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Trend bias on 4H (EMA50/EMA200 + swing structure)\n// 2. POI detection on 1H/15M (Order Blocks + FVG)\n// 3. Execution trigger on 5M (reversal candle at POI retest)\n// 4. Risk management (SL behind structure, TP1/TP2 at 1:1 / 1:2)\n// 5. Adaptive learning from closed-trade outcomes\n// ============================================================\n\nimport { sqlite } from \"https://esm.town/v/std/sqlite/main.ts\";\n\nconst BINANCE = \"https://data-api.binance.vision/api/v3\";\n\n// ---------- Types ----------\nexport interface Candle {\n time: number;\n open: number;\n high: number;\n low: number;\n close: number;\n volume: number;\n}\n\nexport type Side = \"LONG\" | \"SHORT\";\n\nexport interface Signal {\n symbol: string;\n side: Side;\n entry: number;\n stopLoss: number;\n takeProfit1: number;\n takeProfit2: number;\n rr: number; // reward:risk to TP2\n confidence: number; // 0-100 confluence score\n reason: string;\n timeframe: string; // POI timeframe\n timestamp: number;\n // Learning features (used to estimate historical win probability).\n poiType: \"OB\" | \"FVG\";\n trendType: Trend;\n liquidity: \"HIGH\" | \"MEDIUM\";\n}\n\nexport interface LiquidityCoin {\n symbol: string; // e.g. \"BTCUSDT\"\n base: string; // e.g. \"BTC\"\n quoteVolume: number; // 24h USDT volume\n liquidity: \"HIGH\" | \"MEDIUM\";\n}\n\n// ---------- Config ----------\nexport const LIQUIDITY_HIGH_MIN = 50_000_000; // >= $50M/day => HIGH\nexport const LIQUIDITY_MEDIUM_MIN = 5_000_000; // >= $5M/day => MEDIUM\nexport const MAX_COINS_PER_SCAN = 80;\nconst STABLE_BASES = new Set([\n \"USDT\", \"USDC\", \"BUSD\", \"DAI\", \"TUSD\", \"FDUSD\", \"USDP\", \"EUR\", \"AEUR\",\n \"USTC\", \"USDD\", \"PYUSD\", \"EURI\", \"XUSD\", \"USD1\",\n]);\n\n// ---------- Binance data ----------\nexport async function fetchKlines(\n symbol: string,\n interval: string,\n limit = 200,\n): Promise {\n const res = await fetch(\n `${BINANCE}/klines?symbol=${symbol}&interval=${interval}&limit=${limit}`,\n );\n if (!res.ok) throw new Error(`klines ${symbol} ${interval} ${res.status}`);\n const raw = (await res.json()) as unknown[][];\n return raw.map((k) => ({\n time: k[0] as number,\n open: parseFloat(k[1] as string),\n high: parseFloat(k[2] as string),\n low: parseFloat(k[3] as string),\n close: parseFloat(k[4] as string),\n volume: parseFloat(k[5] as string),\n }));\n}\n\n// ---------- Liquidity universe ----------\n// Scan medium & high liquidity coins from the 24h ticker.\nexport async function getLiquidCoins(): Promise {\n const res = await fetch(`${BINANCE}/ticker/24hr`);\n if (!res.ok) throw new Error(`24hr ${res.status}`);\n const all = (await res.json()) as {\n symbol: string;\n quoteVolume: string;\n }[];\n\n const coins: LiquidityCoin[] = [];\n for (const t of all) {\n if (!t.symbol.endsWith(\"USDT\")) continue;\n const base = t.symbol.slice(0, -4);\n if (STABLE_BASES.has(base)) continue;\n const qv = parseFloat(t.quoteVolume);\n if (!Number.isFinite(qv) || qv <= 0) continue;\n if (qv >= LIQUIDITY_HIGH_MIN) {\n coins.push({ symbol: t.symbol, base, quoteVolume: qv, liquidity: \"HIGH\" });\n } else if (qv >= LIQUIDITY_MEDIUM_MIN) {\n coins.push({ symbol: t.symbol, base, quoteVolume: qv, liquidity: \"MEDIUM\" });\n }\n }\n\n // Sort by volume desc, take the top N (mix of medium & high).\n coins.sort((a, b) => b.quoteVolume - a.quoteVolume);\n return coins.slice(0, MAX_COINS_PER_SCAN);\n}\n\n// ---------- Indicators ----------\nexport function ema(values: number[], period: number): number[] {\n const k = 2 / (period + 1);\n const out: number[] = [];\n let prev = values[0];\n for (let i = 0; i < values.length; i++) {\n if (i === 0) {\n out.push(values[0]);\n prev = values[0];\n } else {\n prev = values[i] * k + prev * (1 - k);\n out.push(prev);\n }\n }\n return out;\n}\n\nexport function atr(candles: Candle[], period = 14): number {\n if (candles.length < period + 1) return 0;\n let sum = 0;\n for (let i = 1; i <= period; i++) {\n const c = candles[i];\n const p = candles[i - 1];\n const tr = Math.max(\n c.high - c.low,\n Math.abs(c.high - p.close),\n Math.abs(c.low - p.close),\n );\n sum += tr;\n }\n return sum / period;\n}\n\n// ---------- Swing structure ----------\ninterface Swing {\n index: number;\n price: number;\n type: \"high\" | \"low\";\n}\n\n// Fractal swing detection: a candle is a swing high if its high is the max of\n// the surrounding `left` candles on each side.\nexport function findSwings(candles: Candle[], left = 2): Swing[] {\n const swings: Swing[] = [];\n for (let i = left; i < candles.length - left; i++) {\n let isHigh = true;\n let isLow = true;\n for (let j = i - left; j <= i + left; j++) {\n if (j === i) continue;\n if (candles[j].high >= candles[i].high) isHigh = false;\n if (candles[j].low <= candles[i].low) isLow = false;\n }\n if (isHigh) swings.push({ index: i, price: candles[i].high, type: \"high\" });\n if (isLow) swings.push({ index: i, price: candles[i].low, type: \"low\" });\n }\n return swings;\n}\n\nexport type Trend = \"bullish\" | \"bearish\" | \"range\";\n\n// Trend bias from EMA50/EMA200 on the last candle + swing structure.\nexport function detectTrend(candles: Candle[]): Trend {\n if (candles.length < 200) return \"range\";\n const closes = candles.map((c) => c.close);\n const e50 = ema(closes, 50);\n const e200 = ema(closes, 200);\n const last = closes.length - 1;\n const price = closes[last];\n\n const swings = findSwings(candles, 3).filter((s) => s.index > last - 60);\n const highs = swings.filter((s) => s.type === \"high\");\n const lows = swings.filter((s) => s.type === \"low\");\n\n // Higher highs & higher lows => bullish; lower highs & lower lows => bearish.\n let hh = 0, hl = 0, lh = 0, ll = 0;\n for (let i = 1; i < highs.length; i++) {\n if (highs[i].price > highs[i - 1].price) hh++; else lh++;\n }\n for (let i = 1; i < lows.length; i++) {\n if (lows[i].price > lows[i - 1].price) hl++; else ll++;\n }\n\n const emaBull = price > e50[last] && e50[last] > e200[last];\n const emaBear = price < e50[last] && e50[last] < e200[last];\n\n // Strong sustained trend: EMA alignment confirmed by structure count.\n if (emaBull && hh + hl >= lh + ll - 2) return \"bullish\";\n if (emaBear && lh + ll >= hh + hl - 2) return \"bearish\";\n\n // Weaker but still directional: rely on EMA alignment alone.\n if (emaBull) return \"bullish\";\n if (emaBear) return \"bearish\";\n\n return \"range\";\n}\n\n// ---------- POI detection ----------\nexport interface POI {\n type: \"OB\" | \"FVG\";\n side: Side;\n top: number;\n bottom: number;\n index: number;\n}\n\n// Order Block: the last opposite candle before a strong directional move.\nexport function findOrderBlocks(candles: Candle[], lookback = 60): POI[] {\n const pois: POI[] = [];\n const start = Math.max(1, candles.length - lookback);\n for (let i = start; i < candles.length - 1; i++) {\n const move = candles[i + 1].close - candles[i].close;\n const body = Math.abs(candles[i].close - candles[i].open);\n // Strong up move preceded by a bearish candle => demand OB (LONG)\n if (move > 0 && candles[i].close < candles[i].open && body > 0) {\n pois.push({\n type: \"OB\",\n side: \"LONG\",\n top: Math.max(candles[i].open, candles[i].close),\n bottom: Math.min(candles[i].open, candles[i].close),\n index: i,\n });\n }\n // Strong down move preceded by a bullish candle => supply OB (SHORT)\n if (move < 0 && candles[i].close > candles[i].open && body > 0) {\n pois.push({\n type: \"OB\",\n side: \"SHORT\",\n top: Math.max(candles[i].open, candles[i].close),\n bottom: Math.min(candles[i].open, candles[i].close),\n index: i,\n });\n }\n }\n return pois;\n}\n\n// Fair Value Gap: gap between candle[i-1] and candle[i+1].\nexport function findFVG(candles: Candle[], lookback = 60): POI[] {\n const pois: POI[] = [];\n const start = Math.max(1, candles.length - lookback);\n for (let i = start; i < candles.length - 1; i++) {\n const prev = candles[i - 1];\n const next = candles[i + 1];\n // Bullish FVG: next.low > prev.high\n if (next.low > prev.high) {\n pois.push({\n type: \"FVG\",\n side: \"LONG\",\n top: next.low,\n bottom: prev.high,\n index: i,\n });\n }\n // Bearish FVG: next.high < prev.low\n if (next.high < prev.low) {\n pois.push({\n type: \"FVG\",\n side: \"SHORT\",\n top: prev.low,\n bottom: next.high,\n index: i,\n });\n }\n }\n return pois;\n}\n\n// ---------- Reversal candle detection (execution trigger) ----------\n// Returns true if the last candle is a valid reversal trigger for `side`.\nexport function isReversalTrigger(candles: Candle[], side: Side): boolean {\n if (candles.length < 3) return false;\n const c = candles[candles.length - 1];\n const p = candles[candles.length - 2];\n const body = Math.abs(c.close - c.open);\n const range = c.high - c.low;\n if (range === 0) return false;\n const lowerWick = Math.min(c.open, c.close) - c.low;\n const upperWick = c.high - Math.max(c.open, c.close);\n\n if (side === \"LONG\") {\n // Hammer: small body near top, long lower wick (>= 2x body)\n const hammer = body > 0 && lowerWick >= 2 * body && upperWick <= body;\n // Bullish engulfing: green candle engulfs previous red body\n const engulf = c.close > c.open && p.close < p.open &&\n c.close >= p.open && c.open <= p.close;\n return hammer || engulf;\n } else {\n // Hanging man: small body, long lower wick at top of range (bearish)\n const hangingMan = body > 0 && lowerWick >= 2 * body && c.close >= p.close;\n // Bearish engulfing\n const engulf = c.close < c.open && p.close > p.open &&\n c.close <= p.open && c.open >= p.close;\n return hangingMan || engulf;\n }\n}\n\n// ---------- Full signal generation for one coin ----------\nexport async function generateSignal(\n symbol: string,\n liquidity: \"HIGH\" | \"MEDIUM\" = \"MEDIUM\",\n): Promise {\n // Fetch the three timeframes we need.