(feat) adapt pages to time

frontend/pages/config/dman_v5/app.py

@@ -51,7 +51,7 @@ with c3:
 # Fetch and process data
 candle_data = get_candles(connector_name, trading_pair, interval, max_records)
 df = pd.DataFrame(candle_data)
-df.index = pd.to_datetime(df['timestamp'], unit='ms')
+df.index = pd.to_datetime(df['timestamp'], unit='s')
 df = add_indicators(df, macd_fast, macd_slow, macd_signal, diff_lookback)
 
 # Prepare data for signals

frontend/pages/config/kalman_filter_v1/app.py

@@ -91,7 +91,7 @@ with c2:
 # Load candle data
 candle_data = get_candles(connector_name=candles_connector, trading_pair=candles_trading_pair, interval=interval, max_records=max_records)
 df = pd.DataFrame(candle_data)
-df.index = pd.to_datetime(df['timestamp'], unit='ms')
+df.index = pd.to_datetime(df['timestamp'], unit='s')
 candles_processed = add_indicators(df, observation_covariance, transition_covariance)
 
 

frontend/pages/config/utils.py

@@ -23,6 +23,6 @@ def get_candles(connector_name="binance", trading_pair="BTC-USDT", interval="1m"
     df = pd.DataFrame(backend_client.get_historical_candles(connector_name, trading_pair, interval,
                                                             start_time=int(start_time.timestamp() * 1000),
                                                             end_time=int(end_time.timestamp() * 1000)))
-    df.index = pd.to_datetime(df.timestamp, unit='ms')
+    df.index = pd.to_datetime(df.timestamp, unit='s')
     return df
 

frontend/pages/data/download_candles/app.py

@@ -35,7 +35,7 @@ if get_data_button:
     )
 
     candles_df = pd.DataFrame(candles)
-    candles_df.index = pd.to_datetime(candles_df["timestamp"], unit='ms')
+    candles_df.index = pd.to_datetime(candles_df["timestamp"], unit='s')
 
     # Plotting the candlestick chart
     fig = go.Figure(data=[go.Candlestick(