simplify clock

app.py

@@ -742,167 +742,6 @@ def load_auto_trade_log():
     except Exception:
         return None
 
-class TradingApp:
-    def __init__(self):
-        self.alpaca = AlpacaTrader(st.secrets['ALPACA_API_KEY'], st.secrets['ALPACA_SECRET_KEY'], 'https://paper-api.alpaca.markets')
-        self.sentiment = NewsSentiment(st.secrets['NEWS_API_KEY'])
-        self.analyzer = StockAnalyzer(self.alpaca)
-        self.data = self.analyzer.get_historical_data(self.analyzer.symbols)
-        self.auto_trade_log = []  # Store automatic trade actions
-
-    def display_charts(self):
-        # Dynamically adjust columns based on number of stocks and available width
-        symbols = list(self.data.keys())
-        symbol_to_name = self.analyzer.symbol_to_name
-        n = len(symbols)
-
-        # Calculate columns based on n for best fit
-        if n <= 3:
-            cols = n
-        elif n <= 6:
-            cols = 3
-        elif n <= 8:
-            cols = 4
-        elif n <= 12:
-            cols = 4
-        else:
-            cols = 5
-
-        rows = (n + cols - 1) // cols
-        subplot_titles = [
-            f"{symbol} - {symbol_to_name.get(symbol, '')}" for symbol in symbols
-        ]
-        fig = make_subplots(rows=rows, cols=cols, subplot_titles=subplot_titles)
-        for idx, symbol in enumerate(symbols):
-            df = self.data[symbol]
-            if not df.empty:
-                row = idx // cols + 1
-                col = idx % cols + 1
-                fig.add_trace(
-                    go.Scatter(
-                        x=df.index,
-                        y=df['Close'],
-                        mode='lines',
-                        name=symbol,
-                        hovertemplate=f"%{{x}}<br>{symbol}: %{{y:.2f}}<extra></extra>"
-                    ),
-                    row=row,
-                    col=col
-                )
-        fig.update_layout(
-            title="Top Volume Stocks - Price Charts (Since 2023)",
-            height=max(400 * rows, 600),
-            showlegend=False,
-            dragmode=False,
-        )
-        # Enable scroll-zoom for each subplot (individual zoom)
-        fig.update_layout(
-            xaxis=dict(fixedrange=False),
-            yaxis=dict(fixedrange=False),
-        )
-        for i in range(1, rows * cols + 1):
-            fig.layout[f'xaxis{i}'].update(fixedrange=False)
-            fig.layout[f'yaxis{i}'].update(fixedrange=False)
-        st.plotly_chart(fig, use_container_width=True, config={"scrollZoom": True})
-
-    def manual_trade(self):
-        # Move all user inputs to the sidebar
-        with st.sidebar:
-            st.header("Manual Trade")
-            symbol = st.text_input('Enter stock symbol')
-            qty = int(st.number_input('Enter quantity'))
-            action = st.selectbox('Action', ['Buy', 'Sell'])
-            if st.button('Execute'):
-                if action == 'Buy':
-                    order = self.alpaca.buy(symbol, qty)
-                else:
-                    order = self.alpaca.sell(symbol, qty)
-                if order:
-                    st.success(f"Order executed: {action} {qty} shares of {symbol}")
-                else:
-                    st.error("Order failed")
-            st.header("Portfolio")
-            st.write("Cash Balance:")
-            st.write(self.alpaca.getCash())
-            st.write("Holdings:")
-            st.write(self.alpaca.getHoldings())
-            st.write("Recent Trades:")
-            st.write(pd.DataFrame(self.alpaca.trades))
-
-    def auto_trade_based_on_sentiment(self, sentiment):
-        # Add company name to each action
-        actions = []
-        symbol_to_name = self.analyzer.symbol_to_name
-        for symbol, sentiment_value in sentiment.items():
-            action = None
-            if sentiment_value == 'Positive':
-                order = self.alpaca.buy(symbol, 1)
-                action = 'Buy'
-            elif sentiment_value == 'Negative':
-                order = self.alpaca.sell(symbol, 1)
-                action = 'Sell'
-            else:
-                order = None
-                action = 'Hold'
-            actions.append({
-                'symbol': symbol,
-                'company_name': symbol_to_name.get(symbol, ''),
-                'sentiment': sentiment_value,
-                'action': action
-            })
-        self.auto_trade_log = actions
-        return actions
-
-def background_auto_trade(app):
-    # This function runs in a background thread and does not require a TTY.
-    # The warning "tcgetpgrp failed: Not a tty" is harmless and can be ignored.
-    # It is likely caused by the environment in which the script is running (e.g., Streamlit, Docker, or a notebook).
-    # No code changes are needed for this warning.
-    while True:
-        sentiment = app.sentiment.get_news_sentiment(app.analyzer.symbols)
-        actions = []
-        for symbol, sentiment_value in sentiment.items():
-            action = None
-            if sentiment_value == 'Positive':
-                order = app.alpaca.buy(symbol, 1)
-                action = 'Buy'
-            elif sentiment_value == 'Negative':
-                order = app.alpaca.sell(symbol, 1)
-                action = 'Sell'
-            else:
-                order = None
-                action = 'Hold'
-            actions.append({
-                'symbol': symbol,
-                'sentiment': sentiment_value,
-                'action': action
-            })
-        # Append to log file instead of overwriting
-        log_entry = {
-            "timestamp": datetime.now().isoformat(),
-            "actions": actions,
-            "sentiment": sentiment
-        }
-        try:
-            if os.path.exists(AUTO_TRADE_LOG_PATH):
-                with open(AUTO_TRADE_LOG_PATH, "r") as f:
-                    log_data = json.load(f)
-            else:
-                log_data = []
-        except Exception:
-            log_data = []
-        log_data.append(log_entry)
-        with open(AUTO_TRADE_LOG_PATH, "w") as f:
-            json.dump(log_data, f)
-        time.sleep(AUTO_TRADE_INTERVAL)
-
-def load_auto_trade_log():
-    try:
-        with open(AUTO_TRADE_LOG_PATH, "r") as f:
-            return json.load(f)
-    except Exception:
-        return None
-
 
 def get_market_times(alpaca_api):
     try:
@@ -935,7 +774,7 @@ def main():
         thread.start()
         st.session_state["auto_trade_thread_started"] = True
 
-    # Dynamic market clock
+    # Static market clock (no auto-refresh)
     is_open, now, next_open, next_close = get_market_times(app.alpaca.alpaca)
     market_status = "🟢 Market is OPEN" if is_open else "🔴 Market is CLOSED"
     st.markdown(f"### {market_status}")
@@ -952,10 +791,6 @@ def main():
         seconds_left = int((next_open - now).total_seconds())
         st.markdown(f"**Time until open:** {pd.to_timedelta(seconds_left, unit='s')}")
 
-    # Add auto-refresh for the clock every 5 seconds
-    st.experimental_rerun()
-    time.sleep(5)
-
     # User inputs and portfolio are now in the sidebar
     app.manual_trade()