Pro tier upgrade: quant rearchitect + WebSocket level-breach monitor
- Restore XAU/USD (Pro plan), drop USD/CAD, raise rate limit to 55/min - Reduce signal engine to 4 uncorrelated pillars (EMA, RSI, BB, Candlestick) with regime-adaptive weights (trending boosts EMA, ranging boosts RSI+BB) - Add structure-based S/R (swing highs/lows) for trade setup targeting - Fix confidence death spiral: all adjustments now additive, not multiplicative - Dynamic R:R trade setup: SL behind swing level, TP at next structure level, reject if R:R < 1.5 - Fix news engine for all 4 pairs (was hardcoded to USDJPY) - Add WebSocket monitor: streams live ticks, triggers M15 scan on level breach with 5-min cooldown per pair — catches breakouts between candle boundaries - Add correlation filter (EUR/USD+GBP/USD, XAU/USD+USD/JPY exposure warnings) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
harwinder
@@ -39,13 +39,13 @@ class MarketSession(Enum):
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USDJPY = "USDJPY=X"
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EURUSD = "EURUSD=X"
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GBPUSD = "GBPUSD=X"
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USDCAD = "USDCAD=X"
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XAUUSD = "XAU/USD"
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PAIR_INFO = {
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USDJPY: {"display": "USD/JPY", "pip_value": 0.01, "is_jpy_pair": True},
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EURUSD: {"display": "EUR/USD", "pip_value": 0.0001, "is_jpy_pair": False},
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GBPUSD: {"display": "GBP/USD", "pip_value": 0.0001, "is_jpy_pair": False},
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USDCAD: {"display": "USD/CAD", "pip_value": 0.0001, "is_jpy_pair": False},
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XAUUSD: {"display": "XAU/USD", "pip_value": 0.10, "is_jpy_pair": False},
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}
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# Defaults (kept for backward compat)
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@@ -92,6 +92,7 @@ class TechnicalConfig:
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volume_ma_period: int = 20
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pivot_lookback: int = 1
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divergence_lookback: int = 14
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swing_sr_lookback: int = 50
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disabled_indicators: List[str] = field(default_factory=list)
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@@ -102,12 +103,11 @@ class SignalConfig:
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min_confidence: float = 55.0
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weights: Dict[str, float] = field(default_factory=lambda: {
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"trend": 0.25,
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"momentum": 0.20,
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"volatility": 0.15,
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"price_action": 0.15,
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"multi_timeframe": 0.15,
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"sentiment": 0.10,
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"ema_trend": 1.0,
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"rsi": 1.5,
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"bollinger": 1.5,
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"candlestick": 1.5,
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"divergence": 1.0,
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})
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mtf_agreement_threshold: int = 2 # of 3 timeframes
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@@ -115,13 +115,14 @@ class SignalConfig:
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default_risk_pct: float = 1.0
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default_rr_ratio: float = 2.0
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atr_sl_multiplier: float = 1.5 # SL distance = 1.5 × ATR
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atr_sl_multiplier: float = 1.5
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min_rr_ratio: float = 1.5
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@dataclass
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class NewsConfig:
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"""Economic calendar & sentiment."""
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tracked_currencies: List[str] = field(default_factory=lambda: ["USD", "JPY", "EUR", "GBP", "CAD"])
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tracked_currencies: List[str] = field(default_factory=lambda: ["USD", "JPY", "EUR", "GBP", "XAU"])
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min_impact: str = "medium"
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pre_event_buffer_minutes: int = 30
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post_event_buffer_minutes: int = 15
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@@ -168,11 +169,12 @@ class PlatformConfig:
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refresh_interval: int = 300
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scan_pairs: List[str] = field(
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default_factory=lambda: [EURUSD, USDJPY, GBPUSD, USDCAD]
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default_factory=lambda: [EURUSD, USDJPY, GBPUSD, XAUUSD]
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)
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scan_timeframes: List[Timeframe] = field(
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default_factory=lambda: [Timeframe.M15, Timeframe.H1]
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default_factory=lambda: [Timeframe.M5, Timeframe.M15, Timeframe.H1, Timeframe.H4]
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)
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candle_buffer_seconds: int = 10
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ws_cooldown_seconds: int = 300
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log_level: str = "INFO"
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+4
-2
@@ -49,7 +49,7 @@ class ForexDataProvider:
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Timeframe.D1: 86400, Timeframe.W1: 604800,
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}
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RATE_LIMIT = 8
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RATE_LIMIT = 55
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RATE_WINDOW = 60.0
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def __init__(self, config: PlatformConfig):
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@@ -67,6 +67,8 @@ class ForexDataProvider:
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@staticmethod
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def _to_td_symbol(symbol: str) -> str:
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if "/" in symbol:
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return symbol
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clean = symbol.replace("=X", "")
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return f"{clean[:3]}/{clean[3:]}"
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@@ -132,7 +134,7 @@ class ForexDataProvider:
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# ── Private ────────────────────────────────────────────────────
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def _throttle(self, credits: int = 1) -> None:
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"""Free tier: 8 credits / minute. Block until budget allows."""
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"""Pro tier: 55 credits / minute. Block until budget allows."""
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while True:
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now = time.time()
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while self._request_times and now - self._request_times[0] >= self.RATE_WINDOW:
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@@ -87,16 +87,15 @@ class TechnicalEngine:
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if "ema_trend" not in disabled: self._compute_ema_signals(df, result)
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if "rsi" not in disabled: self._compute_rsi(df, result)
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if "macd" not in disabled: self._compute_macd(df, result)
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if "bollinger" not in disabled: self._compute_bollinger(df, result)
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if "stochastic" not in disabled: self._compute_stochastic(df, result)
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if "adx" not in disabled: self._compute_adx(df, result)
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if "atr" not in disabled: self._compute_atr(df, result)
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if "ichimoku" not in disabled: self._compute_ichimoku(df, result)
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self._compute_macd_internal(df, result)
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if "divergence" not in disabled: self._detect_divergences(df, result)
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if "structure" not in disabled: self._compute_structure_sr(df, result)
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if "candlestick" not in disabled: self._detect_candlestick_patterns(df, result)
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if "pivot" not in disabled: self._compute_support_resistance(df, result)
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if "volume" not in disabled: self._compute_volume_analysis(df, result)
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# ── Classify market regime ─────────────────────────────────
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self._classify_regime(df, result)
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@@ -222,14 +221,14 @@ class TechnicalEngine:
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result.indicators["rsi"] = IndicatorSignal(
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name="RSI", value=rsi_val, signal=signal,
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confidence=conf, description=desc, weight=1.8,
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confidence=conf, description=desc, weight=1.5,
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)
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# Attach RSI series for divergence detection (dynamic attribute, filtered by _prefix)
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result._rsi_series = rsi # type: ignore[attr-defined]
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def _compute_macd(self, df: pd.DataFrame, result: TechnicalAnalysis):
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"""MACD with histogram momentum analysis."""
