Update app/services/market_data.py

app/services/market_data.py

@@ -11,15 +11,59 @@ settings = get_settings()
 
 ALPHA_VANTAGE_URL = "https://www.alphavantage.co/query"
 
-_CACHE = {}
-_CACHE_TTL = 60  # seconds
+_CACHE: dict[str, dict] = {}
+_CACHE_TTL = 60  # seconds (safe for AV free tier)
 
 
 # -------------------------
 # Helpers
 # -------------------------
 def is_crypto(ticker: str) -> bool:
-    return "-" in ticker or ticker.upper() in {"BTC", "ETH", "SOL", "BNB"}
+    t = ticker.upper()
+    return "-" in t or t in {"BTC", "ETH", "SOL", "BNB", "XRP", "DOGE"}
+
+
+def _safe_get_json(params: dict) -> dict:
+    try:
+        resp = requests.get(ALPHA_VANTAGE_URL, params=params, timeout=10)
+        return resp.json()
+    except Exception:
+        raise HTTPException(
+            status_code=503,
+            detail="Market data provider unavailable",
+        )
+
+
+def _detect_ohlcv_columns(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Robust OHLCV resolver for Alpha Vantage crypto.
+    Works for:
+    - 1a / 1b
+    - USD / non-USD
+    - future schema changes
+    """
+    def find(keywords):
+        for c in df.columns:
+            name = c.lower()
+            if all(k in name for k in keywords):
+                return c
+        return None
+
+    open_col  = find(["open"])
+    high_col  = find(["high"])
+    low_col   = find(["low"])
+    close_col = find(["close"])
+    volume_col = find(["volume"])
+
+    if not all([open_col, high_col, low_col, close_col, volume_col]):
+        raise HTTPException(
+            status_code=500,
+            detail=f"Unsupported crypto data format from Alpha Vantage: {list(df.columns)}",
+        )
+
+    df = df[[open_col, high_col, low_col, close_col, volume_col]]
+    df.columns = ["Open", "High", "Low", "Close", "Volume"]
+    return df
 
 
 # -------------------------
@@ -30,26 +74,20 @@ def _fetch_stock_history(ticker: str) -> pd.DataFrame:
         "function": "TIME_SERIES_DAILY",
         "symbol": ticker,
         "apikey": settings.alpha_vantage_api_key,
-        "outputsize": "compact",
+        "outputsize": "compact",  # FREE tier only
     }
 
-    response = requests.get(ALPHA_VANTAGE_URL, params=params, timeout=10)
-    data = response.json()
+    data = _safe_get_json(params)
 
     if "Time Series (Daily)" not in data:
         if "Note" in data or "Information" in data:
-            raise HTTPException(
-                status_code=429,
-                detail="Alpha Vantage rate limit exceeded",
-            )
-        raise HTTPException(
-            status_code=400,
-            detail="Invalid stock ticker symbol",
-        )
+            raise HTTPException(429, "Alpha Vantage rate limit exceeded")
+        raise HTTPException(400, "Invalid stock ticker symbol")
 
-    ts = data["Time Series (Daily)"]
+    df = pd.DataFrame.from_dict(
+        data["Time Series (Daily)"], orient="index"
+    )
 
-    df = pd.DataFrame.from_dict(ts, orient="index")
     df.rename(
         columns={
             "1. open": "Open",
@@ -62,17 +100,13 @@ def _fetch_stock_history(ticker: str) -> pd.DataFrame:
     )
 
     df = df[["Open", "High", "Low", "Close", "Volume"]]
-    df = df.apply(pd.to_numeric, errors="coerce")
-    df.dropna(inplace=True)
+    df = df.apply(pd.to_numeric, errors="coerce").dropna()
 
     df.index = pd.to_datetime(df.index)
     df.sort_index(inplace=True)
 
     if len(df) < settings.history_window + 50:
-        raise HTTPException(
-            status_code=400,
-            detail="Not enough historical stock data",
-        )
+        raise HTTPException(400, "Not enough historical stock data")
 
     return df
 
@@ -81,89 +115,58 @@ def _fetch_stock_history(ticker: str) -> pd.DataFrame:
 # CRYPTO DATA
 # -------------------------
 def _fetch_crypto_history(ticker: str) -> pd.DataFrame:
-    # Normalize ticker
     if "-" in ticker:
-        symbol, market = ticker.split("-")
+        symbol, market = ticker.split("-", 1)
     else:
-        symbol, market = ticker.upper(), "USD"
+        symbol, market = ticker, "USD"
 
     params = {
         "function": "DIGITAL_CURRENCY_DAILY",
-        "symbol": symbol,
-        "market": market,
+        "symbol": symbol.upper(),
+        "market": market.upper(),
         "apikey": settings.alpha_vantage_api_key,
     }
 
