Update app.py

app.py

@@ -10,14 +10,10 @@ logging.basicConfig(level=logging.INFO)
 # OKX endpoints & utility
 ########################################
 
-# 1) GET symbols (spot tickers)
 OKX_TICKERS_ENDPOINT = "https://www.okx.com/api/v5/market/tickers?instType=SPOT"
-
-# 2) GET historical candles for a symbol
-#    e.g. https://www.okx.com/api/v5/market/candles?instId=BTC-USDT&bar=1H&limit=100
 OKX_CANDLE_ENDPOINT = "https://www.okx.com/api/v5/market/candles"
 
-# You can extend or modify this to match more of OKX's `bar` intervals
+# For demonstration, only these mappings
 TIMEFRAME_MAPPING = {
     "1m": "1m",
     "5m": "5m",
@@ -29,7 +25,7 @@ TIMEFRAME_MAPPING = {
     "6h": "6H",
     "12h": "12H",
     "1d": "1D",
-    "1w": "1W",  # OKX supports 1W, etc.
+    "1w": "1W",
 }
 
 def fetch_okx_symbols():
@@ -63,8 +59,22 @@ def fetch_okx_symbols():
 def fetch_okx_candles(symbol, timeframe="1H", limit=100):
     """
     Fetch historical candle data for a symbol from OKX.
-    timeframe must match OKX's `bar` (e.g. "1H", "4H", "1D").
-    Returns (DataFrame, error_message) or (DataFrame, "").
+
+    OKX data columns:
+    [ts, o, h, l, c, vol, volCcy, volCcyQuote, confirm]
+
+    Example:
+    [
+      "1597026383085",  # ts
+      "3.721",          # o
+      "3.743",          # h
+      "3.677",          # l
+      "3.708",          # c
+      "8422410",        # vol
+      "22698348.04828491",  # volCcy
+      "12698348.04828491",  # volCcyQuote
+      "0"               # confirm
+    ]
     """
     logging.info(f"Fetching {limit} candles for {symbol} @ {timeframe} from OKX...")
     params = {
@@ -83,28 +93,47 @@ def fetch_okx_candles(symbol, timeframe="1H", limit=100):
             logging.error(msg)
             return pd.DataFrame(), msg
 
-        # Data looks like: ["1673684400000", "20923.7", "20952.5", "20881.3", "20945.8", "927.879", "19412314.5671"]
-        # Let's parse columns: [0] ts, [1] open, [2] high, [3] low, [4] close, [5] volume, [6] ??? quoteVol
         items = json_data.get("data", [])
         if not items:
             warning_msg = f"No candle data returned for {symbol}."
             logging.warning(warning_msg)
             return pd.DataFrame(), warning_msg
         
-        # items is a list of lists, each is a candle. Reverse if needed to go old->new:
-        # OKX returns the most recent data first, so we invert it for chronological order
+        # OKX returns newest data first, so reverse to chronological
         items.reverse()
 
-        df = pd.DataFrame(items, columns=[
-            "timestamp", "open", "high", "low", "close", "volume", "quoteVolume"
-        ])
+        # Expecting 9 columns per the docs
+        columns = [
+            "ts",       # timestamp
+            "o",        # open
+            "h",        # high
+            "l",        # low
+            "c",        # close
+            "vol",      # volume (base currency)
+            "volCcy",   # volume in quote currency (for SPOT)
+            "volCcyQuote", 
+            "confirm"
+        ]
+        df = pd.DataFrame(items, columns=columns)
+
+        # Rename columns to be more descriptive or consistent
+        df.rename(columns={
+            "ts": "timestamp",
+            "o": "open",
+            "h": "high",
+            "l": "low",
+            "c": "close"
+        }, inplace=True)
+
+        # Convert numeric columns
+        # 'confirm' often is "0" or "1" string, which you can parse as float or int if you want
         df["timestamp"] = pd.to_datetime(df["timestamp"], unit="ms")
-        df[["open", "high", "low", "close", "volume", "quoteVolume"]] = df[
-            ["open", "high", "low", "close", "volume", "quoteVolume"]
-        ].astype(float)
+        numeric_cols = ["open", "high", "low", "close", "vol", "volCcy", "volCcyQuote", "confirm"]
+        df[numeric_cols] = df[numeric_cols].astype(float)
 
         logging.info(f"Fetched {len(df)} rows for {symbol}.")
         return df, ""
+
     except Exception as e:
         err_msg = f"Error fetching candles for {symbol}: {e}"
         logging.error(err_msg)
@@ -154,7 +183,7 @@ def prophet_wrapper(df_prophet, forecast_steps, freq):
     if err:
         return pd.DataFrame(), err
 
-    # Only keep the newly generated future portion
+    # Only keep newly generated portion
     future_only = full_forecast.iloc[len(df_prophet):, ["ds", "yhat", "yhat_lower", "yhat_upper"]]
     return future_only, ""
 
@@ -169,15 +198,15 @@ def predict(symbol, timeframe, forecast_steps):
     # Convert user timeframe to OKX bar param
     okx_bar = TIMEFRAME_MAPPING.get(timeframe, "1H")
 
-    # Let’s fetch 500 candles
+    # Let's fetch 500 candles
     df_raw, err = fetch_okx_candles(symbol, timeframe=okx_bar, limit=500)
     if err:
         return pd.DataFrame(), err
 
     df_prophet = prepare_data_for_prophet(df_raw)
-    # We guess frequency from timeframe. If timeframe is "1h", we'll do freq="H" in Prophet, etc.
-    # We'll do a simple mapping here:
-    freq = "H" if "h" in timeframe.lower() else "D"  # e.g. "1h" -> "H", "1d" -> "D"
+
+    # We'll guess the freq for Prophet: if timeframe has 'h', let's use 'H', else 'D'
+    freq = "H" if "h" in timeframe.lower() else "D"
 
     future_df, err2 = prophet_wrapper(df_prophet, forecast_steps, freq)
     if err2:
@@ -205,8 +234,8 @@ def main():
         gr.Markdown("# OKX Price Forecasting with Prophet")
         gr.Markdown(
             "This app uses OKX's spot market candles to predict future price movements. "
-            "Select a symbol and timeframe, specify forecast steps, then click 'Generate Forecast'. "
-            "No proxies or special access required."
+            "It requests up to 500 candles (1,440 max on OKX side). If you get errors, "
+            "please try a different symbol or timeframe."
         )
 
         symbol_dd = gr.Dropdown(
@@ -239,7 +268,7 @@ def main():
         )
 
         gr.Markdown(
-            "Looking for more automation? Check out this "
+            "Need more tools? Check out this "
             "[crypto trading bot](https://www.gunbot.com)."
         )