SIP_GoldBees_Backend/paper_live_trading/appstreamlit_app.py

# app/streamlit_app.py
import streamlit as st
import time
from datetime import datetime
from pathlib import Path
import pandas as pd
from history import load_monthly_close
from market import us_market_status
from data import fetch_live_price
from strategy import allocation
from execution import try_execute_sip
from state import load_state, save_state
from mtm import log_mtm, should_log_mtm

if "engine_active" not in st.session_state:
    st.session_state.engine_active = False

SP500 = "SPY"
GOLD = "GLD"
GOLD_FALLBACK = "IAU"
SMA_MONTHS = 36

def get_prices():
    try:
        sp = fetch_live_price("SPY")
    except Exception as e:
        st.error(f"SPY price error: {e}")
        return None, None

    try:
        gd = fetch_live_price("GLD")
    except Exception:
        try:
            gd = fetch_live_price("IAU")
        except Exception as e:
            st.error(f"Gold price error: {e}")
            return None, None

    return sp, gd

SIP_AMOUNT = st.number_input("SIP Amount ($)", 100, 5000, 1000)
SIP_INTERVAL_SEC = st.number_input("SIP Interval (sec) [TEST]", 30, 3600, 120)

st.title("SIPXAR — Phase-1 Safe Engine")

market_open, market_time = us_market_status()
st.info(f"Market {'OPEN' if market_open else 'CLOSED'} | ET {market_time}")

col1, col2 = st.columns(2)

with col1:
    if st.button("START ENGINE"):
        st.session_state.engine_active = True

        # HARD RESET
        for f in [
            Path("storage/state.json"),
            Path("storage/mtm_ledger.csv"),
            Path("storage/ledger.csv"),
        ]:
            if f.exists():
                f.unlink()

        fresh_state = {
            "total_invested": 0.0,
            "sp_units": 0.0,
            "gd_units": 0.0,
            "last_sip_ts": datetime.utcnow().isoformat(),
        }
        save_state(fresh_state)

        st.success("Engine started")

with col2:
    if st.button("KILL ENGINE"):
        st.session_state.engine_active = False

        # HARD RESET
        from pathlib import Path

        for f in [
            Path("storage/state.json"),
            Path("storage/mtm_ledger.csv"),
            Path("storage/ledger.csv"),
        ]:
            if f.exists():
                f.unlink()

        st.session_state.clear()
        st.warning("Engine killed and state wiped")

        st.stop()

if not st.session_state.engine_active:
    st.info("Engine is stopped. Click START to begin.")
    st.stop()

state = load_state()

sp_price, gd_price = get_prices()

if sp_price is None or gd_price is None:
    st.stop()

st.write("Engine Prices")
st.write("SP Price:", sp_price)
st.write("Gold Price:", gd_price)

sp_hist = load_monthly_close(SP500)
gd_hist = load_monthly_close(GOLD_FALLBACK)

sp_sma = sp_hist.rolling(SMA_MONTHS).mean().iloc[-1]
gd_sma = gd_hist.rolling(SMA_MONTHS).mean().iloc[-1]

eq_w, gd_w = allocation(
    sp_price=sp_price,
    gd_price=gd_price,
    sp_sma=sp_sma,
    gd_sma=gd_sma
)

state, executed = try_execute_sip(
    now=datetime.utcnow(),
    market_open=market_open,
    sip_interval=SIP_INTERVAL_SEC,
    sip_amount=SIP_AMOUNT,
    sp_price=sp_price,
    gd_price=gd_price,
    eq_w=eq_w,
    gd_w=gd_w
)

MTM_FILE = Path("storage/mtm_ledger.csv")

if MTM_FILE.exists():
    mtm_df = pd.read_csv(MTM_FILE)
else:
    mtm_df = pd.DataFrame()

now = datetime.utcnow()

if market_open and should_log_mtm(mtm_df, now):
    portfolio_value, pnl = log_mtm(
        sp_units=state["sp_units"],
        gd_units=state["gd_units"],
        sp_price=sp_price,
        gd_price=gd_price,
        total_invested=state["total_invested"],
    )
else:
    # Do NOT log MTM when market is closed
    portfolio_value = (
        state["sp_units"] * sp_price +
        state["gd_units"] * gd_price
    )
    pnl = portfolio_value - state["total_invested"]

st.metric("Total Invested", f"${state['total_invested']:,.2f}")
st.metric("SP Units", round(state["sp_units"], 4))
st.metric("Gold Units", round(state["gd_units"], 4))
st.metric("Portfolio Value", f"${portfolio_value:,.2f}")
st.metric("Unrealized PnL", f"${pnl:,.2f}", delta=f"{pnl:,.2f}")

st.subheader("Equity Curve (Unrealized PnL)")

if not mtm_df.empty and len(mtm_df) > 1:
    mtm_df["timestamp"] = pd.to_datetime(mtm_df["timestamp"])
    mtm_df = mtm_df.sort_values("timestamp")
    mtm_df.set_index("timestamp", inplace=True)

    st.line_chart(
        mtm_df["pnl"],
        height=350,
    )
else:
    st.info("Equity curve will appear after sufficient MTM data.")

if executed:
    st.success("SIP Executed")

time.sleep(5)
st.rerun()