\n const [c4h, c1h, c5m] = await Promise.all([\n fetchKlines(symbol, \"4h\", 250),\n fetchKlines(symbol, \"1h\", 100),\n fetchKlines(symbol, \"5m\", 80),\n ]);\n\n const trend = detectTrend(c4h);\n if (trend === \"range\") return null; // no bias => no trade\n\n const last4h = c4h[c4h.length - 1];\n const price = last4h.close;\n const a = atr(c4h, 14);\n if (a <= 0) return null;\n\n // POIs from 1H (medium timeframe).\n const ob = findOrderBlocks(c1h, 60);\n const fvg = findFVG(c1h, 60);\n const pois = [...ob, ...fvg].filter((p) => p.index > c1h.length - 40);\n\n // Pick the side aligned with the trend.\n const side: Side = trend === \"bullish\" ? \"LONG\" : \"SHORT\";\n\n // Find a POI in the direction of the trend whose zone the price is near.\n const zone = pois\n .filter((p) => p.side === side)\n .map((p) => ({ ...p, dist: Math.abs(price - (p.top + p.bottom) / 2) }))\n .sort((x, y) => x.dist - y.dist)[0];\n\n if (!zone) return null;\n\n // Execution: price must be inside / at the POI zone AND a reversal trigger\n // must appear on 5M (retest confirmation).\n const inZone = price >= zone.bottom * 0.995 && price <= zone.top * 1.005;\n const trigger = isReversalTrigger(c5m, side);\n if (!inZone || !trigger) return null;\n\n // ---- Risk management ----\n // SL behind the nearest swing structure beyond the zone.\n const swings = findSwings(c4h, 2).filter((s) => s.index > c4h.length - 60);\n let stopLoss: number;\n if (side === \"LONG\") {\n const support = swings.filter((s) => s.type === \"low\" && s.price < zone.bottom)\n .sort((x, y) => y.price - x.price)[0];\n stopLoss = support ? support.price : zone.bottom - a * 0.5;\n stopLoss -= a * 0.1; // buffer vs stop hunts\n } else {\n const resistance = swings.filter((s) => s.type === \"high\" && s.price > zone.top)\n .sort((x, y) => x.price - y.price)[0];\n stopLoss = resistance ? resistance.price : zone.top + a * 0.5;\n stopLoss += a * 0.1;\n }\n\n const risk = Math.abs(price - stopLoss);\n if (risk <= 0) return null;\n\n // TP1 = 1:1, TP2 = 1:2 (measured move / R:R targets).\n let tp1: number, tp2: number;\n if (side === \"LONG\") {\n tp1 = price + risk;\n tp2 = price + risk * 2;\n } else {\n tp1 = price - risk;\n tp2 = price - risk * 2;\n }\n\n // R:R gate: reject trades below 1:2 to TP2.\n const rr = (Math.abs(tp2 - price)) / risk;\n if (rr < 2) return null;\n\n // Confluence score.\n let confidence = 40;\n if (zone.type === \"OB\") confidence += 15;\n if (ob.some((o) => o.side === side && o.index > c1h.length - 30)) confidence += 10;\n if (trend !== \"range\") confidence += 15;\n if (isReversalTrigger(c5m, side)) confidence += 10;\n\n return {\n symbol,\n side,\n entry: price,\n stopLoss,\n takeProfit1: tp1,\n takeProfit2: tp2,\n rr,\n confidence,\n reason: `${trend} trend | ${zone.type} ${side} @ ${zone.top.toFixed(6)}-${zone.bottom.toFixed(6)} | 5m trigger`,\n timeframe: \"1H\",\n timestamp: Date.now(),\n poiType: zone.type,\n trendType: trend,\n liquidity,\n };\n}\n\n// ---------- DB ----------\nexport async function initStrategyDb() {\n await sqlite.execute(`CREATE TABLE IF NOT EXISTS strategy_wallet (\n id INTEGER PRIMARY KEY CHECK (id = 1),\n balance REAL NOT NULL DEFAULT 1000.0,\n initial_balance REAL NOT NULL DEFAULT 1000.0,\n open_positions INTEGER NOT NULL DEFAULT 0\n )`);\n await sqlite.execute(`CREATE TABLE IF NOT EXISTS strategy_trades (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n symbol TEXT NOT NULL,\n side TEXT NOT NULL,\n entry REAL NOT NULL,\n stop_loss REAL NOT NULL,\n take_profit1 REAL NOT NULL,\n take_profit2 REAL NOT NULL,\n rr REAL NOT NULL,\n confidence INTEGER NOT NULL,\n reason TEXT NOT NULL,\n timeframe TEXT NOT NULL,\n status TEXT NOT NULL DEFAULT 'open',\n pnl REAL,\n opened_at INTEGER NOT NULL,\n closed_at INTEGER,\n poi_type TEXT,\n trend_type TEXT,\n liquidity TEXT\n )`);\n await sqlite.execute(`INSERT OR IGNORE INTO strategy_wallet (id) VALUES (1)`);\n await initLearningDb();\n\n // Migration: add learning-feature columns if this table was created by an\n // older version of the bot (ALTER TABLE fails if the column already exists,\n // so each is wrapped and ignored).\n const migs = [\n \"ALTER TABLE strategy_trades ADD COLUMN poi_type TEXT\",\n \"ALTER TABLE strategy_trades ADD COLUMN trend_type TEXT\",\n \"ALTER TABLE strategy_trades ADD COLUMN liquidity TEXT\",\n ];\n for (const m of migs) {\n try {\n await sqlite.execute(m);\n } catch {\n // column already exists — fine\n }\n }\n}\n\nexport async function getStrategyWallet() {\n const r = await sqlite.execute(\"SELECT * FROM strategy_wallet WHERE id = 1\");\n return r.rows[0] as unknown as {\n balance: number;\n initial_balance: number;\n open_positions: number;\n };\n}\n\nexport async function openTrade(sig: Signal) {\n await sqlite.execute(\n `INSERT INTO strategy_trades\n (symbol, side, entry, stop_loss, take_profit1, take_profit2, rr, confidence,\n reason, timeframe, opened_at, poi_type, trend_type, liquidity)\n VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)`,\n [sig.symbol, sig.side, sig.entry, sig.stopLoss, sig.takeProfit1,\n sig.takeProfit2, sig.rr, sig.confidence, sig.reason, sig.timeframe, sig.timestamp,\n sig.poiType, sig.trendType, sig.liquidity],\n );\n await sqlite.execute(\n \"UPDATE strategy_wallet SET open_positions = open_positions + 1 WHERE id = 1\",\n );\n}\n\n// Close any open positions that have hit SL / TP1 / TP2 based on the latest price.\n// Also feeds every outcome into the adaptive learning system.\nexport async function manageOpenPositions(): Promise {\n const res = await sqlite.execute(\n \"SELECT * FROM strategy_trades WHERE status = 'open'\",\n );\n const open = res.rows as unknown as {\n id: number;\n symbol: string;\n side: string;\n entry: number;\n stop_loss: number;\n take_profit1: number;\n take_profit2: number;\n opened_at: number;\n poi_type: string;\n trend_type: string;\n liquidity: string;\n }[];\n\n let closed = 0;\n for (const t of open) {\n let price: number;\n try {\n const k = await fetchKlines(t.symbol, \"15m\", 1);\n price = k[0].close;\n } catch {\n continue;\n }\n let status: string | null = null;\n let pnlPct = 0;\n if (t.side === \"LONG\") {\n if (price >= t.take_profit2) { status = \"tp2\"; pnlPct = 2; }\n else if (price >= t.take_profit1) { status = \"tp1\"; pnlPct = 1; }\n else if (price <= t.stop_loss) { status = \"sl\"; pnlPct = -1; }\n } else {\n if (price <= t.take_profit2) { status = \"tp2\"; pnlPct = 2; }\n else if (price <= t.take_profit1) { status = \"tp1\"; pnlPct = 1; }\n else if (price >= t.stop_loss) { status = \"sl\"; pnlPct = -1; }\n }\n if (status) {\n await sqlite.execute(\n `UPDATE strategy_trades SET status = ?, pnl = ?, closed_at = ? WHERE id = ?`,\n [status, pnlPct, Date.now(), t.id],\n );\n await sqlite.execute(\n `UPDATE strategy_wallet SET open_positions = open_positions - 1,\n balance = balance + ? WHERE id = 1`,\n [100 * (pnlPct / 100)],\n );\n // Teach the learning system: a win is reaching at least TP1.\n const won = status === \"tp1\" || status === \"tp2\";\n await teachResult({\n poiType: t.poi_type ?? \"OB\",\n trendType: t.trend_type ?? \"bullish\",\n liquidity: t.liquidity ?? \"MEDIUM\",\n side: t.side,\n }, won);\n // Bump the closed-trade counter used to arm the learned gate.\n await sqlite.execute(\n `INSERT INTO strategy_meta (key, value) VALUES ('closed_trades', 1)\n ON CONFLICT(key) DO UPDATE SET value = value + 1`,\n );\n closed++;\n }\n }\n return closed;\n}\n\n// ============================================================\n// Adaptive Learning System\n// ------------------------------------------------------------\n// Learns from every closed trade. Each trade is tagged with its\n// factors (poi type, trend, liquidity, side). After we accumulate\n// enough samples, we compute a historical win-rate for each factor\n// and derive an acceptance gate: a new signal is opened only if its\n// weighted win probability clears the dynamic threshold.\n// ============================================================\n\n// Minimum closed trades before the learned gate kicks in.\nexport const LEARN_MIN_SAMPLES = 15;\n// How much a factor's positive history must beat break-even to be\n// considered an edge. With a ~1:1 to 1:2 payoff we want win rate > ~40%.\nconst WIN_RATE_FLOOR = 0.40;\n\nexport async function initLearningDb() {\n await sqlite.execute(`CREATE TABLE IF NOT EXISTS strategy_factors (\n factor TEXT PRIMARY KEY,\n wins INTEGER NOT NULL DEFAULT 0,\n losses INTEGER NOT NULL DEFAULT 0\n )`);\n await sqlite.execute(`CREATE TABLE IF NOT EXISTS strategy_meta (\n key TEXT PRIMARY KEY,\n value REAL\n )`);\n}\n\n// Record a win/loss against each factor tag of a trade.\nexport async function teachResult(\n features: { poiType: string; trendType: string; liquidity: string; side: string },\n won: boolean,\n) {\n const factors = [\n `poi:${features.poiType}`,\n `trend:${features.trendType}`,\n `liq:${features.liquidity}`,\n `side:${features.side}`,\n ];\n const res = await sqlite.execute(\n `SELECT factor FROM strategy_factors WHERE factor IN (${factors.map(() => \"?