$ streamlit run dashboard.py

# dashboard.py
import streamlit as st
import sqlite3
import pandas as pd
import plotly.graph_objects as go
from datetime import date, timedelta
from alm_schema import DB_PATH

st.set_page_config(page_title="Life ALM Dashboard", layout="wide")

# ─── Dark Theme ────────────────────────────────────────────
st.markdown("""
<style>
 .stApp { background-color: #0a0a0a; }
 .metric-card {
 background: #111;
 border: 1px solid #1a1a1a;
 border-radius: 4px;
 padding: 16px;
 text-align: center;
 }
 .metric-value {
 font-size: 28px;
 font-weight: 700;
 font-family: 'JetBrains Mono', monospace;
 }
 .metric-label {
 font-size: 12px;
 color: #888;
 text-transform: uppercase;
 letter-spacing: 2px;
 }
 .status-green { color: #00ff88; }
 .status-yellow { color: #ffaa00; }
 .status-red { color: #ff4444; }
</style>
""", unsafe_allow_html=True)

# ─── Data Layer ────────────────────────────────────────────
@st.cache_data(ttl=300)
def load_data():
 conn = sqlite3.connect(DB_PATH)

 # Portfolio
 holdings = pd.read_sql("""
 SELECT h.ticker, h.name, h.shares, h.cost_basis,
 p.close_price, h.shares * p.close_price as market_value
 FROM holdings h
 JOIN prices p ON h.ticker = p.ticker
 WHERE p.price_date = (SELECT MAX(price_date) FROM prices)
 """, conn)

 # Loans
 loans = pd.read_sql("SELECT * FROM loans", conn)

 # Expenses & Income
 expenses = pd.read_sql("SELECT * FROM expenses", conn)
 income = pd.read_sql("SELECT * FROM income", conn)

 # Snapshots (history)
 snapshots = pd.read_sql("SELECT * FROM snapshots ORDER BY snapshot_date", conn)

 conn.close()
 return holdings, loans, expenses, income, snapshots

holdings, loans, expenses, income, snapshots = load_data()

# ─── Calculations ──────────────────────────────────────────
portfolio_value = holdings['market_value'].sum()
total_cost = holdings['cost_basis'].sum()
unrealized_pnl = portfolio_value - total_cost

margin_loan = loans[loans['collateral_type'] == 'portfolio']
margin_balance = margin_loan['balance'].sum() if not margin_loan.empty else 0
margin_ratio = margin_balance / portfolio_value if portfolio_value > 0 else 0

credit_line = loans[loans['collateral_type'].isna()]
credit_available = credit_line['balance'].sum() if not credit_line.empty else 0

monthly_expenses = expenses['monthly_amount'].sum()
monthly_income = income['monthly_amount'].sum()
monthly_cashflow = monthly_income - monthly_expenses

annual_expenses = monthly_expenses * 12
estimated_dividends = 5_000_000 # from dividend model
fire_ratio = estimated_dividends / annual_expenses

# Status
if margin_ratio < 0.50:
 status_color = "status-green"
 status_text = "🟢 SAFE"
elif margin_ratio < 0.60:
 status_color = "status-yellow"
 status_text = "🟡 CAUTION"
else:
 status_color = "status-red"
 status_text = "🔴 DANGER"

# ─── Header ────────────────────────────────────────────────
st.markdown(f"""
# Life ALM Dashboard
**{date.today().isoformat()}** · <span class="{status_color}">{status_text}</span>
""", unsafe_allow_html=True)

# ─── Top Metrics ───────────────────────────────────────────
col1, col2, col3, col4, col5 = st.columns(5)

with col1:
 st.metric("Portfolio", f"¥{portfolio_value:,.0f}", f"¥{unrealized_pnl:+,.0f}")
with col2:
 st.metric("Margin Ratio", f"{margin_ratio:.1%}",
 delta=None, delta_color="inverse")
with col3:
 st.metric("Monthly Cashflow", f"¥{monthly_cashflow:+,.0f}")
with col4:
 st.metric("FIRE Ratio", f"{fire_ratio:.0%}")
with col5:
 # Max survivable drawdown
 total_defense = credit_available + 4_500_000 # cash
 for drop in range(1, 100):
 collateral = portfolio_value * (1 - drop/100)
 if collateral <= 0:
 max_drop = drop - 1
 break
 repay = max(0, margin_balance - collateral * 0.85)
 if repay > total_defense:
 max_drop = drop - 1
 break
 else:
 max_drop = 99
 st.metric("Max Survival", f"-{max_drop}%")

# ─── Holdings Table ────────────────────────────────────────
st.markdown("### Holdings")
display_df = holdings[['ticker', 'name', 'shares', 'close_price', 'market_value', 'cost_basis']].copy()
display_df['pnl'] = display_df['market_value'] - display_df['cost_basis']
display_df['weight'] = display_df['market_value'] / portfolio_value * 100
display_df.columns = ['Ticker', 'Name', 'Shares', 'Price', 'Value', 'Cost', 'P&L', 'Weight%']
st.dataframe(display_df.style.format({
 'Price': '¥{:,.0f}',
 'Value': '¥{:,.0f}',
 'Cost': '¥{:,.0f}',
 'P&L': '¥{:+,.0f}',
 'Weight%': '{:.1f}%',
 'Shares': '{:,}',
}), use_container_width=True, hide_index=True)