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def _compute_macd_internal(self, df: pd.DataFrame, result: TechnicalAnalysis):
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"""Compute MACD histogram internally for divergence detection (not a signal pillar)."""
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c = self.config
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close = df["Close"]
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@@ -239,62 +238,6 @@ class TechnicalEngine:
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signal_line = macd_line.ewm(span=c.macd_signal, adjust=False).mean()
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histogram = macd_line - signal_line
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macd_val = macd_line.iloc[-1]
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signal_val = signal_line.iloc[-1]
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hist_val = histogram.iloc[-1]
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# Check for crossover
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crossover = False
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crossover_type = ""
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if len(macd_line) >= 2:
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prev_macd = macd_line.iloc[-2]
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prev_signal = signal_line.iloc[-2]
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if prev_macd <= prev_signal and macd_val > signal_val:
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crossover = True
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crossover_type = "bullish"
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elif prev_macd >= prev_signal and macd_val < signal_val:
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crossover = True
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crossover_type = "bearish"
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# Histogram momentum (is it expanding or contracting?)
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hist_momentum = ""
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if len(histogram) >= 3:
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h1, h2, h3 = histogram.iloc[-3], histogram.iloc[-2], histogram.iloc[-1]
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if h3 > h2 > h1 and h3 > 0:
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hist_momentum = "accelerating bullish"
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elif h3 < h2 < h1 and h3 < 0:
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hist_momentum = "accelerating bearish"
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elif abs(h3) < abs(h2) and h3 * h2 > 0:
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hist_momentum = "decelerating"
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# Signal
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if crossover and crossover_type == "bullish":
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signal = SignalStrength.STRONG_BUY
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conf = 75
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desc = f"MACD bullish crossover | Hist: {hist_val:.6f}"
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elif crossover and crossover_type == "bearish":
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signal = SignalStrength.STRONG_SELL
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conf = 75
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desc = f"MACD bearish crossover | Hist: {hist_val:.6f}"
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elif macd_val > signal_val and hist_val > 0:
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signal = SignalStrength.BUY
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conf = 55
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desc = f"MACD above signal, positive histogram | {hist_momentum}"
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elif macd_val < signal_val and hist_val < 0:
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signal = SignalStrength.SELL
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conf = 55
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desc = f"MACD below signal, negative histogram | {hist_momentum}"
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else:
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signal = SignalStrength.NEUTRAL
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conf = 35
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desc = f"MACD mixed | Hist: {hist_val:.6f}"
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result.indicators["macd"] = IndicatorSignal(
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name="MACD", value=hist_val, signal=signal,
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confidence=conf, description=desc, weight=1.2,
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)
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# Attach histogram for divergence detection
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result._macd_histogram = histogram # type: ignore[attr-defined]
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def _compute_bollinger(self, df: pd.DataFrame, result: TechnicalAnalysis):
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@@ -672,11 +615,39 @@ class TechnicalEngine:
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name="Divergence", value=len(divergences),
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signal=signal, confidence=70,
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description=" | ".join(divergences),
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weight=3.5,
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weight=1.0,
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)
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# Note: divergences are NOT added to pattern_signals to avoid
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# duplicate reason collection (the indicator entry covers them).
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def _compute_structure_sr(self, df: pd.DataFrame, result: TechnicalAnalysis):
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"""Detect swing high/low structure levels for S/R and trade setup."""
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lookback = min(self.config.swing_sr_lookback, len(df) - 1)
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if lookback < 10:
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return
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price = df["Close"].iloc[-1]
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high_window = df["High"].iloc[-lookback:]
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low_window = df["Low"].iloc[-lookback:]
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swing_highs = self._find_swing_points(high_window, mode="high", window=3)
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swing_lows = self._find_swing_points(low_window, mode="low", window=3)
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highs_above = sorted([v for _, v in swing_highs if v > price])
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highs_below = sorted([v for _, v in swing_highs if v <= price], reverse=True)
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lows_below = sorted([v for _, v in swing_lows if v < price], reverse=True)
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lows_above = sorted([v for _, v in swing_lows if v >= price])
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for i, val in enumerate(highs_above[:3], 1):
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result.key_levels[f"swing_high_{i}"] = float(val)
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for i, val in enumerate(lows_below[:3], 1):
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result.key_levels[f"swing_low_{i}"] = float(val)
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if not highs_above and highs_below:
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result.key_levels["swing_high_1"] = float(highs_below[0])
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if not lows_below and lows_above:
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result.key_levels["swing_low_1"] = float(lows_above[0])
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def _detect_candlestick_patterns(self, df: pd.DataFrame, result: TechnicalAnalysis):
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"""Detect significant candlestick patterns on recent bars."""