\").join(\",\")})`,\n factors,\n );\n const existing = new Set((res.rows as { factor: string }[]).map((r) => r.factor));\n const upserts = factors.filter((f) => !existing.has(f)).map((f) => [f]);\n if (upserts.length) {\n await sqlite.batch(upserts.map(([f]) => ({\n sql: `INSERT OR IGNORE INTO strategy_factors (factor, wins, losses) VALUES (?, 0, 0)`,\n args: [f],\n })));\n }\n const col = won ? \"wins\" : \"losses\";\n await sqlite.batch(factors.map((f) => ({\n sql: `UPDATE strategy_factors SET ${col} = ${col} + 1 WHERE factor = ?`,\n args: [f],\n })));\n}\n\n// Win rate for a single factor (0..1), or null if too few samples.\nasync function factorWinRate(factor: string): Promise {\n const r = await sqlite.execute(\n \"SELECT wins, losses FROM strategy_factors WHERE factor = ?\",\n [factor],\n );\n const row = r.rows[0] as { wins: number; losses: number } | undefined;\n if (!row) return null;\n const total = row.wins + row.losses;\n if (total < LEARN_MIN_SAMPLES) return null;\n return row.wins / total;\n}\n\n// Historical edge adjustment: returns a delta in confidence points built from\n// how each factor of the signal has performed historically.\nexport async function learnScoreAdjustment(sig: {\n poiType: string;\n trendType: string;\n liquidity: string;\n side: string;\n}): Promise {\n const factors = [\n `poi:${sig.poiType}`,\n `trend:${sig.trendType}`,\n `liq:${sig.liquidity}`,\n `side:${sig.side}`,\n ];\n let known = 0;\n let edge = 0;\n for (const f of factors) {\n const wr = await factorWinRate(f);\n if (wr === null) continue; // not enough data yet — no signal\n known++;\n // Edge is how far this factor's historical win rate beats the floor.\n edge += wr - WIN_RATE_FLOOR;\n }\n if (known === 0) return 0; // still in the learning/warm-up phase\n // Map cumulative edge into a bounded confidence-points offset.\n return Math.max(-15, Math.min(15, edge * 15));\n}\n\n// Adaptive acceptance decision. After enough history, reject a signal whose\n// learned win probability (adjusted confidence) fails the dynamic floor.\nexport async function shouldAccept(sig: {\n confidence: number;\n poiType: string;\n trendType: string;\n liquidity: string;\n side: string;\n}): Promise<{ accept: boolean; adjusted: number; reason: string }> {\n const adjustment = await learnScoreAdjustment(sig);\n const adjusted = sig.confidence + adjustment;\n\n // How many closed trades have we learned from so far?\n const meta = await sqlite.execute(\n \"SELECT value FROM strategy_meta WHERE key = 'closed_trades'\",\n );\n const closed = (meta.rows[0] as { value: number } | undefined)?.value ?? 0;\n\n if (closed < LEARN_MIN_SAMPLES) {\n // Warm-up: accept everything that already passed technical gates; the\n // learning gate engages once we have enough outcomes.\n return { accept: true, adjusted, reason: \"warm-up\" };\n }\n\n // Dynamic threshold: learned win probability must beat break-even.\n // With payoff ~1:1 to 1:2, we require adjusted confidence >= 55.\n const threshold = 55;\n if (adjusted >= threshold) {\n return { accept: true, adjusted, reason: `learned-edge (+${adjustment.toFixed(1)})` };\n }\n return { accept: false, adjusted, reason: `weak-history (${adjusted.toFixed(0)}<${threshold})` };\n}\n","// ============================================================\n// Multi-Timeframe Technical Analysis Strategy Engine\n// Systematic Trading Strategy (ICT / SMC Hybrid)\n// ------------------------------------------------------------\n// Pipeline:\n// 1. Trend bias on 4H (EMA50/EMA200 + swing structure)\n// 2. POI detection on 1H/15M (Order Blocks + FVG)\n// 3. Execution trigger on 5M (reversal candle at POI retest)\n// 4. Risk management (SL behind structure, TP1/TP2 at 1:1 / 1:2)\n// 5. Adaptive learning from closed-trade outcomes\n// ============================================================\n\nimport { sqlite } from \"https://esm.town/v/std/sqlite/main.ts\";\n\nconst BINANCE = \"https://data-api.binance.vision/api/v3\";\n\n// ---------- Types ----------\nexport interface Candle {\n time: number;\n open: number;\n high: number;\n low: number;\n close: number;\n volume: number;\n}\n\nexport type Side = \"LONG\" | \"SHORT\";\n\nexport interface Signal {\n symbol: string;\n side: Side;\n entry: number;\n stopLoss: number;\n takeProfit1: number;\n takeProfit2: number;\n rr: number; // reward:risk to TP2\n confidence: number; // 0-100 confluence score\n reason: string;\n timeframe: string; // POI timeframe\n timestamp: number;\n // Learning features (used to estimate historical win probability).\n poiType: \"OB\" | \"FVG\";\n trendType: Trend;\n liquidity: \"HIGH\" | \"MEDIUM\";\n}\n\nexport interface LiquidityCoin {\n symbol: string; // e.g. \"BTCUSDT\"\n base: string; // e.g. \"BTC\"\n quoteVolume: number; // 24h USDT volume\n liquidity: \"HIGH\" | \"MEDIUM\";\n}\n\n// ---------- Config ----------\nexport const LIQUIDITY_HIGH_MIN = 50_000_000; // >= $50M/day => HIGH\nexport const LIQUIDITY_MEDIUM_MIN = 5_000_000; // >= $5M/day => MEDIUM\nexport const MAX_COINS_PER_SCAN = 80;\nconst STABLE_BASES = new Set([\n \"USDT\", \"USDC\", \"BUSD\", \"DAI\", \"TUSD\", \"FDUSD\", \"USDP\", \"EUR\", \"AEUR\",\n \"USTC\", \"USDD\", \"PYUSD\", \"EURI\", \"XUSD\", \"USD1\",\n]);\n\n// ---------- Binance data ----------\nexport async function fetchKlines(\n symbol: string,\n interval: string,\n limit = 200,\n): Promise {\n const res = await fetch(\n `${BINANCE}/klines?symbol=${symbol}&interval=${interval}&limit=${limit}`,\n );\n if (!res.ok) throw new Error(`klines ${symbol} ${interval} ${res.status}`);\n const raw = (await res.json()) as unknown[][];\n return raw.map((k) => ({\n time: k[0] as number,\n open: parseFloat(k[1] as string),\n high: parseFloat(k[2] as string),\n low: parseFloat(k[3] as string),\n close: parseFloat(k[4] as string),\n volume: parseFloat(k[5] as string),\n }));\n}\n\n// ---------- Liquidity universe ----------\n// Scan medium & high liquidity coins from the 24h ticker.\nexport async function getLiquidCoins(): Promise {\n const res = await fetch(`${BINANCE}/ticker/24hr`);\n if (!res.ok) throw new Error(`24hr ${res.status}`);\n const all = (await res.json()) as {\n symbol: string;\n quoteVolume: string;\n }[];\n\n const coins: LiquidityCoin[] = [];\n for (const t of all) {\n if (!t.symbol.endsWith(\"USDT\")) continue;\n const base = t.symbol.slice(0, -4);\n if (STABLE_BASES.has(base)) continue;\n const qv = parseFloat(t.quoteVolume);\n if (!Number.isFinite(qv) || qv <= 0) continue;\n if (qv >= LIQUIDITY_HIGH_MIN) {\n coins.push({ symbol: t.symbol, base, quoteVolume: qv, liquidity: \"HIGH\" });\n } else if (qv >= LIQUIDITY_MEDIUM_MIN) {\n coins.push({ symbol: t.symbol, base, quoteVolume: qv, liquidity: \"MEDIUM\" });\n }\n }\n\n // Sort by volume desc, take the top N (mix of medium & high).\n coins.sort((a, b) => b.quoteVolume - a.quoteVolume);\n return coins.slice(0, MAX_COINS_PER_SCAN);\n}\n\n// ---------- Indicators ----------\nexport function ema(values: number[], period: number): number[] {\n const k = 2 / (period + 1);\n const out: number[] = [];\n let prev = values[0];\n for (let i = 0; i < values.length; i++) {\n if (i === 0) {\n out.push(values[0]);\n prev = values[0];\n } else {\n prev = values[i] * k + prev * (1 - k);\n out.push(prev);\n }\n }\n return out;\n}\n\nexport function atr(candles: Candle[], period = 14): number {\n if (candles.length < period + 1) return 0;\n let sum = 0;\n for (let i = 1; i <= period; i++) {\n const c = candles[i];\n const p = candles[i - 1];\n const tr = Math.max(\n c.high - c.low,\n Math.abs(c.high - p.close),\n Math.abs(c.low - p.close),\n );\n sum += tr;\n }\n return sum / period;\n}\n\n// ---------- Swing structure ----------\ninterface Swing {\n index: number;\n price: number;\n type: \"high\" | \"low\";\n}\n\n// Fractal swing detection: a candle is a swing high if its high is the max of\n// the surrounding `left` candles on each side.\nexport function findSwings(candles: Candle[], left = 2): Swing[] {\n const swings: Swing[] = [];\n for (let i = left; i < candles.length - left; i++) {\n let isHigh = true;\n let isLow = true;\n for (let j = i - left; j <= i + left; j++) {\n if (j === i) continue;\n if (candles[j].high >= candles[i].high) isHigh = false;\n if (candles[j].low <= candles[i].low) isLow = false;\n }\n if (isHigh) swings.push({ index: i, price: candles[i].high, type: \"high\" });\n if (isLow) swings.push({ index: i, price: candles[i].low, type: \"low\" });\n }\n return swings;\n}\n\nexport type Trend = \"bullish\" | \"bearish\" | \"range\";\n\n// Trend bias from EMA50/EMA200 on the last candle + swing structure.\nexport function detectTrend(candles: Candle[]): Trend {\n if (candles.length < 200) return \"range\";\n const closes = candles.map((c) => c.close);\n const e50 = ema(closes, 50);\n const e200 = ema(closes, 200);\n const last = closes.length - 1;\n const price = closes[last];\n\n const swings = findSwings(candles, 3).filter((s) => s.index > last - 60);\n const highs = swings.filter((s) => s.type === \"high\");\n const lows = swings.filter((s) => s.type === \"low\");\n\n // Higher highs & higher lows => bullish; lower highs & lower lows => bearish.