Options Income Lab · Paper Bot
Is the strategy actually working?
The Best Options identifier is paper-traded live on Alpaca with a virtual $1,000 budget — put credit spreads only, the exact entry gates and exit rules the backtest validated. This page is the scoreboard: realized equity, every open position's management plan, and the append-only decision journal. Judge it on closed trades, not vibes.
Virtual equity
$1000.00
Realized P&L
$0.00
Today
$0.00
Open risk
$434.00
Win rate
no closed trades
Profit factor
—
Realized equity ($1000 paper budget)
Flat until the first position closes — the curve steps on every realized exit. Unrealized P&L is deliberately not plotted: the bot grades itself on closed trades only.
Positions (1)
WMT 2026-08-21 $105/$100 put credit spread ×1live — managed every 30 min
credit $0.67/shmax loss $434.0042 DTEprofit target: close ≤ $0.34stop: close ≥ $1.47time exit by 2026-08-14
Latest entry cycle (2026-07-10 18:20 UTC)
ENTERED WMT tier=high blended=92% bt=95%/n=39 risk=$430
ENTERED NFLX tier=high blended=76% bt=74%/n=39 risk=$241
KO — liquidity gate failed (spread/OI)
CVX — model-priced quote — refuse to trade fiction
PG — liquidity gate failed (spread/OI)
COST — liquidity gate failed (spread/OI)
BAC — credit/width 0.10 below floor
GOOGL — max loss $1261 exceeds per-trade cap $450
IWM — max loss $1344 exceeds per-trade cap $450
JPM — model-priced quote — refuse to trade fiction
HD — model-priced quote — refuse to trade fiction
UNH — blended win prob 0.73 < 0.75
XOM — liquidity gate failed (spread/OI)
AVGO — tier moderate below the entry bar
Paper trading on Alpaca with a virtual $1,000 budget — no real money. Auto-refreshes every 60s. Bot CLI: python -m iov.bestopts.bot status in ~/intraday-options.
Methodology — pricing, scoring, strategy mechanics
Pricing modes
- Model — Black-Scholes premium estimated from 60-day historical volatility. Risk-free rate is implicit at zero. Greeks (delta) are computed from the same model.
- Live chain — bid / ask / mid / IV / Greeks pulled from the option chain endpoint when available. Mid is used for yield math; risk-flag
wide_spreadfires when bid/ask > 5%. - Hybrid — live chain when present; falls back to model. The status bar surfaces the actual source per request.
Strategy mechanics
- Covered call — own 100 shares, sell a short call. Premium = income cap, strike = sell price ceiling. Breakeven = purchase price − premium received.
- Cash-secured put — set aside strike × 100 cash, sell a short put. Breakeven = strike − premium. Capital required = strike × 100 per contract.
- Wheel — sell CSPs until assigned, hold the stock, sell CCs until called away, then restart. Adjusted basis tracks premium received less stock losses.
Scoring (composite 0..100)
Each strategy decomposes into 6–8 weighted axes — premium quality, assignment fit, downside risk, liquidity, IV attractiveness, event risk, historical outcome, goal fit. The composite is multiplied by a risk-penalty product (each ≤ 1.0) so wide-spread, low-sample, model-only, or earnings- inside-DTE setups grade down even with a strong raw composite.
Confidence grading
- n < 5 — Very low (do not rank as strong)
- 5..10 — Low
- 10..20 — Moderate
- 20..50 — Good
- ≥ 50 — High
Model-only premium and missing liquidity data each downgrade by one tier.
Decision labels
- Conservative income — low delta, broad breakeven, low assignment risk.
- Balanced income — 0.20–0.35 delta band, moderate premium + assignment.
- Aggressive income — higher delta, higher premium, frequent assignment.
- Exit strategy — high-delta call on shares you want to exit.
- Assignment candidate — CSP at a strike you want to own.
- Avoid chasing premium — composite low or risk penalties dominate.
Limitations & disclaimer
Liquidity, spread, IV rank, open interest, earnings date, and ex-dividend date are surfaced only when the upstream provider supplies them. Missing fields render explicit fallback labels ( Unrated, N/A) — never fake values. Backtests assume entries from a deterministic rule and exit at expiration; real-world slippage, commissions, and early-assignment outcomes are not modelled. This page surfaces analytical projections derived from public market data — not personalised investment advice. Options involve risk, including loss of principal and the risk of assignment. Do your own due diligence and manage risk according to your situation.