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if len(df) < 5:
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@@ -738,11 +709,33 @@ class TechnicalEngine:
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else:
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signal = SignalStrength.NEUTRAL
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conf = 50
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atr_val = result.key_levels.get("atr", 0)
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latest = c
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proximity = atr_val * 0.5 if atr_val > 0 else 0
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if proximity > 0:
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swing_lows = sorted(
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[v for k, v in result.key_levels.items() if k.startswith("swing_low_")]
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)
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swing_highs = sorted(
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[v for k, v in result.key_levels.items() if k.startswith("swing_high_")]
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)
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near_support = any(abs(latest - sl) <= proximity for sl in swing_lows)
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near_resistance = any(abs(latest - sh) <= proximity for sh in swing_highs)
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if bullish > bearish and near_support:
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conf += 20
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patterns.append("(at support)")
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elif bearish > bullish and near_resistance:
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conf += 20
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patterns.append("(at resistance)")
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result.indicators["candlestick"] = IndicatorSignal(
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name="Candlestick", value=float(bullish - bearish),
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signal=signal, confidence=50,
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signal=signal, confidence=conf,
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description=" | ".join(patterns),
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weight=1.2,
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weight=1.5,
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)
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result.pattern_signals.extend(patterns)
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@@ -854,10 +847,18 @@ class TechnicalEngine:
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total_weight = 0.0
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total_confidence = 0.0
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_trending = {
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"ema_trend": 2.0, "rsi": 1.5, "bollinger": 0.8,
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"candlestick": 1.5, "divergence": 1.0,
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}
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regime_weights = {
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"trending_up": {"divergence": 1.5, "rsi": 1.2, "stochastic": 1.1, "candlestick": 1.2, "ema_trend": 1.0, "macd": 1.0, "adx": 1.0, "ichimoku": 0.8, "bollinger": 0.7, "pivot": 0.8},
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"trending_down": {"divergence": 1.5, "rsi": 1.2, "stochastic": 1.1, "candlestick": 1.2, "ema_trend": 1.0, "macd": 1.0, "adx": 1.0, "ichimoku": 0.8, "bollinger": 0.7, "pivot": 0.8},
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"ranging": {"divergence": 1.3, "rsi": 1.4, "stochastic": 1.4, "bollinger": 1.5, "candlestick": 1.3, "pivot": 1.3, "ema_trend": 0.5, "macd": 0.6, "adx": 0.4, "ichimoku": 0.2},
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"trending_up": _trending,
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"trending_down": _trending,
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"trending": _trending,
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"ranging": {
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"ema_trend": 0.5, "rsi": 2.0, "bollinger": 2.0,
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"candlestick": 1.5, "divergence": 1.5,
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},
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}
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active_regime_weights = regime_weights.get(result.market_regime, {})
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@@ -76,9 +76,8 @@ def print_header(title: str = "USD/JPY SIGNAL INTELLIGENCE"):
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# ── Live signal renderer ───────────────────────────────────────────
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_INDICATOR_PREFIXES = (
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"EMA Trend", "RSI", "MACD", "Bollinger Bands", "Stochastic",
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"ADX", "Ichimoku", "Divergence", "Candlestick", "Pivot Points",
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"News", "Regime",
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"EMA Trend", "RSI", "Bollinger Bands", "Divergence",
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"Candlestick", "ADX", "ATR", "News", "Regime",
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)
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@@ -170,13 +169,17 @@ def print_signal_report(report: SignalReport, detail: bool = False):
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levels = report.primary_analysis.key_levels
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if levels:
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shown = [lvl for lvl in ["r3", "r2", "r1", "pivot", "s1", "s2", "s3"] if lvl in levels]
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if shown:
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print(f"\n {Colors.BOLD}Pivot Levels{Colors.RESET}")
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for lvl in shown:
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val = levels[lvl]
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swing_keys = sorted(
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[k for k in levels if k.startswith("swing_")],
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key=lambda k: levels[k], reverse=True,
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)
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if swing_keys:
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print(f"\n {Colors.BOLD}Structure Levels{Colors.RESET}")
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for k in swing_keys:
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val = levels[k]
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color = Colors.RED if val > report.current_price else Colors.GREEN
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print(f" {lvl.upper():>6} {color}{val:.3f}{Colors.RESET}")
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label = k.replace("_", " ").title()
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print(f" {label:>20} {color}{val:.3f}{Colors.RESET}")
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def report_to_dict(report: SignalReport) -> dict:
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Regular → Executable
+19
-10
@@ -104,6 +104,7 @@ class NewsEngine:
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"AUD": ["aussie", "rba", "australian", "australia"],
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"CAD": ["loonie", "boc", "canadian", "canada"],
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"NZD": ["kiwi", "rbnz", "new zealand"],
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"XAU": ["gold", "xau", "precious metal", "safe haven", "bullion"],
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}
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# High-impact event types (these move markets significantly)
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@@ -187,15 +188,21 @@ class NewsEngine:
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return context
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@staticmethod
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def _parse_pair(pair_symbol: str) -> Tuple[str, str]:
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if "/" in pair_symbol:
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parts = pair_symbol.split("/")
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return parts[0], parts[1]
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clean = pair_symbol.replace("=X", "")
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return clean[:3], clean[3:]
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def get_event_risk_level(self, pair_symbol: str, context: FundamentalContext) -> Tuple[str, str]:
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"""
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Get event risk level for a specific pair.
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Returns (risk_level, description).
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risk_level: "clear", "caution", "danger"
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"""
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clean = pair_symbol.replace("=X", "")
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base = clean[:3]
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quote = clean[3:]
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base, quote = self._parse_pair(pair_symbol)
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# Check upcoming high-impact events
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for event in context.events_upcoming_1h:
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@@ -222,9 +229,7 @@ class NewsEngine:
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Returns (score, description) where score is -1 to +1.
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Positive = bullish for the pair, negative = bearish.
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"""
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clean = pair_symbol.replace("=X", "")
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base = clean[:3]
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quote = clean[3:]
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base, quote = self._parse_pair(pair_symbol)
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base_sent = context.news_sentiment.get(base, 0.0)
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quote_sent = context.news_sentiment.get(quote, 0.0)
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@@ -413,7 +418,11 @@ class NewsEngine:
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@staticmethod
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def _get_affected_pairs(currency: str) -> List[str]:
|
||||
"""Map a currency event to USD/JPY (the only watched pair)."""
|
||||
if currency in ("USD", "JPY"):
|
||||
return ["USDJPY=X"]
|
||||
return []
|
||||
_MAP = {
|
||||
"USD": ["EURUSD=X", "USDJPY=X", "GBPUSD=X", "XAU/USD"],
|
||||
"EUR": ["EURUSD=X"],
|
||||
"JPY": ["USDJPY=X"],
|
||||
"GBP": ["GBPUSD=X"],
|
||||
"XAU": ["XAU/USD"],
|
||||
}
|
||||
return _MAP.get(currency, [])
|
||||
|
||||
+60
-33
@@ -334,54 +334,44 @@ class SignalAggregator:
|
||||
score = report.primary_score
|
||||
confidence = report.primary_confidence
|
||||
|
||||
# MTF
|
||||
if report.mtf_agreement:
|
||||
confidence = min(confidence + 15, 95)
|
||||
confidence += 10
|
||||
all_scores = list(report.mtf_scores.values())
|
||||
if all_scores:
|
||||
mtf_avg = sum(all_scores) / len(all_scores)
|
||||
score = score * 0.6 + mtf_avg * 0.4
|
||||
else:
|
||||
confidence *= 0.7
|
||||
confidence -= 15
|
||||
if len(report.mtf_scores) >= 2:
|
||||
scores = list(report.mtf_scores.values())
|
||||
has_bull = any(s > 10 for s in scores)
|
||||
has_bear = any(s < -10 for s in scores)
|
||||
if has_bull and has_bear:
|
||||
report.warnings.append("Timeframes CONFLICT — mixed signals across timeframes")
|
||||
confidence *= 0.6
|
||||
|
||||
# News sentiment
|
||||
if abs(report.news_sentiment) > 0.2:
|
||||
if (report.news_sentiment > 0 and score > 0) or \
|
||||
(report.news_sentiment < 0 and score < 0):
|
||||
confidence = min(confidence + 5, 95)
|
||||
confidence += 5
|
||||
elif (report.news_sentiment > 0 and score < -10) or \
|
||||
(report.news_sentiment < 0 and score > 10):
|
||||
report.warnings.append("News sentiment CONFLICTS with technical signal")
|
||||
confidence *= 0.85
|
||||
confidence -= 10
|
||||
|
||||
# Event risk
|
||||
if report.event_risk == "danger":
|
||||
report.warnings.append(f"EVENT RISK: {report.event_description}")
|
||||
confidence *= 0.3
|
||||
confidence = min(confidence, 30)
|
||||
report.action_recommendation = f"WAIT — {report.event_description}"
|
||||
elif report.event_risk == "caution":
|
||||
report.warnings.append(f"Upcoming event: {report.event_description}")
|
||||
confidence *= 0.7
|
||||
confidence -= 15
|
||||
|
||||
# Volatility regime
|
||||
if report.volatility_regime == "extreme":
|
||||
report.warnings.append("EXTREME volatility — wider stops needed, reduced position size")
|
||||
confidence *= 0.8
|
||||
confidence -= 10
|
||||
elif report.volatility_regime == "low":
|
||||
report.warnings.append("Low volatility — potential for breakout, tight ranges")