\n let hh = 0, hl = 0, lh = 0, ll = 0;\n for (let i = 1; i < highs.length; i++) {\n if (highs[i].price > highs[i - 1].price) hh++; else lh++;\n }\n for (let i = 1; i < lows.length; i++) {\n if (lows[i].price > lows[i - 1].price) hl++; else ll++;\n }\n\n const emaBull = price > e50[last] && e50[last] > e200[last];\n const emaBear = price < e50[last] && e50[last] < e200[last];\n\n // Strong sustained trend: EMA alignment confirmed by structure count.\n if (emaBull && hh + hl >= lh + ll - 2) return \"bullish\";\n if (emaBear && lh + ll >= hh + hl - 2) return \"bearish\";\n\n // Weaker but still directional: rely on EMA alignment alone.\n if (emaBull) return \"bullish\";\n if (emaBear) return \"bearish\";\n\n return \"range\";\n}\n\n// ---------- POI detection ----------\nexport interface POI {\n type: \"OB\" | \"FVG\";\n side: Side;\n top: number;\n bottom: number;\n index: number;\n}\n\n// Order Block: the last opposite candle before a strong directional move.\nexport function findOrderBlocks(candles: Candle[], lookback = 60): POI[] {\n const pois: POI[] = [];\n const start = Math.max(1, candles.length - lookback);\n for (let i = start; i < candles.length - 1; i++) {\n const move = candles[i + 1].close - candles[i].close;\n const body = Math.abs(candles[i].close - candles[i].open);\n // Strong up move preceded by a bearish candle => demand OB (LONG)\n if (move > 0 && candles[i].close < candles[i].open && body > 0) {\n pois.push({\n type: \"OB\",\n side: \"LONG\",\n top: Math.max(candles[i].open, candles[i].close),\n bottom: Math.min(candles[i].open, candles[i].close),\n index: i,\n });\n }\n // Strong down move preceded by a bullish candle => supply OB (SHORT)\n if (move < 0 && candles[i].close > candles[i].open && body > 0) {\n pois.push({\n type: \"OB\",\n side: \"SHORT\",\n top: Math.max(candles[i].open, candles[i].close),\n bottom: Math.min(candles[i].open, candles[i].close),\n index: i,\n });\n }\n }\n return pois;\n}\n\n// Fair Value Gap: gap between candle[i-1] and candle[i+1].\nexport function findFVG(candles: Candle[], lookback = 60): POI[] {\n const pois: POI[] = [];\n const start = Math.max(1, candles.length - lookback);\n for (let i = start; i < candles.length - 1; i++) {\n const prev = candles[i - 1];\n const next = candles[i + 1];\n // Bullish FVG: next.low > prev.high\n if (next.low > prev.high) {\n pois.push({\n type: \"FVG\",\n side: \"LONG\",\n top: next.low,\n bottom: prev.high,\n index: i,\n });\n }\n // Bearish FVG: next.high < prev.low\n if (next.high < prev.low) {\n pois.push({\n type: \"FVG\",\n side: \"SHORT\",\n top: prev.low,\n bottom: next.high,\n index: i,\n });\n }\n }\n return pois;\n}\n\n// ---------- Reversal candle detection (execution trigger) ----------\n// Returns true if the last candle is a valid reversal trigger for `side`.\nexport function isReversalTrigger(candles: Candle[], side: Side): boolean {\n if (candles.length < 3) return false;\n const c = candles[candles.length - 1];\n const p = candles[candles.length - 2];\n const body = Math.abs(c.close - c.open);\n const range = c.high - c.low;\n if (range === 0) return false;\n const lowerWick = Math.min(c.open, c.close) - c.low;\n const upperWick = c.high - Math.max(c.open, c.close);\n\n if (side === \"LONG\") {\n // Hammer: small body near top, long lower wick (>= 2x body)\n const hammer = body > 0 && lowerWick >= 2 * body && upperWick <= body;\n // Bullish engulfing: green candle engulfs previous red body\n const engulf = c.close > c.open && p.close < p.open &&\n c.close >= p.open && c.open <= p.close;\n return hammer || engulf;\n } else {\n // Hanging man: small body, long lower wick at top of range (bearish)\n const hangingMan = body > 0 && lowerWick >= 2 * body && c.close >= p.close;\n // Bearish engulfing\n const engulf = c.close < c.open && p.close > p.open &&\n c.close <= p.open && c.open >= p.close;\n return hangingMan || engulf;\n }\n}\n\n// ---------- Full signal generation for one coin ----------\nexport async function generateSignal(\n symbol: string,\n liquidity: \"HIGH\" | \"MEDIUM\" = \"MEDIUM\",\n): Promise {\n // Fetch the three timeframes we need.\n const [c4h, c1h, c5m] = await Promise.all([\n fetchKlines(symbol, \"4h\", 250),\n fetchKlines(symbol, \"1h\", 100),\n fetchKlines(symbol, \"5m\", 80),\n ]);\n\n const trend = detectTrend(c4h);\n if (trend === \"range\") return null; // no bias => no trade\n\n const last4h = c4h[c4h.length - 1];\n const price = last4h.close;\n const a = atr(c4h, 14);\n if (a <= 0) return null;\n\n // POIs from 1H (medium timeframe).\n const ob = findOrderBlocks(c1h, 60);\n const fvg = findFVG(c1h, 60);\n const pois = [...ob, ...fvg].filter((p) => p.index > c1h.length - 40);\n\n // Pick the side aligned with the trend.\n const side: Side = trend === \"bullish\" ? \"LONG\" : \"SHORT\";\n\n // Find a POI in the direction of the trend whose zone the price is near.\n const zone = pois\n .filter((p) => p.side === side)\n .map((p) => ({ ...p, dist: Math.abs(price - (p.top + p.bottom) / 2) }))\n .sort((x, y) => x.dist - y.dist)[0];\n\n if (!zone) return null;\n\n // Execution: price must be inside / at the POI zone AND a reversal trigger\n // must appear on 5M (retest confirmation).\n const inZone = price >= zone.bottom * 0.995 && price <= zone.top * 1.005;\n const trigger = isReversalTrigger(c5m, side);\n if (!inZone || !trigger) return null;\n\n // ---- Risk management ----\n // SL behind the nearest swing structure beyond the zone.\n const swings = findSwings(c4h, 2).filter((s) => s.index > c4h.length - 60);\n let stopLoss: number;\n if (side === \"LONG\") {\n const support = swings.filter((s) => s.type === \"low\" && s.price < zone.bottom)\n .sort((x, y) => y.price - x.price)[0];\n stopLoss = support ? support.price : zone.bottom - a * 0.5;\n stopLoss -= a * 0.1; // buffer vs stop hunts\n } else {\n const resistance = swings.filter((s) => s.type === \"high\" && s.price > zone.top)\n .sort((x, y) => x.price - y.price)[0];\n stopLoss = resistance ? resistance.price : zone.top + a * 0.5;\n stopLoss += a * 0.1;\n }\n\n const risk = Math.abs(price - stopLoss);\n if (risk <= 0) return null;\n\n // TP1 = 1:1, TP2 = 1:2 (measured move / R:R targets).\n let tp1: number, tp2: number;\n if (side === \"LONG\") {\n tp1 = price + risk;\n tp2 = price + risk * 2;\n } else {\n tp1 = price - risk;\n tp2 = price - risk * 2;\n }\n\n // R:R gate: reject trades below 1:2 to TP2.\n const rr = (Math.abs(tp2 - price)) / risk;\n if (rr < 2) return null;\n\n // Confluence score.\n let confidence = 40;\n if (zone.type === \"OB\") confidence += 15;\n if (ob.some((o) => o.side === side && o.index > c1h.length - 30)) confidence += 10;\n if (trend !== \"range\") confidence += 15;\n if (isReversalTrigger(c5m, side)) confidence += 10;\n\n return {\n symbol,\n side,\n entry: price,\n stopLoss,\n takeProfit1: tp1,\n takeProfit2: tp2,\n rr,\n confidence,\n reason: `${trend} trend | ${zone.type} ${side} @ ${zone.top.toFixed(6)}-${zone.bottom.toFixed(6)} | 5m trigger`,\n timeframe: \"1H\",\n timestamp: Date.now(),\n poiType: zone.type,\n trendType: trend,\n liquidity,\n };\n}\n\n// ---------- DB ----------\nexport async function initStrategyDb() {\n await sqlite.execute(`CREATE TABLE IF NOT EXISTS strategy_wallet (\n id INTEGER PRIMARY KEY CHECK (id = 1),\n balance REAL NOT NULL DEFAULT 1000.0,\n initial_balance REAL NOT NULL DEFAULT 1000.0,\n open_positions INTEGER NOT NULL DEFAULT 0\n )`);\n await sqlite.execute(`CREATE TABLE IF NOT EXISTS strategy_trades (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n symbol TEXT NOT NULL,\n side TEXT NOT NULL,\n entry REAL NOT NULL,\n stop_loss REAL NOT NULL,\n take_profit1 REAL NOT NULL,\n take_profit2 REAL NOT NULL,\n rr REAL NOT NULL,\n confidence INTEGER NOT NULL,\n reason TEXT NOT NULL,\n timeframe TEXT NOT NULL,\n status TEXT NOT NULL DEFAULT 'open',\n pnl REAL,\n opened_at INTEGER NOT NULL,\n closed_at INTEGER,\n poi_type TEXT,\n trend_type TEXT,\n liquidity TEXT\n )`);\n await sqlite.execute(`INSERT OR IGNORE INTO strategy_wallet (id) VALUES (1)`);\n await initLearningDb();\n\n // Migration: add learning-feature columns if this table was created by an\n // older version of the bot (ALTER TABLE fails if the column already exists,\n // so each is wrapped and ignored).\n const migs = [\n \"ALTER TABLE strategy_trades ADD COLUMN poi_type TEXT\",\n \"ALTER TABLE strategy_trades ADD COLUMN trend_type TEXT\",\n \"ALTER TABLE strategy_trades ADD COLUMN liquidity TEXT\",\n ];\n for (const m of migs) {\n try {\n await sqlite.execute(m);\n } catch {\n // column already exists — fine\n }\n }\n}\n\nexport async function getStrategyWallet() {\n const r = await sqlite.execute(\"SELECT * FROM strategy_wallet WHERE id = 1\");\n return r.rows[0] as unknown as {\n balance: number;\n initial_balance: number;\n open_positions: number;\n };\n}\n\nexport async function openTrade(sig: Signal) {\n await sqlite.execute(\n `INSERT INTO strategy_trades\n (symbol, side, entry, stop_loss, take_profit1, take_profit2, rr, confidence,\n reason, timeframe, opened_at, poi_type, trend_type, liquidity)\n VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)`,\n [sig.symbol, sig.side, sig.entry, sig.stopLoss, sig.takeProfit1,\n sig.takeProfit2, sig.rr, sig.confidence, sig.reason, sig.timeframe, sig.timestamp,\n sig.poiType, sig.trendType, sig.liquidity],\n );\n await sqlite.execute(\n \"UPDATE strategy_wallet SET open_positions = open_positions + 1 WHERE id = 1\",\n );\n}\n\n// Close any open positions that have hit SL / TP1 / TP2 based on the latest price.