var(--text2)">${bt.error}</div>`; return; } if (bt.error) { $('backtest-cards').innerHTML = `<div style="color:var(--text2)">${bt.error}</div>`; return; }
const names = {base_rate:'Base Rate',markov_1:'Markov-1',markov_2:'Markov-2',recent_20:'Recent 20',streak:'Streak',balance:'Balance',combined:'Combined'}; const names = {base_rate:'Base Rate',markov_1:'Markov-1',markov_2:'Markov-2',recent_50:'Recent 50',streak:'Streak',balance:'Balance',combined:'Combined'};
$('backtest-cards').innerHTML = Object.entries(bt.accuracy).map(([key, acc]) => { $('backtest-cards').innerHTML = Object.entries(bt.accuracy).map(([key, acc]) => {
const fh = bt.full_hits?.[key] ?? '?'; const fh = bt.full_hits?.[key] ?? '?';
const sh = bt.semi_hits?.[key] ?? '?'; const sh = bt.semi_hits?.[key] ?? '?';
@@ -1062,7 +1059,7 @@ function renderBacktest(bt) {
}).join(''); }).join('');
if (bt.rolling_accuracy) { if (bt.rolling_accuracy) {
const ctx = $('backtest-chart').getContext('2d'); const ctx = $('backtest-chart').getContext('2d');
const colors = {base_rate:'#8b8fa3',markov_1:'#3b82f6',markov_2:'#ec4899',recent_20:'#f59e0b',streak:'#10b981',balance:'#f472b6',combined:'#6c5ce7'}; const colors = {base_rate:'#8b8fa3',markov_1:'#3b82f6',markov_2:'#ec4899',recent_50:'#f59e0b',streak:'#10b981',balance:'#f472b6',combined:'#6c5ce7'};
const datasets = Object.entries(bt.rolling_accuracy).map(([key, data]) => ({ const datasets = Object.entries(bt.rolling_accuracy).map(([key, data]) => ({
label: names[key]||key, data, borderColor: colors[key]||'#fff', backgroundColor: 'transparent', label: names[key]||key, data, borderColor: colors[key]||'#fff', backgroundColor: 'transparent',
borderWidth: key === 'combined' ? 3 : 1.5, pointRadius: 0, tension: 0.3, borderWidth: key === 'combined' ? 3 : 1.5, pointRadius: 0, tension: 0.3,