|
||||
|
||||
# EMA200 distance gate — empirically the strongest single discriminator
|
||||
# across 2024 trend / 2023 chop / 2026 hostile windows. Sweet spot is
|
||||
# 0.5–1.5% from EMA200: closer = false-breakout chop, farther = chase.
|
||||
# Within band keeps confidence; outside band cuts it sharply so the
|
||||
# trade falls below the B-grade threshold and is rejected.
|
||||
primary = report.primary_analysis
|
||||
if primary is not None:
|
||||
ema200 = primary.key_levels.get("ema_trend")
|
||||
@@ -389,17 +379,18 @@ class SignalAggregator:
|
||||
ema_dist_pct = abs(report.current_price - ema200) / ema200 * 100.0
|
||||
_lo = float(os.environ.get("FB2_EMA_LO", "0.5"))
|
||||
_hi = float(os.environ.get("FB2_EMA_HI", "1.5"))
|
||||
_mult = float(os.environ.get("FB2_EMA_MULT", "0.55"))
|
||||
if ema_dist_pct < _lo:
|
||||
report.warnings.append(
|
||||
f"NEAR EMA200 ({ema_dist_pct:.2f}%) — chop risk, signal damped"
|
||||
f"NEAR EMA200 ({ema_dist_pct:.2f}%) — chop risk"
|
||||
)
|
||||
confidence *= _mult
|
||||
confidence -= 20
|
||||
elif ema_dist_pct > _hi:
|
||||
report.warnings.append(
|
||||
f"FAR FROM EMA200 ({ema_dist_pct:.2f}%) — over-extended, signal damped"
|
||||
f"FAR FROM EMA200 ({ema_dist_pct:.2f}%) — over-extended"
|
||||
)
|
||||
confidence *= _mult
|
||||
confidence -= 20
|
||||
|
||||
confidence = max(0.0, min(95.0, confidence))
|
||||
|
||||
# Final signal
|
||||
if score >= 40 and confidence >= 65:
|
||||
@@ -452,21 +443,57 @@ class SignalAggregator:
|
||||
return None
|
||||
|
||||
is_buy = report.final_signal in (SignalStrength.BUY, SignalStrength.STRONG_BUY)
|
||||
rr = self.config.signals.default_rr_ratio
|
||||
sl_mult = self.config.signals.atr_sl_multiplier
|
||||
|
||||
sl_distance = atr * sl_mult
|
||||
tp1_distance = sl_distance * rr
|
||||
tp2_distance = sl_distance * rr * 1.5
|
||||
levels = report.primary_analysis.key_levels
|
||||
min_rr = self.config.signals.min_rr_ratio
|
||||
|
||||
if is_buy:
|
||||
entry, sl = price, price - sl_distance
|
||||
tp1, tp2 = price + tp1_distance, price + tp2_distance
|
||||
swing_lows = sorted(
|
||||
[v for k, v in levels.items() if k.startswith("swing_low_") and v < price],
|
||||
reverse=True,
|
||||
)
|
||||
if swing_lows:
|
||||
sl_price = swing_lows[0] - atr * 0.5
|
||||
else:
|
||||
sl_price = price - atr * 1.5
|
||||
if price - sl_price < atr * 1.0:
|
||||
sl_price = price - atr * 1.0
|
||||
|
||||
swing_highs = sorted(
|
||||
v for k, v in levels.items() if k.startswith("swing_high_") and v > price + atr * 0.3
|
||||
)
|
||||
tp1 = swing_highs[0] if len(swing_highs) >= 1 else price + atr * 2.0
|
||||
tp2 = swing_highs[1] if len(swing_highs) >= 2 else price + atr * 3.0
|
||||
|
||||
sl_distance = price - sl_price
|
||||
direction = "BUY"
|
||||
entry, sl = price, sl_price
|
||||
else:
|
||||
entry, sl = price, price + sl_distance
|
||||
tp1, tp2 = price - tp1_distance, price - tp2_distance
|
||||
swing_highs = sorted(
|
||||
v for k, v in levels.items() if k.startswith("swing_high_") and v > price
|
||||
)
|
||||
if swing_highs:
|
||||
sl_price = swing_highs[0] + atr * 0.5
|
||||
else:
|
||||
sl_price = price + atr * 1.5
|
||||
if sl_price - price < atr * 1.0:
|
||||
sl_price = price + atr * 1.0
|
||||
|
||||
swing_lows = sorted(
|
||||
[v for k, v in levels.items() if k.startswith("swing_low_") and v < price - atr * 0.3],
|
||||
reverse=True,
|
||||
)
|
||||
tp1 = swing_lows[0] if len(swing_lows) >= 1 else price - atr * 2.0
|
||||
tp2 = swing_lows[1] if len(swing_lows) >= 2 else price - atr * 3.0
|
||||
|
||||
sl_distance = sl_price - price
|
||||
direction = "SELL"
|
||||
entry, sl = price, sl_price
|
||||
|
||||
tp1_distance = abs(tp1 - entry)
|
||||
rr = tp1_distance / sl_distance if sl_distance > 0 else 0
|
||||
|
||||
if rr < min_rr:
|
||||
return None
|
||||
|
||||
risk_pips = sl_distance / pip_value
|
||||
reward_pips = tp1_distance / pip_value
|
||||
@@ -479,7 +506,7 @@ class SignalAggregator:
|
||||
stop_loss=sl,
|
||||
take_profit_1=tp1,
|
||||
take_profit_2=tp2,
|
||||
risk_reward=rr,
|
||||
risk_reward=round(rr, 2),
|
||||
risk_pips=risk_pips,
|
||||
reward_pips=reward_pips,
|
||||
pip_value=pip_value,
|
||||
|
||||
+254
-59
@@ -2,19 +2,27 @@
|
||||
"""
|
||||
ForexBot2 — Telegram Bot
|
||||
========================
|
||||
Async Telegram interface with candle-boundary auto-scanner.