\n// Also feeds every outcome into the adaptive learning system.\nexport async function manageOpenPositions(): Promise {\n const res = await sqlite.execute(\n \"SELECT * FROM strategy_trades WHERE status = 'open'\",\n );\n const open = res.rows as unknown as {\n id: number;\n symbol: string;\n side: string;\n entry: number;\n stop_loss: number;\n take_profit1: number;\n take_profit2: number;\n opened_at: number;\n poi_type: string;\n trend_type: string;\n liquidity: string;\n }[];\n\n let closed = 0;\n for (const t of open) {\n let price: number;\n try {\n const k = await fetchKlines(t.symbol, \"15m\", 1);\n price = k[0].close;\n } catch {\n continue;\n }\n let status: string | null = null;\n let pnlPct = 0;\n if (t.side === \"LONG\") {\n if (price >= t.take_profit2) { status = \"tp2\"; pnlPct = 2; }\n else if (price >= t.take_profit1) { status = \"tp1\"; pnlPct = 1; }\n else if (price <= t.stop_loss) { status = \"sl\"; pnlPct = -1; }\n } else {\n if (price <= t.take_profit2) { status = \"tp2\"; pnlPct = 2; }\n else if (price <= t.take_profit1) { status = \"tp1\"; pnlPct = 1; }\n else if (price >= t.stop_loss) { status = \"sl\"; pnlPct = -1; }\n }\n if (status) {\n await sqlite.execute(\n `UPDATE strategy_trades SET status = ?, pnl = ?, closed_at = ? WHERE id = ?`,\n [status, pnlPct, Date.now(), t.id],\n );\n await sqlite.execute(\n `UPDATE strategy_wallet SET open_positions = open_positions - 1,\n balance = balance + ? WHERE id = 1`,\n [100 * (pnlPct / 100)],\n );\n // Teach the learning system: a win is reaching at least TP1.\n const won = status === \"tp1\" || status === \"tp2\";\n await teachResult({\n poiType: t.poi_type ?? \"OB\",\n trendType: t.trend_type ?? \"bullish\",\n liquidity: t.liquidity ?? \"MEDIUM\",\n side: t.side,\n }, won);\n // Bump the closed-trade counter used to arm the learned gate.\n await sqlite.execute(\n `INSERT INTO strategy_meta (key, value) VALUES ('closed_trades', 1)\n ON CONFLICT(key) DO UPDATE SET value = value + 1`,\n );\n closed++;\n }\n }\n return closed;\n}\n\n// ============================================================\n// Adaptive Learning System\n// ------------------------------------------------------------\n// Learns from every closed trade. Each trade is tagged with its\n// factors (poi type, trend, liquidity, side). After we accumulate\n// enough samples, we compute a historical win-rate for each factor\n// and derive an acceptance gate: a new signal is opened only if its\n// weighted win probability clears the dynamic threshold.\n// ============================================================\n\n// Minimum closed trades before the learned gate kicks in.\nexport const LEARN_MIN_SAMPLES = 15;\n// How much a factor's positive history must beat break-even to be\n// considered an edge. With a ~1:1 to 1:2 payoff we want win rate > ~40%.\nconst WIN_RATE_FLOOR = 0.40;\n\nexport async function initLearningDb() {\n await sqlite.execute(`CREATE TABLE IF NOT EXISTS strategy_factors (\n factor TEXT PRIMARY KEY,\n wins INTEGER NOT NULL DEFAULT 0,\n losses INTEGER NOT NULL DEFAULT 0\n )`);\n await sqlite.execute(`CREATE TABLE IF NOT EXISTS strategy_meta (\n key TEXT PRIMARY KEY,\n value REAL\n )`);\n}\n\n// Record a win/loss against each factor tag of a trade.\nexport async function teachResult(\n features: { poiType: string; trendType: string; liquidity: string; side: string },\n won: boolean,\n) {\n const factors = [\n `poi:${features.poiType}`,\n `trend:${features.trendType}`,\n `liq:${features.liquidity}`,\n `side:${features.side}`,\n ];\n const res = await sqlite.execute(\n `SELECT factor FROM strategy_factors WHERE factor IN (${factors.map(() => \"?\").join(\",\")})`,\n factors,\n );\n const existing = new Set((res.rows as { factor: string }[]).map((r) => r.factor));\n const upserts = factors.filter((f) => !existing.has(f)).map((f) => [f]);\n if (upserts.length) {\n await sqlite.batch(upserts.map(([f]) => ({\n sql: `INSERT OR IGNORE INTO strategy_factors (factor, wins, losses) VALUES (?, 0, 0)`,\n args: [f],\n })));\n }\n const col = won ? \"wins\" : \"losses\";\n await sqlite.batch(factors.map((f) => ({\n sql: `UPDATE strategy_factors SET ${col} = ${col} + 1 WHERE factor = ?`,\n args: [f],\n })));\n}\n\n// Win rate for a single factor (0..1), or null if too few samples.\nasync function factorWinRate(factor: string): Promise {\n const r = await sqlite.execute(\n \"SELECT wins, losses FROM strategy_factors WHERE factor = ?\",\n [factor],\n );\n const row = r.rows[0] as { wins: number; losses: number } | undefined;\n if (!row) return null;\n const total = row.wins + row.losses;\n if (total < LEARN_MIN_SAMPLES) return null;\n return row.wins / total;\n}\n\n// Historical edge adjustment: returns a delta in confidence points built from\n// how each factor of the signal has performed historically.\nexport async function learnScoreAdjustment(sig: {\n poiType: string;\n trendType: string;\n liquidity: string;\n side: string;\n}): Promise {\n const factors = [\n `poi:${sig.poiType}`,\n `trend:${sig.trendType}`,\n `liq:${sig.liquidity}`,\n `side:${sig.side}`,\n ];\n let known = 0;\n let edge = 0;\n for (const f of factors) {\n const wr = await factorWinRate(f);\n if (wr === null) continue; // not enough data yet — no signal\n known++;\n // Edge is how far this factor's historical win rate beats the floor.\n edge += wr - WIN_RATE_FLOOR;\n }\n if (known === 0) return 0; // still in the learning/warm-up phase\n // Map cumulative edge into a bounded confidence-points offset.\n return Math.max(-15, Math.min(15, edge * 15));\n}\n\n// Adaptive acceptance decision. After enough history, reject a signal whose\n// learned win probability (adjusted confidence) fails the dynamic floor.\nexport async function shouldAccept(sig: {\n confidence: number;\n poiType: string;\n trendType: string;\n liquidity: string;\n side: string;\n}): Promise<{ accept: boolean; adjusted: number; reason: string }> {\n const adjustment = await learnScoreAdjustment(sig);\n const adjusted = sig.confidence + adjustment;\n\n // How many closed trades have we learned from so far?\n const meta = await sqlite.execute(\n \"SELECT value FROM strategy_meta WHERE key = 'closed_trades'\",\n );\n const closed = (meta.rows[0] as { value: number } | undefined)?.value ?? 0;\n\n if (closed < LEARN_MIN_SAMPLES) {\n // Warm-up: accept everything that already passed technical gates; the\n // learning gate engages once we have enough outcomes.\n return { accept: true, adjusted, reason: \"warm-up\" };\n }\n\n // Dynamic threshold: learned win probability must beat break-even.\n // With payoff ~1:1 to 1:2, we require adjusted confidence >= 55.\n const threshold = 55;\n if (adjusted >= threshold) {\n return { accept: true, adjusted, reason: `learned-edge (+${adjustment.toFixed(1)})` };\n }\n return { accept: false, adjusted, reason: `weak-history (${adjusted.toFixed(0)}<${threshold})` };\n}\n// ---------- Learning report ----------\n// Build a human-readable summary of what the bot has learned so far:\n// the accumulated closed-trade count and the win rate of every tracked factor.\nexport async function buildLearningReport(): Promise {\n const meta = await sqlite.execute(\n \"SELECT value FROM strategy_meta WHERE key = 'closed_trades'\",\n );\n const closed = (meta.rows[0] as { value: number } | undefined)?.value ?? 0;\n\n const factorsRes = await sqlite.execute(\n \"SELECT factor, wins, losses FROM strategy_factors ORDER BY factor\",\n );\n const factors = factorsRes.rows as unknown as {\n factor: string;\n wins: number;\n losses: number;\n }[];\n\n const lines = [\n \"🧠 Strategy Learning Report\",\n \"📥 Closed trades: \" + closed + \"\",\n \"\",\n ];\n if (factors.length === 0) {\n lines.push(\"No closed trades to learn from yet.\");\n } else {\n lines.push(\"Per-factor win rates:\");\n for (const f of factors) {\n const total = f.wins + f.losses;\n const wr = total > 0 ? ((f.wins / total) * 100).toFixed(0) : \"0\";\n const bar = total > 0 ? \"█\".repeat(Math.min(10, Math.round((f.wins / total) * 10))) : \"\";\n const state = total >= LEARN_MIN_SAMPLES ? \"\" : \" (warm-up)\";\n lines.push(\n `• ${f.factor} — ${wr}% (${f.wins}W/${f.losses}L) ${bar}${state}`,\n );\n }\n }\n if (closed >= LEARN_MIN_SAMPLES) {\n lines.push(\"\");\n lines.push(\"✅ Learned gate active — filtering weak signals.\");\n } else {\n lines.push(\"\");\n lines.push(`⏳ Learned gate arms after ${LEARN_MIN_SAMPLES - closed} more closed trades.