# ─── Stress Test ───────────────────────────────────────────
st.markdown("### Stress Test")

stress_data = []
for drop in range(5, 75, 5):
 collateral = portfolio_value * (1 - drop/100)
 ratio = margin_balance / collateral if collateral > 0 else float('inf')
 repay_85 = max(0, margin_balance - collateral * 0.85)
 survive = "✅" if repay_85 <= total_defense else "❌"

 if ratio > 0.85:
 zone = "🔴 Forced Liq"
 elif ratio > 0.70:
 zone = "🟠 Margin Call"
 elif ratio > 0.60:
 zone = "🟡 Frozen"
 else:
 zone = "🟢 Safe"

 stress_data.append({
 'Drop': f"-{drop}%",
 'Collateral': collateral,
 'Ratio': ratio,
 'Zone': zone,
 'Repay Needed': repay_85,
 'Survive?': survive,
 })

stress_df = pd.DataFrame(stress_data)
st.dataframe(stress_df.style.format({
 'Collateral': '¥{:,.0f}',
 'Ratio': '{:.1%}',
 'Repay Needed': '¥{:,.0f}',
}), use_container_width=True, hide_index=True)

# ─── Margin Ratio Chart ───────────────────────────────────
drops = list(range(0, 75))
ratios = [margin_balance / (portfolio_value * (1 - d/100)) * 100
 for d in drops if portfolio_value * (1 - d/100) > 0]

fig = go.Figure()
fig.add_trace(go.Scatter(x=drops[:len(ratios)], y=ratios,
 mode='lines', line=dict(color='#00ff88', width=3), name='Margin Ratio'))

# Threshold lines
fig.add_hline(y=60, line_dash="dash", line_color="#ffaa00", annotation_text="Freeze (60%)")
fig.add_hline(y=70, line_dash="dash", line_color="#ff6600", annotation_text="Call (70%)")
fig.add_hline(y=85, line_dash="dash", line_color="#ff0000", annotation_text="Forced Liq (85%)")

fig.update_layout(
 template='plotly_dark',
 paper_bgcolor='#0a0a0a',
 plot_bgcolor='#0a0a0a',
 title='Margin Ratio vs Drawdown',
 xaxis_title='Portfolio Drawdown (%)',
 yaxis_title='Margin Ratio (%)',
 height=400,
)
st.plotly_chart(fig, use_container_width=True)

# ─── Dividend Projection ──────────────────────────────────
st.markdown("### Dividend Snowball Projection")

years = list(range(11))
div_income = []
cumulative = 0
extra = 0
base_div = 5_000_000
growth = 0.06

for y in years:
 organic = base_div * (1 + growth) ** y
 total = organic + extra
 div_income.append(total)
 reinvest = total + 200_000 * 12 # additional monthly investment
 cumulative += reinvest
 extra = cumulative * 0.045

fig2 = go.Figure()
fig2.add_trace(go.Bar(
 x=[2026 + y for y in years],
 y=[d / 1_000_000 for d in div_income],
 marker_color='#00ff88',
 name='Annual Dividends'
))
fig2.add_hline(y=annual_expenses / 1_000_000, line_dash="dash",
 line_color="#ff4444", annotation_text="Annual Expenses")

fig2.update_layout(
 template='plotly_dark',
 paper_bgcolor='#0a0a0a',
 plot_bgcolor='#0a0a0a',
 title='Projected Dividend Income (¥M)',
 yaxis_title='¥ Millions',
 height=400,
)
st.plotly_chart(fig2, use_container_width=True)

# ─── Balance Sheet Summary ─────────────────────────────────
st.markdown("### Balance Sheet")
col_a, col_l = st.columns(2)

with col_a:
 st.markdown("**Assets**")
 st.write(f"Portfolio: ¥{portfolio_value:,.0f}")
 st.write(f"Cash: ¥4,500,000")
 st.write(f"Real Estate: (paid off)")
 st.write(f"**Total: ¥{portfolio_value + 4_500_000:,.0f}**")

with col_l:
 st.markdown("**Liabilities**")
 st.write(f"Margin Loan: ¥{margin_balance:,.0f}")
 st.write(f"Credit Line: ¥{credit_available:,.0f} (standby)")
 st.write(f"Monthly Burn: ¥{monthly_expenses:,.0f}/mo")
 st.write(f"**Net Worth: ¥{portfolio_value + 4_500_000 - margin_balance:,.0f}**")

# ─── Footer ────────────────────────────────────────────────
st.markdown("---")
st.markdown(
 "*Life ALM Dashboard · 枯れた技術の水平思考 · "
 "[Series: Building Investment Systems with Python]"
 "(https://dev.to/soytuber/why-im-engineering-my-fire-with-python-a-manifesto-32l7)*"
)

# Install dependencies
pip install streamlit plotly pandas

# Run
streamlit run dashboard.py

# Or add to your startup
echo "streamlit run ~/alm/dashboard.py --server.port 8501" >> ~/.bashrc

# /etc/systemd/system/alm-dashboard.service
[Unit]
Description=Life ALM Dashboard

[Service]
ExecStart=/usr/bin/streamlit run /home/user/alm/dashboard.py --server.port 8501
WorkingDirectory=/home/user/alm
Restart=always

[Install]
WantedBy=multi-user.target