|
||||
Async Telegram interface with candle-boundary auto-scanner and
|
||||
real-time WebSocket level-breach monitor.
|
||||
|
||||
Manual scan: 4 pairs × 2 timeframes via inline buttons.
|
||||
Manual scan: 4 pairs × 4 timeframes via inline buttons.
|
||||
Auto-scan: Starts automatically on boot if TELEGRAM_CHAT_ID is set.
|
||||
/autoscan toggles for additional chats or to stop.
|
||||
M5 fires at :00/:05/:10/… + 10s buffer.
|
||||
M15 fires at :00/:15/:30/:45 + 10s buffer.
|
||||
H1 fires at :00 + 70s buffer (staggered to respect 8 credits/min).
|
||||
H1 fires at :00 + 10s buffer.
|
||||
H4 fires at :00 every 4 hours + 10s buffer.
|
||||
WebSocket: Streams live ticks from TwelveData for all 4 pairs.
|
||||
When price crosses a key structure level (swing S/R, BB,
|
||||
EMA200) cached from the last boundary scan, triggers an
|
||||
immediate M15 scan and sends a Level Break Alert.
|
||||
5-minute cooldown per pair prevents spam.
|
||||
|
||||
Credit budget (free tier):
|
||||
M15 boundary: 4 pairs × 1 credit = 4 credits
|
||||
H1 boundary: 4 pairs × 1 credit = 4 credits (staggered to next minute)
|
||||
Top of hour: 4 + 4 = 8 across two minutes → ≤ 8 / min
|
||||
Daily total: 384 (M15) + 96 (H1) = 480 / day (limit 800)
|
||||
Plan: TwelveData Pro (55 credits/min, 1500 WS credits).
|
||||
Credit budget (worst case, 4-hour boundary):
|
||||
Unique fetches: 4 pairs × ~6 TFs = ~24 calls (rest cached) → fits in 55/min.
|
||||
WebSocket: 4 symbols = 4 credits (one-time on connect).
|
||||
Level breach scans: ~3 REST calls each, ~10/hour worst case.
|
||||
|
||||
Usage:
|
||||
python telegram_bot.py
|
||||
@@ -22,12 +30,16 @@ Usage:
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
from datetime import datetime, timezone
|
||||
from typing import List, Optional
|
||||
from typing import Dict, List, Optional
|
||||
|
||||
import websockets
|
||||
|
||||
from telegram import InlineKeyboardButton, InlineKeyboardMarkup, Update
|
||||
from telegram.constants import ParseMode
|
||||
@@ -40,7 +52,7 @@ from telegram.ext import (
|
||||
|
||||
from config import (
|
||||
PlatformConfig, SignalStrength, Timeframe,
|
||||
EURUSD, USDJPY, GBPUSD, USDCAD, PAIR_INFO,
|
||||
EURUSD, USDJPY, GBPUSD, XAUUSD, PAIR_INFO,
|
||||
)
|
||||
from data_provider import ForexDataProvider
|
||||
from signal_aggregator import SignalAggregator, SignalReport
|
||||
@@ -63,31 +75,60 @@ CHAT_ID = os.environ.get("TELEGRAM_CHAT_ID", "6025899022")
|
||||
|
||||
# ── Pairs & buttons ───────────────────────────────────────────────────
|
||||
|
||||
SCAN_PAIRS = [EURUSD, USDJPY, GBPUSD, USDCAD]
|
||||
SCAN_PAIRS = [EURUSD, USDJPY, GBPUSD, XAUUSD]
|
||||
|
||||
CORRELATION_GROUPS = {
|
||||
"usd_negative": {"pairs": {EURUSD, GBPUSD}, "label": "USD-weakness"},
|
||||
"risk_sentiment": {"pairs": {XAUUSD, USDJPY}, "label": "Risk-sentiment"},
|
||||
}
|
||||
|
||||
_WS_TO_PAIR = {
|
||||
"EUR/USD": EURUSD,
|
||||
"GBP/USD": GBPUSD,
|
||||
"USD/JPY": USDJPY,
|
||||
"XAU/USD": XAUUSD,
|
||||
}
|
||||
_WS_SYMBOLS = list(_WS_TO_PAIR.keys())
|
||||
|
||||
_LEVEL_KEYS = (
|
||||
"swing_high_1", "swing_high_2", "swing_high_3",
|
||||
"swing_low_1", "swing_low_2", "swing_low_3",
|
||||
"bb_upper", "bb_lower", "ema_trend",
|
||||
)
|
||||
|
||||
SCAN_BUTTONS = [
|
||||
{"cb": "scan_eurusd_m5", "symbol": EURUSD, "tf": Timeframe.M5, "label": "EUR/USD 5M"},
|
||||
{"cb": "scan_eurusd_m15", "symbol": EURUSD, "tf": Timeframe.M15, "label": "EUR/USD 15M"},
|
||||
{"cb": "scan_eurusd_h1", "symbol": EURUSD, "tf": Timeframe.H1, "label": "EUR/USD 1H"},
|
||||
{"cb": "scan_eurusd_h4", "symbol": EURUSD, "tf": Timeframe.H4, "label": "EUR/USD 4H"},
|
||||
{"cb": "scan_usdjpy_m5", "symbol": USDJPY, "tf": Timeframe.M5, "label": "USD/JPY 5M"},
|
||||
{"cb": "scan_usdjpy_m15", "symbol": USDJPY, "tf": Timeframe.M15, "label": "USD/JPY 15M"},
|
||||
{"cb": "scan_usdjpy_h1", "symbol": USDJPY, "tf": Timeframe.H1, "label": "USD/JPY 1H"},
|
||||
{"cb": "scan_usdjpy_h4", "symbol": USDJPY, "tf": Timeframe.H4, "label": "USD/JPY 4H"},
|
||||
{"cb": "scan_gbpusd_m5", "symbol": GBPUSD, "tf": Timeframe.M5, "label": "GBP/USD 5M"},
|
||||
{"cb": "scan_gbpusd_m15", "symbol": GBPUSD, "tf": Timeframe.M15, "label": "GBP/USD 15M"},
|
||||