`);\n }\n return lines.join(\"\\n\");\n}\n","createdAt",["D",1788375410196],"branchVersion","branchId","706a186d-17b0-49d7-afcd-061e66763458","id","4656a5b0-561d-4ff3-812e-b3949dee3507","binance-arb-paper-bot",["D",1788372422505],"deletedAt","legacyEnvVars","privacy","public","httpPrivacy","description","Binance triangular arbitrage paper bot","imageUrl","a79beaf8-5d23-4a58-a785-4a70bab11255","pinnedFileId","01a0634d-9e20-77ee-876e-733794a7e2e2","pinPreview",true,"pinMigrated","screenshotVersion","likeCount",0,"orgId","01a0634a-0ca9-7584-9a97-35a1aeb44011","tursoName","4656a5b0561d4ff3812eb3949dee3507","tursoHostname","4656a5b0561d4ff3812eb3949dee3507-val-town.aws-us-east-2.turso.io","tursoMeasuredAt",["D",1789588306543],"pinned","userPerm","r","valPerm","blobPerm","rw","emailPerm","sqlitePerm","projectPerm","telemetryPerm","user",{"_106":105,"_113":108},"org",{"_106":105,"_107":108,"_109":108,"_19":61,"_110":111,"_112":31},"branch",{"_13":95,"_38":96,"_49":48,"_97":50,"_98":-5,"_99":-5,"_100":-5,"_101":-5,"_102":-5,"_103":51,"_104":105},"isOwned",78,"main","projectId","forkedBranchId","forkedBranchVersion","forkedProjectPrivacy","forkedProjectId","forkedProjectOrgId","projectName","userHandle","Tringelar","handle","displayName","","avatar","tier","free","canRestrictVals","profileImageUrl","branchName","adoptOrgId",{"_49":50,"_38":51,"_44":212,"_53":-5,"_54":31,"_55":56,"_57":56,"_58":59,"_60":-5,"_19":61,"_62":63,"_64":65,"_66":-5,"_67":-5,"_68":69,"_70":71,"_72":73,"_74":75,"_76":213,"_78":-5,"_79":80,"_81":80,"_82":83,"_84":83,"_85":83,"_86":80,"_87":80,"_88":214,"_90":215,"_92":216,"_94":31},"tree",[191,192,193,194,195],"files",[191,192,193,194,195],"counts",{"_180":69,"_181":146,"_182":69,"_183":184,"_185":186},"contents",[135,136,137,138,139],"upstreamProject","upstreamBranch","emptyEnvVars","flags",[],"hasHttpFiles","grants","orgs","customDomains",{},{"_49":173,"_38":174,"_36":175,"_142":-5,"_47":48,"_46":176,"_144":65,"_44":177,"_13":161,"_39":26,"_41":178,"_30":31,"_149":-5,"_33":179},{"_49":165,"_38":166,"_36":167,"_142":-5,"_47":48,"_46":168,"_144":65,"_44":169,"_13":170,"_39":162,"_41":171,"_30":31,"_149":-5,"_33":172},{"_49":156,"_38":157,"_36":158,"_142":-5,"_47":48,"_46":159,"_144":31,"_44":160,"_13":161,"_39":162,"_41":163,"_30":31,"_149":-5,"_33":164},{"_49":152,"_38":34,"_36":37,"_142":-5,"_47":48,"_46":14,"_144":31,"_44":153,"_13":154,"_39":40,"_41":43,"_30":31,"_149":-5,"_33":155},{"_49":140,"_38":141,"_36":63,"_142":-5,"_47":48,"_46":143,"_144":31,"_44":145,"_13":146,"_39":147,"_41":148,"_30":31,"_149":150,"_33":151},"c95c6196-6487-421b-bb46-5ffa1864d4a9","webhook.ts","parentId",4,"latest",["D",1788372464721],1,"http","// Telegram webhook handler — responds to /stats (and other commands).\n// Set the webhook to this endpoint's URL in Telegram:\n// https://api.telegram.org/bot/setWebhook?url=\n\nimport { sqlite } from \"https://esm.town/v/std/sqlite/main.ts\";\nimport { buildReport } from \"./bot.ts\";\n\nexport async function http(req: Request) {\n let text = \"Unknown command. Try /stats.\";\n try {\n const update = await req.json();\n const msg = update?.message;\n const cmd: string = (msg?.text ?? \"\").split(/[ @]/)[0];\n\n if (cmd === \"/start\") {\n text = \"Binance Triangular Arb Paper Bot ready. Use /stats for the current report.\";\n } else if (cmd === \"/stats\" || cmd === \"/report\") {\n await sqlite.execute(\n `CREATE TABLE IF NOT EXISTS paper_wallet (\n id INTEGER PRIMARY KEY CHECK (id = 1),\n balance REAL NOT NULL DEFAULT 1000.0,\n initial_balance REAL NOT NULL DEFAULT 1000.0,\n total_scans INTEGER NOT NULL DEFAULT 0,\n total_trades INTEGER NOT NULL DEFAULT 0,\n winning_trades INTEGER NOT NULL DEFAULT 0,\n last_report_time INTEGER NOT NULL DEFAULT 0\n )`,\n );\n await sqlite.execute(`INSERT OR IGNORE INTO paper_wallet (id) VALUES (1)`);\n text = await buildReport();\n }\n } catch (e) {\n text = \"Error: \" + (e instanceof Error ? e.message : String(e));\n }\n\n // If this came from a Telegram webhook, reply directly to the chat\n try {\n const update = await req.clone().json();\n const chatId = update?.message?.chat?.id;\n if (chatId) {\n const token = Deno.env.get(\"TELEGRAM_BOT_TOKEN\");\n if (token) {\n await fetch(`https://api.telegram.org/bot${token}/sendMessage`, {\n method: \"POST\",\n headers: { \"content-type\": \"application/json\" },\n body: JSON.stringify({ chat_id: chatId, text, parse_mode: \"HTML\" }),\n });\n return new Response(\"ok\");\n }\n }\n } catch { /* not a webhook call */ }\n\n return new Response(text, {\n headers: { \"content-type\": \"text/plain; charset=utf-8\" },\n });\n}\n","label","binance-arb-bot",[141],"b111959a-9d47-4b49-8870-f33eefd3ec4f",["D",1788375410196],12,[34],"964b5ea1-f05e-48fa-aabb-9eced1108b59","strategy-bot.ts","9388951a-a6fe-11f1-9e37-1607ee4eb77e",41,["D",1788375190588],5,"interval","// ============================================================\n// Strategy Bot — Multi-Timeframe Technical Analysis Scanner\n// ------------------------------------------------------------\n// Runs on a cron interval (min ~15min on free tier). Each run:\n// 1. Discover medium & high liquidity coins\n// 2. Manage any open paper positions (close at SL/TP1/TP2)\n// 3. Generate signals using the strategy engine\n// 4. Open new paper trades + send Telegram alerts\n// ============================================================\n\nimport {\n generateSignal,\n getLiquidCoins,\n getStrategyWallet,\n initStrategyDb,\n manageOpenPositions,\n openTrade,\n shouldAccept,\n type Signal,\n} from \"./strategy.ts\";\n\n// Risk per trade as % of wallet balance (exposed for Telegram display).\nconst RISK_PCT = 0.01; // 1% risk standard\nconst POSITION_SIZE_USDT = 100; // paper bet size per position\n\nasync function sendTelegram(text: string) {\n const token = Deno.env.get(\"TELEGRAM_BOT_TOKEN\");\n const chatId = Deno.env.get(\"TELEGRAM_CHAT_ID\");\n if (!token || !chatId) return false;\n await fetch(`https://api.telegram.org/bot${token}/sendMessage`, {\n method: \"POST\",\n headers: { \"content-type\": \"application/json\" },\n body: JSON.stringify({ chat_id: chatId, text, parse_mode: \"HTML\" }),\n });\n return true;\n}\n\nconst sideEmoji = (s: string) => (s === \"LONG\" ? \"🟢\" : \"🔴\");\n\nfunction formatSignalAlert(sig: Signal, walletBalance: number) {\n const riskAmt = walletBalance * RISK_PCT;\n const lines = [\n `${sideEmoji(sig.side)} STRATEGY ${sig.side} — ${sig.symbol} ${sideEmoji(sig.side)}`,\n \"\",\n `🎯 Entry: ${sig.entry.toFixed(6)}`,\n `⛔ Stop Loss: ${sig.stopLoss.toFixed(6)}`,\n `✅ TP1 (1:1): ${sig.takeProfit1.toFixed(6)}`,\n `✅ TP2 (1:2): ${sig.takeProfit2.toFixed(6)}`,\n `📐 R:R → TP2: 1:${sig.rr.toFixed(2)}`,\n `🧠 Conf: ${sig.confidence}%`,\n `💵 Risk: ~$${riskAmt.toFixed(2)}`,\n \"\",\n `📌 ${sig.reason}`,\n ];\n return lines.join(\"\\n\");\n}\n\nexport default async function strategyBot() {\n await initStrategyDb();\n\n const wallet = await getStrategyWallet();\n\n // 1. Close any positions that hit their targets / stop.\n let closed = 0;\n try {\n closed = await manageOpenPositions();\n } catch (e) {\n console.log(\"[strategy] position management failed:\", e instanceof Error ? e.message : e);\n }\n\n // 2. Discover the medium & high liquidity universe.\n let coins = [];\n try {\n coins = await getLiquidCoins();\n } catch (e) {\n console.log(\"[strategy] liquidity discovery failed:\", e instanceof Error ? e.message : e);\n }\n if (coins.length === 0) {\n console.log(\"[strategy] no liquid coins found; skipping scan\");\n return;\n }\n\n const highCount = coins.filter((c) => c.liquidity === \"HIGH\").length;\n const medCount = coins.length - highCount;\n\n // 3. Generate signals across the universe, with a concurrency limit to\n // avoid hammering Binance rate limits when scanning many coins.\n const CONCURRENCY = 10;\n const results = await mapLimit(coins, CONCURRENCY, (c) => generateSignal(c.symbol, c.liquidity));\n\n let opened = 0;\n let learnedRejected = 0;\n const freshWallet = await getStrategyWallet();\n for (const sig of results) {\n if (!sig) continue;\n\n // 4. Adaptive learning gate: after enough closed trades, only accept\n // signals whose learned win probability clears the dynamic threshold.\n const verdict = await shouldAccept({\n confidence: sig.confidence,\n poiType: sig.poiType,\n trendType: sig.trendType,\n liquidity: sig.liquidity,\n side: sig.side,\n });\n if (!verdict.accept) {\n learnedRejected++;\n console.log(`[strategy] rejected by learning: ${sig.symbol} ${sig.side} (${verdict.reason})`);\n continue;\n }\n\n await openTrade(sig);\n opened++;\n await sendTelegram(formatSignalAlert(sig, freshWallet.balance));\n }\n\n console.log(\n `[strategy] coins: HIGH=${highCount} MED=${medCount} | closed=${closed} opened=${opened} learnedRejected=${learnedRejected}`,\n );\n}\nconst sleep = (ms: number) => new Promise((r) => setTimeout(r, ms));\n\n// Map over `items` running at most `limit` async tasks concurrently.\n// Returns results in the same order as items (null on rejection).\nasync function mapLimit(items: T[], limit: number, fn: (item: T) => Promise): Promise<(R | null)[]> {\n const out: (R | null)[] = new Array(items.length);\n let next = 0;\n const workers = Array.from({ length: Math.min(limit, items.length) }, async () => {\n while (true) {\n const i = next++;\n if (i >= items.length) return;\n try {\n out[i] = await fn(items[i]);\n } catch (e) {\n console.log(\"[strategy] signal failed:\", e instanceof Error ? e.message : e);\n out[i] = null;\n }\n }\n });\n await Promise.all(workers);\n return out;\n}\n",[157],"00ba2cec-5f37-4ffb-be18-25ad4fa29501","bot.ts","01a0634d-9d8b-740b-a6cc-84f18531dd38",17,["D",1788373286407],11,"// Binance Triangular Arbitrage Paper Trading Bot (fast, with live alerts)\n// Dynamically discovers top-gainer tokens with active USDT + ETH pairs,\n// computes triangular cross-rates, logs paper trades to SQLite, and:\n// • scans very fast (multiple rounds per cron run, ~4s apart);\n// • sends a real-time Telegram alert for EVERY trade (small or large)\n// including total balance + PnL;\n// • still sends a throttled 10-minute summary.\n\nimport { sqlite } from \"https://esm.town/v/std/sqlite/main.ts\";\n\n// ---------- Config ----------\n// Binance public market data. api.binance.com geo-blocks some regions (HTTP 451);\n// data-api.binance.vision serves the identical /api/v3 endpoints globally.