google-labs-jules[bot]
bf6c4846c4
Instead of repeatedly filtering the DataFrame for each symbol in a loop, sort the DataFrame by timestamp once and use groupby(COL_SYMBOL) to iterate over the groups. This reduces the time complexity and significantly speeds up the signal processing loop. Co-authored-by: maghdam <63883156+maghdam@users.noreply.github.com>
113 lines
3.7 KiB
Python
113 lines
3.7 KiB
Python
import streamlit as st
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import sqlite3
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import pandas as pd
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from streamlit_autorefresh import st_autorefresh
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from pathlib import Path
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from typing import Optional
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# --- Constants ---
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N_FORWARD = 3 # Set this to match your trading bot's N_FORWARD
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DB_PATH = Path("live_signals.db")
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TABLE_NAME = "signals"
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COL_TIMESTAMP = "timestamp"
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COL_SYMBOL = "symbol"
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COL_PREDICTION = "prediction"
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# --- Page Config ---
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st.set_page_config(page_title="AlphaFlow Live Signals", layout="wide")
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st.title("📈 AlphaFlow Trading Bot - Live Signals")
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# Auto-refresh every 60 seconds
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st_autorefresh(interval=60_000, key="refresh")
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# --- Data Loading ---
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@st.cache_data(ttl=30)
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def load_data() -> Optional[pd.DataFrame]:
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"""Load signal data from the SQLite database."""
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if not DB_PATH.exists():
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return None
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try:
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conn = sqlite3.connect(DB_PATH)
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df = pd.read_sql(f'SELECT * FROM {TABLE_NAME} ORDER BY {COL_TIMESTAMP} DESC', conn)
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conn.close()
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return df
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except (sqlite3.Error, pd.errors.DatabaseError) as e:
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st.error(f"Database error: {e}")
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return None
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def display_latest_signals(df: pd.DataFrame):
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st.subheader("Latest Signal Per Symbol")
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latest = (
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df.sort_values(COL_TIMESTAMP)
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.groupby(COL_SYMBOL)
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.tail(1)
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.sort_values(COL_SYMBOL)
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.reset_index(drop=True)
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)
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st.dataframe(latest[[COL_SYMBOL, COL_PREDICTION, COL_TIMESTAMP]], use_container_width=True)
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def display_recent_signals(df: pd.DataFrame):
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st.subheader(f"Latest {N_FORWARD} Signals Per Symbol")
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recent = (
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df.sort_values([COL_SYMBOL, COL_TIMESTAMP])
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.groupby(COL_SYMBOL)
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.tail(N_FORWARD)
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.sort_values([COL_SYMBOL, COL_TIMESTAMP])
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.reset_index(drop=True)
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)
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st.dataframe(recent[[COL_SYMBOL, COL_PREDICTION, COL_TIMESTAMP]], use_container_width=True)
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# Optional: Show a quick mini-forecast chart per symbol
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st.write("---")
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st.subheader("Mini Signal Forecasts (per symbol)")
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for symbol, group in df.sort_values(COL_TIMESTAMP).groupby(COL_SYMBOL):
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mini_df = group.tail(N_FORWARD)
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# Only show if there is more than one unique value
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if mini_df[COL_PREDICTION].nunique() > 1:
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st.write(f"**{symbol}**")
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st.line_chart(mini_df.set_index(COL_TIMESTAMP)[[COL_PREDICTION]], height=100, use_container_width=True)
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def display_signal_history(df: pd.DataFrame):
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with st.expander("Show Full Signal History"):
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st.dataframe(df[[COL_SYMBOL, COL_PREDICTION, COL_TIMESTAMP]], use_container_width=True)
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def display_signal_distribution(df: pd.DataFrame):
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st.subheader("Signal Distribution (All Time)")
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signal_counts = df.groupby([COL_SYMBOL, COL_PREDICTION]).size().unstack(fill_value=0)
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st.bar_chart(signal_counts)
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def main():
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"""Main function to run the Streamlit dashboard."""
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# Show signal legend and refresh time
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st.markdown("""
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| Signal | Meaning |
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|--------|---------|
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| -1 | **Sell**|
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| 0 | **Flat**|
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| 1 | **Buy** |
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""")
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st.caption(f"Last refreshed: {pd.Timestamp.now().strftime('%Y-%m-%d %H:%M:%S')} (server time, probably UTC)")
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df = load_data()
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if df is None or df.empty:
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st.warning("No signals found in the database yet. Please wait for signals to be generated.")
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return
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view_mode = st.radio(
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"View Mode",
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["Latest Signal Per Symbol", f"Latest {N_FORWARD} Signals Per Symbol"],
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horizontal=True,
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)
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if view_mode == "Latest Signal Per Symbol":
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display_latest_signals(df)
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else:
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display_recent_signals(df)
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display_signal_history(df)
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display_signal_distribution(df)
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if __name__ == "__main__":
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main()
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