-    response = requests.get(ALPHA_VANTAGE_URL, params=params, timeout=10)
-    data = response.json()
+    data = _safe_get_json(params)
 
     if "Time Series (Digital Currency Daily)" not in data:
         if "Note" in data or "Information" in data:
-            raise HTTPException(
-                status_code=429,
-                detail="Alpha Vantage rate limit exceeded",
-            )
-        raise HTTPException(
-            status_code=400,
-            detail="Invalid crypto ticker symbol",
-        )
+            raise HTTPException(429, "Alpha Vantage rate limit exceeded")
+        raise HTTPException(400, "Invalid crypto ticker symbol")
 
-    ts = data["Time Series (Digital Currency Daily)"]
-
-    df = pd.DataFrame.from_dict(ts, orient="index")
-
-    # ---- 🔑 Dynamic USD column detection (CRITICAL FIX) ----
-    try:
-        open_col = next(c for c in df.columns if "open (USD)" in c)
-        high_col = next(c for c in df.columns if "high (USD)" in c)
-        low_col = next(c for c in df.columns if "low (USD)" in c)
-        close_col = next(c for c in df.columns if "close (USD)" in c)
-    except StopIteration:
-        raise HTTPException(
-            status_code=500,
-            detail="Unexpected crypto data format from Alpha Vantage",
-        )
-
-    volume_col = "5. volume"
-
-    df = df[[open_col, high_col, low_col, close_col, volume_col]]
+    df = pd.DataFrame.from_dict(
+        data["Time Series (Digital Currency Daily)"], orient="index"
+    )
 
-    df.columns = ["Open", "High", "Low", "Close", "Volume"]
+    df = _detect_ohlcv_columns(df)
 
-    # ---- Clean & normalize ----
     df = df.apply(pd.to_numeric, errors="coerce")
     df.dropna(subset=["Open", "High", "Low", "Close"], inplace=True)
 
     df.index = pd.to_datetime(df.index)
     df.sort_index(inplace=True)
 
-    # ---- Indicator safety buffer ----
     if len(df) < settings.history_window + 50:
-        raise HTTPException(
-            status_code=400,
-            detail="Not enough historical crypto data after cleaning",
-        )
+        raise HTTPException(400, "Not enough historical crypto data")
 
     return df
 
 
-
 # -------------------------
 # Cached unified fetcher
 # -------------------------
 def fetch_raw_history(ticker: str) -> pd.DataFrame:
-    now = time.time()
     key = ticker.upper()
+    now = time.time()
 
-    if key in _CACHE:
-        cached = _CACHE[key]
-        if now - cached["ts"] < _CACHE_TTL:
-            return cached["df"]
+    if key in _CACHE and now - _CACHE[key]["ts"] < _CACHE_TTL:
+        return _CACHE[key]["df"]
 
-    if is_crypto(key):
-        df = _fetch_crypto_history(key)
-    else:
-        df = _fetch_stock_history(key)
+    df = (
+        _fetch_crypto_history(key)
+        if is_crypto(key)
+        else _fetch_stock_history(key)
+    )
 
     _CACHE[key] = {"df": df, "ts": now}
     return df
@@ -175,12 +178,21 @@ def fetch_raw_history(ticker: str) -> pd.DataFrame:
 def get_enriched_history(ticker: str) -> Tuple[pd.DataFrame, pd.DataFrame]:
     df_raw = fetch_raw_history(ticker)
     df_tech = add_technical_indicators(df_raw)
+
+    if len(df_tech) < settings.history_window:
+        raise HTTPException(
+            status_code=400,
+            detail="Insufficient data after technical indicator calculation",
+        )
+
     return df_raw, df_tech
 
 
 def last_n_candles(df: pd.DataFrame, n: int) -> list[dict]:
-    tail = df.tail(n)
     return [
-        {"date": idx.strftime("%Y-%m-%d"), "price": float(row["Close"])}
-        for idx, row in tail.iterrows()
+        {
+            "date": idx.strftime("%Y-%m-%d"),
+            "price": float(row["Close"]),
+        }
+        for idx, row in df.tail(n).iterrows()
     ]