\nconst BINANCE = \"https://data-api.binance.vision/api/v3\";\nconst FEE_PER_LEG = 0.001; // 0.1% per leg (use 0.00075 for 0.075%)\nconst TRADE_AMOUNT_USDT = 100; // simulated size per triangular loop\nconst MIN_NET_PROFIT_PCT = 0.1; // minimum net profit % to execute\nconst TOP_GAINERS_N = 20; // how many top gainers to consider per scan\nconst REPORT_INTERVAL_MS = 10 * 60 * 1000; // 10 minutes summary (kept, throttled)\nconst REFRESH_GAINERS_MS = 60 * 1000; // refresh the heavy top-gainer list once/min\nconst SCAN_ROUNDS = 12; // scans per cron run\nconst COOLDOWN_MS = 4 * 1000; // 4s between scans → a fast, repeated sweep\n\nconst sleep = (ms: number) => new Promise((r) => setTimeout(r, ms));\n\n// ---------- Cached top-gainer list ----------\n// The /ticker/24hr endpoint is heavy (~1200 weight). Top gainers barely change\n// second to second, so we refresh the candidate list once a minute and reuse it\n// for the fast cheap bookTicker scans in between — letting us scan far more\n// often without tripping Binance rate limits.\nlet cachedGainers: { list: Ticker[]; at: number } | null = null;\n\nasync function getTopGainersCached(n: number): Promise {\n const now = Date.now();\n if (cachedGainers && now - cachedGainers.at < REFRESH_GAINERS_MS) {\n return cachedGainers.list;\n }\n const fresh = await getTopGainers(n);\n cachedGainers = { list: fresh, at: now };\n return fresh;\n}\n\n// ---------- DB ----------\nasync function initDb() {\n await sqlite.execute(`CREATE TABLE IF NOT EXISTS paper_wallet (\n id INTEGER PRIMARY KEY CHECK (id = 1),\n balance REAL NOT NULL DEFAULT 1000.0,\n initial_balance REAL NOT NULL DEFAULT 1000.0,\n total_scans INTEGER NOT NULL DEFAULT 0,\n total_trades INTEGER NOT NULL DEFAULT 0,\n winning_trades INTEGER NOT NULL DEFAULT 0,\n last_report_time INTEGER NOT NULL DEFAULT 0\n )`);\n await sqlite.execute(`CREATE TABLE IF NOT EXISTS paper_trades (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n symbol TEXT NOT NULL,\n profit_loss REAL NOT NULL,\n arb_percent REAL NOT NULL,\n route TEXT NOT NULL,\n timestamp INTEGER NOT NULL\n )`);\n await sqlite.execute(\n `INSERT OR IGNORE INTO paper_wallet (id) VALUES (1)`,\n );\n}\n\ninterface Wallet {\n balance: number;\n initial_balance: number;\n total_scans: number;\n total_trades: number;\n winning_trades: number;\n last_report_time: number;\n}\n\nasync function getWallet(): Promise {\n const r = await sqlite.execute(\"SELECT * FROM paper_wallet WHERE id = 1\");\n return r.rows[0] as unknown as Wallet;\n}\n\n// ---------- Binance top-gainer discovery ----------\ninterface Ticker {\n symbol: string;\n priceChangePercent: string;\n}\n\n// Fetch 24h stats, keep only USDT-quoted symbols, return sorted by gain desc.\nasync function getTopGainers(n: number): Promise {\n const res = await fetch(`${BINANCE}/ticker/24hr`);\n if (!res.ok) throw new Error(`24hr ${res.status}`);\n const all = (await res.json()) as Ticker[];\n\n const symbols = new Set(all.map((t) => t.symbol));\n const usdtQuoted = all.filter((t) => t.symbol.endsWith(\"USDT\"));\n\n const candidates: Ticker[] = [];\n for (const t of usdtQuoted) {\n const base = t.symbol.slice(0, -4); // strip \"USDT\"\n if (base === \"ETH\" || base === \"USDT\") continue;\n // Must have an active TOKEN/ETH pair to form a triangle with USDT+ETH.\n if (!symbols.has(`${base}ETH`)) continue;\n candidates.push(t);\n }\n\n candidates.sort(\n (a, b) => parseFloat(b.priceChangePercent) - parseFloat(a.priceChangePercent),\n );\n return candidates.slice(0, n);\n}\n\n// ---------- Order book (best bid/ask) ----------\ninterface Book {\n symbol: string;\n bidPrice: string;\n askPrice: string;\n}\n\nasync function fetchBook(symbol: string): Promise {\n const res = await fetch(`${BINANCE}/ticker/bookTicker?symbol=${symbol}`);\n if (!res.ok) throw new Error(`book ${symbol} ${res.status}`);\n return (await res.json()) as Book;\n}\n\n// ---------- Triangular cross-rate calculation ----------\ninterface ArbResult {\n symbol: string; // target top-gainer token\n arbPercent: number; // gross arbitrage %\n netProfit: number; // net profit in USDT after fees\n route: string; // step-by-step route\n}\n\n// For a token with pairs TOKENUSDT, TOKENETH, ETHUSDT, compute both\n// triangular directions and return the more profitable one.\nasync function checkTriangle(token: string): Promise {\n const symUsdt = `${token}USDT`;\n const symEth = `${token}ETH`;\n\n // Concurrently fetch the 3 legs of the triangle.\n const [u, e, eth] = await Promise.allSettled([\n fetchBook(symUsdt),\n fetchBook(symEth),\n fetchBook(\"ETHUSDT\"),\n ]);\n\n if (u.status !== \"fulfilled\" || e.status !== \"fulfilled\" || eth.status !== \"fulfilled\") {\n throw new Error(\"bookTicker fetch failed\");\n }\n\n const askUsdt = parseFloat(u.value.askPrice); // USDT per TOKEN (buy TOKEN)\n const bidUsdt = parseFloat(u.value.bidPrice); // USDT per TOKEN (sell TOKEN)\n const bidEth = parseFloat(e.value.bidPrice); // ETH per TOKEN (sell TOKEN for ETH)\n const askEth = parseFloat(e.value.askPrice); // ETH per TOKEN (buy TOKEN with ETH)\n const askEthUsdt = parseFloat(eth.value.askPrice); // USDT per ETH (buy ETH)\n const bidEthUsdt = parseFloat(eth.value.bidPrice); // USDT per ETH (sell ETH)\n\n // Guard against missing/zero prices (e.g. iliquid pair momentarily at 0),\n // which would otherwise produce Infinity/NaN that SQLite rejects.\n const prices = [askUsdt, bidUsdt, bidEth, askEth, askEthUsdt, bidEthUsdt];\n if (!prices.every((p) => Number.isFinite(p) && p > 0)) {\n // Iliquid pair momentarily at 0/missing → not a valid triangle.\n return { symbol: token, arbPercent: 0, netProfit: 0, route: \"invalid book\" };\n }\n\n const feeFactor = Math.pow(1 - FEE_PER_LEG, 3);\n\n // Path A: USDT -> TOKEN -> ETH -> USDT\n // final = X / ask(TOKENUSDT) * bid(TOKENETH) * bid(ETHUSDT)\n const aFinal = (TRADE_AMOUNT_USDT / askUsdt) * bidEth * bidEthUsdt;\n const aNet = aFinal * feeFactor;\n\n // Path B: USDT -> ETH -> TOKEN -> USDT\n // final = X * bid(TOKENUSDT) / (ask(ETHUSDT) * ask(TOKENETH))\n const bFinal = (TRADE_AMOUNT_USDT * bidUsdt) / (askEthUsdt * askEth);\n const bNet = bFinal * feeFactor;\n\n if (!Number.isFinite(aNet) || !Number.isFinite(bNet)) {\n return { symbol: token, arbPercent: 0, netProfit: 0, route: \"non-finite\" };\n }\n\n const useA = aNet >= bNet;\n const net = useA ? aNet : bNet;\n const gross = useA ? aFinal : bFinal;\n const route = useA\n ? `USDT → ${token} → ETH → USDT`\n : `USDT → ETH → ${token} → USDT`;\n\n return {\n symbol: token,\n arbPercent: ((gross - TRADE_AMOUNT_USDT) / TRADE_AMOUNT_USDT) * 100,\n netProfit: net - TRADE_AMOUNT_USDT,\n route,\n };\n}\n\n// ---------- Telegram ----------\nasync function sendTelegram(text: string) {\n const token = Deno.env.get(\"TELEGRAM_BOT_TOKEN\");\n const chatId = Deno.env.get(\"TELEGRAM_CHAT_ID\");\n if (!token || !chatId) {\n console.log(\"[telegram] missing TELEGRAM_BOT_TOKEN / TELEGRAM_CHAT_ID; skipping send\");\n return false;\n }\n await fetch(`https://api.telegram.org/bot${token}/sendMessage`, {\n method: \"POST\",\n headers: { \"content-type\": \"application/json\" },\n body: JSON.stringify({ chat_id: chatId, text, parse_mode: \"HTML\" }),\n });\n return true;\n}\n\n// Real-time alert for a single executed trade, including total balance + PnL.\n// Sent for both small and large trades (no filtering).\nasync function sendTradeAlert(a: ArbResult, w: Wallet) {\n const pnl = w.balance - w.initial_balance;\n const pnlPct = (pnl / w.initial_balance) * 100;\n const text = [\n \"⚡ ARB TRADE OPENED ⚡\",\n \"\",\n `🎯 Token: ${a.symbol}`,\n `🔄 Route: ${a.route}`,\n `📈 Gross Arb: ${a.arbPercent.toFixed(4)}%`,\n `💵 Net Profit: ${a.netProfit >= 0 ? \"+\" : \"\"}${a.netProfit.toFixed(4)}`,\n \"\",\n `💰 Total Balance: ${w.balance.toFixed(2)}`,\n `📊 PnL: ${pnl >= 0 ? \"+\" : \"\"}${pnl.toFixed(4)} (${pnlPct.toFixed(3)}%)`,\n ].join(\"\\n\");\n await sendTelegram(text);\n}\n\n// Shared by cron and the /stats HTTP handler\nexport async function buildReport() {\n const w = await getWallet();\n const since = w.last_report_time;\n const trades = await sqlite.execute(\n \"SELECT * FROM paper_trades WHERE timestamp > ? ORDER BY timestamp\",\n [since],\n );\n const recent = trades.rows as unknown as {\n symbol: string;\n profit_loss: number;\n arb_percent: number;\n route: string;\n timestamp: number;\n }[];\n\n const pnl = w.balance - w.initial_balance;\n const pnlPct = (pnl / w.initial_balance) * 100;\n const winRate = w.total_trades > 0 ? (w.winning_trades / w.total_trades) * 100 : 0;\n const avgArb = recent.length\n ? recent.reduce((s, t) => s + t.arb_percent, 0) / recent.length\n : 0;\n\n const lines = [\n \"📊 Binance Triangular Arb — 10min Report\",\n \"\",\n `💰 Balance: $${w.balance.toFixed(2)}`,\n `📈 PnL: ${pnl >= 0 ? \"+\" : \"\"}$${pnl.toFixed(4)} (${pnlPct.toFixed(3)}%)`,\n `🎯 Win Rate: ${winRate.toFixed(1)}% (${w.winning_trades}/${w.total_trades})`,\n `🔍 Total Scans: ${w.total_scans}`,\n `⚡ Trades (last 10m): ${recent.length}`,\n `📊 Avg Arb (last 10m): ${avgArb.toFixed(4)}%`,\n \"\",\n ];\n if (recent.length === 0) {\n lines.push(\"No trades captured in the last 10 minutes.