{"cb": "scan_gbpusd_h1", "symbol": GBPUSD, "tf": Timeframe.H1, "label": "GBP/USD 1H"},
|
||||
{"cb": "scan_usdcad_m15", "symbol": USDCAD, "tf": Timeframe.M15, "label": "USD/CAD 15M"},
|
||||
{"cb": "scan_usdcad_h1", "symbol": USDCAD, "tf": Timeframe.H1, "label": "USD/CAD 1H"},
|
||||
{"cb": "scan_gbpusd_h4", "symbol": GBPUSD, "tf": Timeframe.H4, "label": "GBP/USD 4H"},
|
||||
{"cb": "scan_xauusd_m5", "symbol": XAUUSD, "tf": Timeframe.M5, "label": "XAU/USD 5M"},
|
||||
{"cb": "scan_xauusd_m15", "symbol": XAUUSD, "tf": Timeframe.M15, "label": "XAU/USD 15M"},
|
||||
{"cb": "scan_xauusd_h1", "symbol": XAUUSD, "tf": Timeframe.H1, "label": "XAU/USD 1H"},
|
||||
{"cb": "scan_xauusd_h4", "symbol": XAUUSD, "tf": Timeframe.H4, "label": "XAU/USD 4H"},
|
||||
]
|
||||
|
||||
SCAN_LOOKUP = {b["cb"]: b for b in SCAN_BUTTONS}
|
||||
|
||||
# Manual scans use richer confirmations (may spend extra cached credits)
|
||||
TF_CONFIRMATIONS = {
|
||||
Timeframe.M5: [Timeframe.M15, Timeframe.H1],
|
||||
Timeframe.M15: [Timeframe.M5, Timeframe.H1],
|
||||
Timeframe.H1: [Timeframe.M15, Timeframe.H4],
|
||||
Timeframe.H4: [Timeframe.H1, Timeframe.D1],
|
||||
}
|
||||
|
||||
# Auto-scan uses only cross-TF (already cached, zero extra credits)
|
||||
AUTOSCAN_CONFIRMATIONS = {
|
||||
Timeframe.M15: [Timeframe.H1],
|
||||
Timeframe.H1: [Timeframe.M15],
|
||||
Timeframe.M5: [Timeframe.M15, Timeframe.H1],
|
||||
Timeframe.M15: [Timeframe.M5, Timeframe.H1],
|
||||
Timeframe.H1: [Timeframe.M15, Timeframe.H4],
|
||||
Timeframe.H4: [Timeframe.H1, Timeframe.D1],
|
||||
}
|
||||
|
||||
_executor = ThreadPoolExecutor(max_workers=2)
|
||||
@@ -209,6 +250,33 @@ def format_signal_message(report: SignalReport) -> str:
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
# ── Correlation filter ───────────────────────────────────────────────
|
||||
|
||||
def _detect_correlated_exposure(reports: List[SignalReport]) -> List[str]:
|
||||
warnings = []
|
||||
for group_info in CORRELATION_GROUPS.values():
|
||||
group_pairs = group_info["pairs"]
|
||||
group_label = group_info["label"]
|
||||
active = [
|
||||
r for r in reports
|
||||
if r.symbol in group_pairs
|
||||
and r.final_signal not in (SignalStrength.NEUTRAL,)
|
||||
]
|
||||
if len(active) >= 2:
|
||||
directions = set()
|
||||
for r in active:
|
||||
if r.final_signal in (SignalStrength.BUY, SignalStrength.STRONG_BUY):
|
||||
directions.add("BUY")
|
||||
else:
|
||||
directions.add("SELL")
|
||||
if len(directions) == 1:
|
||||
names = ", ".join(r.display_name for r in active)
|
||||
warnings.append(
|
||||
f"⚠️ Same exposure ({group_label}): {names} both {directions.pop()}"
|
||||
)
|
||||
return warnings
|
||||
|
||||
|
||||
# ── Auto-scan summary format ─────────────────────────────────────────
|
||||
|
||||
def _format_scan_summary(reports: List[SignalReport], tf: Timeframe) -> str:
|
||||
@@ -235,6 +303,10 @@ def _format_scan_summary(reports: List[SignalReport], tf: Timeframe) -> str:
|
||||
else:
|
||||
lines.append("No actionable signals this cycle.")
|
||||
|
||||
corr_warnings = _detect_correlated_exposure(reports)
|
||||
for w in corr_warnings:
|
||||
lines.append(w)
|
||||
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
@@ -242,10 +314,10 @@ def _format_scan_summary(reports: List[SignalReport], tf: Timeframe) -> str:
|
||||
|
||||
def scan_keyboard() -> InlineKeyboardMarkup:
|
||||
rows = []
|
||||
for i in range(0, len(SCAN_BUTTONS), 2):
|
||||
for i in range(0, len(SCAN_BUTTONS), 4):
|
||||
rows.append([
|
||||
InlineKeyboardButton(f"🔍 {b['label']}", callback_data=b["cb"])
|
||||
for b in SCAN_BUTTONS[i:i + 2]
|
||||
InlineKeyboardButton(b["label"], callback_data=b["cb"])
|
||||
for b in SCAN_BUTTONS[i:i + 4]
|
||||
])
|
||||
return InlineKeyboardMarkup(rows)
|
||||
|
||||
@@ -315,6 +387,8 @@ async def _scan_timeframe(
|
||||
if not reports:
|
||||
return
|
||||
|
||||
_update_level_cache(reports)
|
||||
|
||||
summary = _format_scan_summary(reports, tf)
|
||||
actionable = [r for r in reports if r.signal_quality in ("A+", "A", "B")]
|
||||
|
||||
@@ -328,60 +402,175 @@ async def _scan_timeframe(
|
||||
logger.exception(f"Failed to send to chat {chat_id}")
|
||||
|
||||
|
||||
async def _m15_loop(bot) -> None:
|
||||
"""Sleep until each M15 candle close, then scan all pairs."""
