Align with Twelve Data API docs and add Telegram command menu
data_provider: - Switch to Authorization header (recommended over apikey query param) - Enforce 800 credits/day daily limit with warning at 80% - Fall back to synthetic data when daily limit is reached - Fix null OHLC field handling (API can return null for any field) telegram_bot: - Register commands with set_my_commands so Telegram shows / menu - Add /credits command to show daily API credit usage and limits Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Harwinder
+57
-27
@@ -60,9 +60,10 @@ class ForexDataProvider:
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Timeframe.W1: 0.143,
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}
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# Free plan: 8 API credits per minute (each symbol = 1 credit)
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# Free plan: 8 credits/minute, 800 credits/day
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RATE_LIMIT = 8
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RATE_WINDOW = 60.0
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DAILY_LIMIT = 800
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def __init__(self, config: PlatformConfig):
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self.config = config
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@@ -72,12 +73,17 @@ class ForexDataProvider:
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)
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self._cache: Dict[str, pd.DataFrame] = {}
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self._cache_timestamps: Dict[str, float] = {}
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self._request_times: deque = deque() # timestamps of recent API credits used
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self._request_times: deque = deque() # timestamps of recent per-minute credits
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self._daily_credits: int = 0 # credits used today
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self._daily_reset: float = time.time() + 86400 # next midnight reset
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self._no_api_data: set = set() # td_symbols with no values from API (use synthetic)
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self._session = requests.Session()
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# Docs recommend Authorization header over apikey query param
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self._session.headers.update({
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"User-Agent": "Mozilla/5.0 (compatible; ForexIntel/1.0)"
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"User-Agent": "Mozilla/5.0 (compatible; ForexIntel/1.0)",
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})
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if self._api_key:
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self._session.headers["Authorization"] = f"apikey {self._api_key}"
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@staticmethod
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def _to_td_symbol(symbol: str) -> str:
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@@ -199,27 +205,48 @@ class ForexDataProvider:
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# ── Private Methods ────────────────────────────────────────────
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def _throttle(self, credits: int = 1) -> None:
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def _throttle(self, credits: int = 1) -> bool:
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"""
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Block until `credits` API credits are available.
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Block until `credits` per-minute API credits are available.
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Returns False if the daily limit is reached (caller should use synthetic data).
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Twelve Data free tier: 8 credits/minute. Each symbol in any request
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(single or batch) consumes 1 credit. This method enforces the limit
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regardless of whether requests are batched or individual.
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Twelve Data free tier: 8 credits/minute, 800 credits/day.
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Each symbol in any request (single or batch) consumes 1 credit.
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"""
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# Reset daily counter after 24 hours
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now = time.time()
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if now >= self._daily_reset:
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self._daily_credits = 0
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self._daily_reset = now + 86400
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# Daily limit check
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if self._daily_credits + credits > self.DAILY_LIMIT:
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logger.warning(
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f"Daily API limit reached ({self._daily_credits}/{self.DAILY_LIMIT} credits) "
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"— using cached/synthetic data for remaining requests today."
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)
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return False
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# Warn when approaching daily limit
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if self._daily_credits + credits > self.DAILY_LIMIT * 0.8:
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logger.warning(
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f"Approaching daily API limit: {self._daily_credits}/{self.DAILY_LIMIT} credits used."
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)
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# Per-minute rate limit
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while True:
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now = time.time()
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# Evict credits older than the rate window
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while self._request_times and now - self._request_times[0] >= self.RATE_WINDOW:
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self._request_times.popleft()
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available = self.RATE_LIMIT - len(self._request_times)
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if available >= credits:
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ts = time.time()
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for _ in range(credits):
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self._request_times.append(time.time())
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return
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self._request_times.append(ts)
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self._daily_credits += credits
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return True
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# Wait until the oldest credit expires
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wait = (self._request_times[0] + self.RATE_WINDOW) - now + 0.2
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logger.info(
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f"Rate limit: {len(self._request_times)}/{self.RATE_LIMIT} credits used "
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@@ -246,7 +273,8 @@ class ForexDataProvider:
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outputsize = max(outputsize, 300)
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# Each symbol in a batch request costs 1 credit
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self._throttle(credits=len(symbols))
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if not self._throttle(credits=len(symbols)):
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return # daily limit reached — callers will use synthetic data
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resp = self._session.get(
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f"{self.TD_BASE_URL}/time_series",
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@@ -254,7 +282,6 @@ class ForexDataProvider:
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"symbol": ",".join(td_symbols),
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"interval": interval,
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"outputsize": outputsize,
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"apikey": self._api_key,
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"timezone": "UTC",
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"format": "json",
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},
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@@ -283,7 +310,7 @@ class ForexDataProvider:
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values = sym_data.get("values")
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if not values:
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if td_sym not in self._no_api_data:
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logger.warning(
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logger.info(
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f"No API data for {td_sym} — using synthetic data"
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)
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self._no_api_data.add(td_sym)
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@@ -301,13 +328,14 @@ class ForexDataProvider:
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records = [
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{
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"Datetime": pd.Timestamp(v["datetime"], tz="UTC"),
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"Open": float(v["open"]),
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"High": float(v["high"]),
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"Low": float(v["low"]),
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"Close": float(v["close"]),
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"Volume": float(v.get("volume") or 0),
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"Open": float(v["open"]) if v.get("open") is not None else None,
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"High": float(v["high"]) if v.get("high") is not None else None,
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"Low": float(v["low"]) if v.get("low") is not None else None,
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"Close": float(v["close"]) if v.get("close") is not None else None,
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"Volume": float(v["volume"]) if v.get("volume") is not None else 0.0,
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}
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for v in reversed(values)
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if v.get("datetime")
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]
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df = pd.DataFrame(records).set_index("Datetime")
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df.index.name = "Datetime"
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@@ -341,7 +369,8 @@ class ForexDataProvider:
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)
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outputsize = max(outputsize, 300)
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self._throttle(credits=1)
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if not self._throttle(credits=1):
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return self._generate_synthetic_data(symbol, timeframe, lookback_days)
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try:
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resp = self._session.get(
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@@ -350,7 +379,6 @@ class ForexDataProvider:
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"symbol": td_symbol,
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"interval": interval,
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"outputsize": outputsize,
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"apikey": self._api_key,
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"timezone": "UTC",
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"format": "json",
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},
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@@ -369,16 +397,18 @@ class ForexDataProvider:
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raise DataIntegrityError(f"Empty response from Twelve Data for {td_symbol}")
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# Values arrive newest-first; reverse to chronological order
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# API may return null for any OHLC field — map to None for pandas NaN handling
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records = [
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{
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"Datetime": pd.Timestamp(v["datetime"], tz="UTC"),
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"Open": float(v["open"]),
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"High": float(v["high"]),
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"Low": float(v["low"]),
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"Close": float(v["close"]),
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"Volume": float(v.get("volume") or 0),
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"Open": float(v["open"]) if v.get("open") is not None else None,
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"High": float(v["high"]) if v.get("high") is not None else None,
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"Low": float(v["low"]) if v.get("low") is not None else None,
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"Close": float(v["close"]) if v.get("close") is not None else None,
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"Volume": float(v["volume"]) if v.get("volume") is not None else 0.0,
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}
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for v in reversed(values)
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if v.get("datetime")
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]
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df = pd.DataFrame(records).set_index("Datetime")
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