\");\n } else {\n lines.push(\"Last 10m trades:\");\n for (const t of recent) {\n lines.push(\n `• ${t.symbol} | ${t.arb_percent.toFixed(3)}% | ` +\n `${t.profit_loss >= 0 ? \"+\" : \"\"}$${t.profit_loss.toFixed(4)} | ${t.route}`,\n );\n }\n }\n return lines.join(\"\\n\");\n}\n\nasync function maybeSendReport(w: Wallet): Promise {\n const now = Date.now();\n if (now - w.last_report_time < REPORT_INTERVAL_MS) return w;\n const report = await buildReport();\n const sent = await sendTelegram(report);\n // Only advance the timer if it was actually delivered, so the first\n // configured run sends immediately rather than being skipped.\n if (sent) {\n await sqlite.execute(\n \"UPDATE paper_wallet SET last_report_time = ? WHERE id = 1\",\n [now],\n );\n return { ...w, last_report_time: now };\n }\n return w;\n}\n\n// One full scan cycle. Reads the wallet fresh each round, since we may run\n// several rounds per cron invocation.\nasync function runScan(round: number) {\n let w = await getWallet();\n\n let topGainers: Ticker[] = [];\n try {\n topGainers = await getTopGainersCached(TOP_GAINERS_N);\n } catch (e) {\n console.log(\"[scan] top-gainer fetch failed:\", e instanceof Error ? e.message : e);\n }\n\n // Concurrently analyze every discovered top-gainer triangle.\n const results = await Promise.allSettled(topGainers.map((t) => checkTriangle(t.symbol.slice(0, -4))));\n\n const arbs: ArbResult[] = [];\n for (const r of results) {\n if (r.status === \"fulfilled\") arbs.push(r.value);\n else console.log(\"[scan] triangle failed:\", r.reason?.message ?? r.reason);\n }\n\n // Execute profitable paper trades, sending a real-time Telegram alert for\n // EVERY trade (small or large) with the updated total balance.\n let executed = 0;\n for (const a of arbs) {\n if (\n Number.isFinite(a.netProfit) &&\n a.netProfit >= (TRADE_AMOUNT_USDT * MIN_NET_PROFIT_PCT) / 100 &&\n w.balance >= TRADE_AMOUNT_USDT\n ) {\n await sqlite.execute(\n `INSERT INTO paper_trades (symbol, profit_loss, arb_percent, route, timestamp)\n VALUES (?, ?, ?, ?, ?)`,\n [a.symbol, a.netProfit, a.arbPercent, a.route, Date.now()],\n );\n w = {\n ...w,\n balance: w.balance + a.netProfit,\n total_trades: w.total_trades + 1,\n winning_trades: w.winning_trades + 1,\n };\n executed++;\n await sendTradeAlert(a, w);\n }\n }\n\n w.total_scans += 1;\n await sqlite.execute(\n `UPDATE paper_wallet SET balance = ?, total_scans = ?, total_trades = ?, winning_trades = ?\n WHERE id = 1`,\n [w.balance, w.total_scans, w.total_trades, w.winning_trades],\n );\n\n console.log(\n `[scan ${round}/${SCAN_ROUNDS}] top gainers: ${topGainers.length}, triangles ok: ${arbs.length}, executed: ${executed}, balance: ${w.balance.toFixed(2)}`,\n );\n\n // At most one Telegram summary per 10 minutes\n await maybeSendReport(w);\n}\n\n// ---------- Cron entrypoint ----------\n// One cron run (every minute) performs SCAN_ROUNDS scans spaced COOLDOWN_MS\n// apart, so a new scan happens roughly every 15 seconds (Val Town cron has no\n// sub-minute granularity on this tier).\nexport default async function binanceTriangularBot() {\n await initDb();\n\n for (let round = 1; round <= SCAN_ROUNDS; round++) {\n const started = Date.now();\n await runScan(round);\n const elapsed = Date.now() - started;\n if (round < SCAN_ROUNDS) {\n const wait = Math.max(0, COOLDOWN_MS - elapsed);\n await sleep(wait);\n }\n }\n}\n",[166],"dada8e7a-0ab7-41d4-b8ff-a0e5c00133d1","README.md","7d594726-a6f9-11f1-9aba-1607ee4eb77e",20,["D",1788373295716],"# Binance Triangular Arbitrage Paper Bot\n\nA Val Town Deno/TypeScript **paper-trading** bot that scans Binance for triangular\narbitrage opportunities among today's top-gaining tokens. It only *simulates*\ntrades — no real money is ever at risk.\n\n## How it works\n\n1. **Discovery** — fetches `/api/v3/ticker/24hr`, keeps symbols quoted in USDT,\n filters to those that also have an active `TOKEN/ETH` pair, then takes the top\n N by 24h % change.\n2. **Cross-rate analysis** — for each top-gainer token it concurrently fetches the\n three legs of the triangle (`TOKENUSDT`, `TOKENETH`, `ETHUSDT`) via\n `/api/v3/ticker/bookTicker`, computes both triangular directions, applies a\n 0.1% fee on each leg (0.3% total), and reports the gross %, net $ profit, and route.\n3. **Paper execution** — when net profit clears the 0.1% threshold and the $1000\n paper wallet has funds, the trade is logged to SQLite **and** a real-time\n Telegram alert is sent for **every** trade (small or large), including the\n updated total balance and running PnL.\n4. **Reporting** — additionally, at most every 10 minutes a throttled Telegram\n summary is sent covering balance, PnL, win rate, scan count, and the last 10\n minutes of trades.\n\n## Data source\n\nBinance's primary API (`api.binance.com`) can return **HTTP 451** from data-center\nIPs (a regional block). This bot uses `data-api.binance.vision`, which serves the\nidentical `/api/v3` endpoints globally — same quotes, no geo-lock.\n\n## Files\n\n- `bot.ts` — the cron job (`binanceTriangularBot`). Runs every minute and performs\n 12 fast scan rounds ~4s apart. The top-gainer list is cached and refreshed once\n a minute (the 24h call is heavy), while the cheap order-book scans run every\n few seconds — fast scanning without hitting Binance rate limits. Sends a\n real-time alert per executed trade plus a 10-minute summary. Exports\n `buildReport()` for reuse by the webhook.\n- `webhook.ts` — HTTP handler responding to Telegram `/start`, `/stats`, and\n `/report` commands.\n\n## Setup\n\n1. Optionally set the two env vars to enable Telegram reporting:\n - `TELEGRAM_BOT_TOKEN` — from [@BotFather](https://t.me/BotFather)\n - `TELEGRAM_CHAT_ID` — your chat id (ask [@userinfobot](https://t.me/userinfobot))\n\n2. Point Telegram at the webhook (replace `` with your bot token) so\n `/stats` works in-chat:\n\n ```text\n https://api.telegram.org/bot/setWebhook?url=https://binance-arb-bot.val.run/\n ```\n\n## Tuning\n\nAll knobs live at the top of `bot.ts`:\n\n| Constant | Default | Meaning |\n| --- | --- | --- |\n| `FEE_PER_LEG` | `0.001` | fee per leg (0.1%); use `0.00075` for 0.075% |\n| `TRADE_AMOUNT_USDT` | `100` | simulated $ per triangular loop |\n| `MIN_NET_PROFIT_PCT` | `0.1` | min net % to execute |\n| `TOP_GAINERS_N` | `20` | top gainers analyzed per scan |\n| `SCAN_ROUNDS` | `12` | scans per cron run |\n| `COOLDOWN_MS` | `4000` | seconds between scans (4s) |\n| `REFRESH_GAINERS_MS` | `60000` | how often the top-gainer list is re-fetched |\n\n> ⚠️ **Real-time per-trade alerts** mean Telegram can be chatty during volatile\n> windows. If that's too much, raise `MIN_NET_PROFIT_PCT` or drop `SCAN_ROUNDS`.\n\nLive Binance spreads plus 0.3% total fees mean real trades are rare — that's\ncorrect behavior; opportunities appear during volatile moves.\n",[174],"pulls","branches","forks","envVars",{"_187":189,"_190":69},"connections",{"_187":69,"_188":69},"count","unfilledCount",2,"emptyCount",{"_49":173,"_38":174,"_36":175,"_142":-5,"_47":48,"_46":176,"_144":65,"_44":209,"_13":161,"_39":26,"_41":178,"_30":31,"_149":-5,"_197":210,"_33":211},{"_49":165,"_38":166,"_36":167,"_142":-5,"_47":48,"_46":168,"_144":65,"_44":206,"_13":170,"_39":162,"_41":171,"_30":31,"_149":-5,"_197":207,"_33":208},{"_49":156,"_38":157,"_36":158,"_142":-5,"_47":48,"_46":159,"_144":31,"_44":203,"_13":161,"_39":162,"_41":163,"_30":31,"_149":-5,"_197":204,"_33":205},{"_49":152,"_38":34,"_36":37,"_142":-5,"_47":48,"_46":14,"_144":31,"_44":200,"_13":154,"_39":40,"_41":43,"_30":31,"_149":-5,"_197":201,"_33":202},{"_49":140,"_38":141,"_36":63,"_142":-5,"_47":48,"_46":143,"_144":31,"_44":196,"_13":146,"_39":147,"_41":148,"_30":31,"_149":150,"_197":198,"_33":199},["D",1788372464721],"children",[],[141],["D",1788375410196],[],[34],["D",1788375190588],[],[157],["D",1788373286407],[],[166],["D",1788373295716],[],[174],["D",1788372422505],["D",1789588306543],{"_106":105,"_113":108},{"_106":105,"_107":108,"_109":108,"_19":61,"_110":111,"_112":31},{"_13":95,"_38":96,"_49":48,"_97":50,"_98":-5,"_99":-5,"_100":-5,"_101":-5,"_102":-5,"_103":51,"_104":105},"ctx",{"_88":-5,"_132":250,"_251":252,"_253":254,"_255":31,"_256":31,"_257":31,"_258":65,"_259":-5,"_260":-7,"_261":-7,"_262":69,"_263":31,"_264":31,"_265":-5,"_266":31,"_267":31,"_268":31,"_269":-5},"ENV",{"_221":222,"_223":224,"_225":96,"_226":227,"_228":229,"_230":231,"_232":233,"_234":235,"_236":237,"_238":239,"_240":241,"_242":243,"_244":245,"_246":247,"_248":249},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","RENDER_GIT_COMMIT","811aa22498c9b0b5a47e8d778de579cdb990f332","RENDER_GIT_BRANCH","POSTHOG_API_TOKEN","phc_nmoyuAkStEZuUp69rYlg1DZgrIt19Gx0Y4Hwf8tGgJP","POSTHOG_API_HOST","https://wart.val.town","WEB_API_TEMPLATE","https://{val}.web.val.run","APP_URL","https://www.val.town","MODULE_URL","https://esm.town","VANITY_WEB_API_TEMPLATE","https://{label}.val.run/","DENOLS_URL","https://lsp.val.town","NODE_ENV","production","CLOUDFLARE_TURNSTILE_SITE_KEY","0x4AAAAAACtaIZJH7s9rBMZ6","GOOGLE_CLIENT_ID","875989696899-jtsqkhlbdv5jrlsk8631p7ipjg3c6o62.apps.googleusercontent.com","STRIPE_PUBLISHABLE_KEY","pk_live_51N2zvWEpZTTLipIkVEwXrUG9AT0yeOOEYxszarZT1jpsZV58Ly6wlg3SltZpHN26CUalOBmUSY7adQRQiSkCZz2500qSXNkf8Y","API_URL","https://api.val.town",[],"preferencesKeybindings","default","preferencesTheme","light","preferencesFixers","preferencesCopilot","preferencesSelectionAi","preferencesCloseBrackets","preferencesChatPanelState","preferencesDprint","preferencesDenofmt","notificationCount","flagDenols","isBusinessOrgMember","businessOrgHandle","isBusinessOrgAdmin","isHigherTownieTier","navCollapsed","currentOrgId"]

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