|
||||
# ── WebSocket level-breach monitor ──────────────────────────────────
|
||||
|
||||
_level_cache: Dict[str, Dict[str, float]] = {}
|
||||
_last_prices: Dict[str, float] = {}
|
||||
_scan_cooldowns: Dict[str, float] = {}
|
||||
|
||||
_cfg = PlatformConfig()
|
||||
_WS_API_KEY = os.environ.get("TWELVE_DATA_API_KEY") or _cfg.twelve_data_api_key
|
||||
_WS_URL = f"wss://ws.twelvedata.com/v1/quotes/price?apikey={_WS_API_KEY}"
|
||||
_WS_COOLDOWN = _cfg.ws_cooldown_seconds
|
||||
|
||||
|
||||
def _update_level_cache(reports: List[SignalReport]) -> None:
|
||||
for r in reports:
|
||||
if r.primary_analysis and r.primary_analysis.key_levels:
|
||||
levels = {
|
||||
k: v for k, v in r.primary_analysis.key_levels.items()
|
||||
if k in _LEVEL_KEYS
|
||||
}
|
||||
if levels:
|
||||
_level_cache[r.symbol] = levels
|
||||
|
||||
|
||||
def _check_level_breach(symbol: str, price: float) -> Optional[str]:
|
||||
levels = _level_cache.get(symbol)
|
||||
if not levels:
|
||||
return None
|
||||
|
||||
last = _last_prices.get(symbol)
|
||||
if last is None:
|
||||
return None
|
||||
|
||||
for name, level in levels.items():
|
||||
if (last < level <= price) or (last > level >= price):
|
||||
return name
|
||||
|
||||
return None
|
||||
|
||||
|
||||
async def _handle_breach(bot, ws_symbol: str, pair_symbol: str, price: float, level_name: str) -> None:
|
||||
now = time.time()
|
||||
if now - _scan_cooldowns.get(pair_symbol, 0) < _WS_COOLDOWN:
|
||||
return
|
||||
|
||||
_scan_cooldowns[pair_symbol] = now
|
||||
display = PAIR_INFO.get(pair_symbol, {}).get("display", ws_symbol)
|
||||
label = level_name.replace("_", " ").title()
|
||||
logger.info(f"Level breach: {display} crossed {label} at {price}")
|
||||
|
||||
try:
|
||||
report = await run_signal(pair_symbol, Timeframe.M15)
|
||||
_update_level_cache([report])
|
||||
text = (
|
||||
f"🔔 *Level Break — {display}*\n"
|
||||
f"Price crossed *{label}* at `{price}`\n"
|
||||
f"━━━━━━━━━━━━━━━━━━━━\n"
|
||||
+ format_signal_message(report)
|
||||
)
|
||||
except Exception as exc:
|
||||
logger.exception(f"Breach scan failed for {display}")
|
||||
text = f"🔔 *Level Break — {display}*\nPrice crossed {label} at `{price}`\n❌ Scan failed: {exc}"
|
||||
|
||||
for chat_id in list(_autoscan_chats):
|
||||
try:
|
||||
await bot.send_message(chat_id, text, parse_mode=ParseMode.MARKDOWN)
|
||||
except Exception:
|
||||
logger.exception(f"Failed to send breach alert to {chat_id}")
|
||||
|
||||
|
||||
async def _ws_monitor(bot) -> None:
|
||||
backoff = 1
|
||||
while _autoscan_chats:
|
||||
try:
|
||||
async with websockets.connect(_WS_URL) as ws:
|
||||
subscribe = {
|
||||
"action": "subscribe",
|
||||
"params": {"symbols": ",".join(_WS_SYMBOLS)},
|
||||
}
|
||||
await ws.send(json.dumps(subscribe))
|
||||
logger.info(f"WebSocket connected, subscribed to {len(_WS_SYMBOLS)} symbols")
|
||||
backoff = 1
|
||||
|
||||
async for raw in ws:
|
||||
if not _autoscan_chats:
|
||||
break
|
||||
|
||||
msg = json.loads(raw)
|
||||
if msg.get("event") != "price":
|
||||
continue
|
||||
|
||||
ws_symbol = msg.get("symbol")
|
||||
pair_symbol = _WS_TO_PAIR.get(ws_symbol)
|
||||
if not pair_symbol:
|
||||
continue
|
||||
|
||||
price = float(msg["price"])
|
||||
breached = _check_level_breach(pair_symbol, price)
|
||||
_last_prices[pair_symbol] = price
|
||||
|
||||
if breached:
|
||||
asyncio.create_task(
|
||||
_handle_breach(bot, ws_symbol, pair_symbol, price, breached)
|
||||
)
|
||||
|
||||
except asyncio.CancelledError:
|
||||
break
|
||||
except Exception:
|
||||
logger.exception(f"WebSocket error, reconnecting in {backoff}s")
|
||||
await asyncio.sleep(backoff)
|
||||
backoff = min(backoff * 2, 60)
|
||||
|
||||
logger.info("WebSocket monitor stopped")
|
||||
|
||||
|
||||
async def _candle_loop(bot, tf: Timeframe, interval_minutes: int) -> None:
|
||||
label = tf.value
|
||||
try:
|
||||
while _autoscan_chats:
|
||||
wait = _seconds_until_boundary(15, buffer=10)
|
||||
logger.info(f"M15 auto-scan: next in {wait:.0f}s")
|
||||
wait = _seconds_until_boundary(interval_minutes, buffer=10)
|
||||
logger.info(f"{label} auto-scan: next in {wait:.0f}s")
|
||||
await asyncio.sleep(wait)
|
||||
if not _autoscan_chats:
|
||||
break
|
||||
logger.info("M15 auto-scan: firing")
|
||||
logger.info(f"{label} auto-scan: firing")
|
||||
await _scan_timeframe(
|
||||
bot, Timeframe.M15, AUTOSCAN_CONFIRMATIONS[Timeframe.M15]
|
||||
bot, tf, AUTOSCAN_CONFIRMATIONS[tf]
|
||||
)
|
||||
except asyncio.CancelledError:
|
||||
pass
|
||||
finally:
|
||||
logger.info("M15 auto-scan loop stopped")
|
||||
logger.info(f"{label} auto-scan loop stopped")
|
||||
|
||||
|
||||
async def _h1_loop(bot) -> None:
|
||||
"""Sleep until each H1 candle close + 60s stagger, then scan all pairs."""
|
||||
try:
|
||||
while _autoscan_chats:
|
||||
wait = _seconds_until_boundary(60, buffer=70)
|
||||
logger.info(f"H1 auto-scan: next in {wait:.0f}s")
|
||||
await asyncio.sleep(wait)
|
||||
if not _autoscan_chats:
|
||||
break
|
||||
logger.info("H1 auto-scan: firing")
|
||||
await _scan_timeframe(
|
||||
bot, Timeframe.H1, AUTOSCAN_CONFIRMATIONS[Timeframe.H1]
|
||||
)
|
||||
except asyncio.CancelledError:
|
||||
pass
|
||||
finally:
|
||||
logger.info("H1 auto-scan loop stopped")
|
||||
_AUTOSCAN_LOOPS = [
|
||||
(Timeframe.M5, 5),
|
||||
(Timeframe.M15, 15),
|
||||
(Timeframe.H1, 60),
|
||||
(Timeframe.H4, 240),
|
||||
]
|
||||
|
||||
|
||||
_ws_task: Optional[asyncio.Task] = None
|
||||
|
||||
|
||||
def _start_scheduler(bot) -> None:
|
||||
"""Start the M15 and H1 scanning loops if not already running."""
|
||||
global _ws_task
|
||||
alive = [t for t in _autoscan_tasks if not t.done()]
|
||||
if not alive:
|
||||
_autoscan_tasks.clear()
|
||||
_autoscan_tasks.append(asyncio.create_task(_m15_loop(bot)))
|
||||
_autoscan_tasks.append(asyncio.create_task(_h1_loop(bot)))
|
||||
logger.info("Candle-boundary scheduler started")
|
||||
for tf, interval in _AUTOSCAN_LOOPS:
|
||||
_autoscan_tasks.append(
|
||||
asyncio.create_task(_candle_loop(bot, tf, interval))
|
||||
)
|
||||
logger.info("Candle-boundary scheduler started (M5/M15/H1/H4)")
|
||||
|
||||
if _ws_task is None or _ws_task.done():
|
||||
_ws_task = asyncio.create_task(_ws_monitor(bot))
|
||||
logger.info("WebSocket level-breach monitor started")
|
||||
|
||||
|
||||
def _stop_scheduler() -> None:
|
||||
"""Cancel all scanning loops."""
|
||||
global _ws_task
|
||||
for t in _autoscan_tasks:
|
||||
t.cancel()
|
||||
_autoscan_tasks.clear()
|
||||
logger.info("Candle-boundary scheduler stopped")
|
||||
if _ws_task and not _ws_task.done():
|
||||
_ws_task.cancel()
|
||||
_ws_task = None
|
||||
logger.info("Candle-boundary scheduler and WebSocket monitor stopped")
|
||||
|
||||
|
||||
# ── Handlers ──────────────────────────────────────────────────────────
|
||||
@@ -417,13 +606,16 @@ async def cmd_autoscan(update: Update, context: ContextTypes.DEFAULT_TYPE) -> No
|
||||
_autoscan_chats.add(chat_id)
|
||||
_start_scheduler(context.bot)
|
||||
|
||||
next_m5 = _seconds_until_boundary(5, buffer=10)
|
||||
next_m15 = _seconds_until_boundary(15, buffer=10)
|
||||
next_h1 = _seconds_until_boundary(60, buffer=70)
|
||||
next_h1 = _seconds_until_boundary(60, buffer=10)
|
||||
next_h4 = _seconds_until_boundary(240, buffer=10)
|
||||
await update.message.reply_text(
|
||||
"✅ *Auto-scan enabled*\n\n"
|
||||
f"Scanning {len(SCAN_PAIRS)} pairs on M15 + H1 at candle close.\n"
|
||||
f"Next M15 scan in {next_m15 / 60:.0f} min\n"
|
||||
f"Next H1 scan in {next_h1 / 60:.0f} min\n\n"
|
||||
f"Scanning {len(SCAN_PAIRS)} pairs on M5/M15/H1/H4 at candle close.\n"
|
||||
f"📡 Live price monitoring active — level-break alerts enabled.\n"
|
||||
f"Next: M5 {next_m5 / 60:.0f}m | M15 {next_m15 / 60:.0f}m | "
|
||||
f"H1 {next_h1 / 60:.0f}m | H4 {next_h4 / 60:.0f}m\n\n"
|
||||
"Use /autoscan again to stop.",
|
||||
parse_mode=ParseMode.MARKDOWN,
|
||||
)
|
||||
@@ -470,20 +662,23 @@ async def _post_init(app: Application) -> None:
|
||||
_autoscan_chats.add(chat_id)
|
||||
_start_scheduler(app.bot)
|
||||
|
||||
next_m5 = _seconds_until_boundary(5, buffer=10)
|
||||
next_m15 = _seconds_until_boundary(15, buffer=10)
|
||||
next_h1 = _seconds_until_boundary(60, buffer=70)
|
||||
next_h1 = _seconds_until_boundary(60, buffer=10)
|
||||
next_h4 = _seconds_until_boundary(240, buffer=10)
|
||||
logger.info(
|
||||
f"Auto-scan started for chat {chat_id} — "
|
||||
f"M15 in {next_m15:.0f}s, H1 in {next_h1:.0f}s"
|
||||
f"M5 {next_m5:.0f}s, M15 {next_m15:.0f}s, H1 {next_h1:.0f}s, H4 {next_h4:.0f}s"
|
||||
)
|
||||
|
||||
try:
|
||||
await app.bot.send_message(
|
||||
chat_id,
|
||||
f"🤖 *ForexBot2 started*\n\n"
|
||||
f"Auto-scanning {len(SCAN_PAIRS)} pairs on M15 + H1.\n"
|
||||
f"Next M15 in {next_m15 / 60:.0f} min | "
|
||||
f"Next H1 in {next_h1 / 60:.0f} min\n\n"
|
||||
f"Auto-scanning {len(SCAN_PAIRS)} pairs on M5/M15/H1/H4.\n"
|
||||
f"📡 Live price monitoring active — level-break alerts enabled.\n"
|
||||
f"Next: M5 {next_m5 / 60:.0f}m | M15 {next_m15 / 60:.0f}m | "
|
||||
f"H1 {next_h1 / 60:.0f}m | H4 {next_h4 / 60:.0f}m\n\n"
|
||||
f"/autoscan to stop | Manual buttons via /start",
|
||||
parse_mode=ParseMode.MARKDOWN,
|
||||
)
|
||||
|
||||
Regular → Executable
+1
-1
@@ -8,7 +8,7 @@ from datetime import datetime, timezone
|
||||
|
||||
API_KEY = "f563b22ffdfd4924955decbe1a8c8701"
|
||||
WS_URL = f"wss://ws.twelvedata.com/v1/quotes/price?apikey={API_KEY}"
|
||||
SYMBOLS = ["EUR/USD", "GBP/USD", "USD/JPY"]
|
||||
SYMBOLS = ["EUR/USD", "GBP/USD", "USD/JPY", "XAU/USD"]
|
||||
|
||||
|
||||
async def stream_ticks():
|
||||
|
||||
Reference in New Issue
Block a user