Live capital is switched on only when every gate above is cleared. Until then Quantum stays on paper.
Strategy vs simply holding COIN — a neutral benchmark. 1D = today's live intraday feed; 1W / 1M / All = the daily track record. Judge it on drawdown and down-market behaviour, not just the endpoint.
Cumulative return each day, from the daily reports: using Quantum (the auto-strategy) vs not using it (holding COIN over the same window). The gap is what the automation added or cost.
| Time | Ticker | Action | Size | Price | Status |
|---|---|---|---|---|---|
| — | COIN | reverse → long | — | awaiting live feed | |
| — | COIN | reverse → short | — | awaiting live feed |
- 2026-06-06
Project created. Tested Alpaca strategy ported to IBKR as a single reversing-order model. Design docs + draft bridge written. - 2026-06-08
Connected to IBKR paper. Bridge upgraded to confirm a true Filled / Rejected status before logging a trade — no optimistic fills. - 2026-06-09
End-to-end live: TradingView alert → bridge → IB Gateway, first real paper fills on this page. Sizing now reads the real account; per-strategy records + selector added. - 2026-06-12
Promotion rule set: live capital only after 30 consecutive clean paper sessions, drawdown within cap, and explicit sign-off. Never automatic. - 2026-06-22
Paper / live mode flag wired so simulated $0 P&L can never be mistaken for real revenue. - In progress
Paper validation running · V2 strategy forward-testing. Numbers above are live paper results, not backtests.
Only order errors is instrumented on the live feed today. Slippage, reverse-failures and uptime come online with the live-capital gateway — until then we show a blank, not a guess.
We publish only numbers we can prove — no inflated claims, no fabricated competitor benchmarks. The edge is the method above, running in the open on this page.
Surpass move: KAI is the only algo X account with a running bot's daily paper P&L + real equity curve — the gap the field refuses to close
The Track Record
Every backtest we run, in the open — loading the lab's iteration history…
| # | Date | Verdict | What was tested |
|---|---|---|---|
| history loading… | |||
Each row is one backtest iteration, pulled straight from the lab's log and auto-updated as new iterations run — no cherry-picking. Educational only — not financial advice. The method behind these tests, in plain words, is in the Field Notes →
Field Notes from the Quantum Strategy Lab
We did the boring, rigorous work — hundreds of backtests, a stack of academic papers, and real paper-trading sessions — and here is the honest truth, shared freely. No hype, no "get rich," nothing to sell. Just what the evidence actually says about automated trading.
Van Tharp and Tom Basso ran a famous test: enter long or short at random — literally a coin flip — across 10 markets. It still made money every time, once they added just two things: a trailing stop (an exit that follows the price to lock in gains and cap losses) and sensible position sizing (risking only about 1% of the account on each trade). The win rate was only ~38%, yet it was reliably profitable. The lesson is blunt: the exits and the bet-size carried the edge, not the entry.
Using liquid, retail-broker instruments — no supercomputers, no exchange co-location:
- HFT market-making — a microsecond speed game won on hardware.
- Institutional statistical arbitrage at scale — bound by latency and capacity.
- Solo machine-learning alpha — an overfitting trap.
- "Medallion-class" secrets — the famous fund is closed and undisclosed by construction; treat any promise to copy it as a red flag.
We ran 106 backtests on COIN (Coinbase stock — a high-beta crypto proxy that swings about 3.5× the market). We found a trend strategy (a Donchian channel — buy on a break above the recent high, sell below the recent low) that beats simply buying and holding, on paper: a profit factor of ~1.39 (win $1.39 for every $1 lost) with a max drawdown of only ~6%.
Then came the honest lesson: a "profit factor above 2 on a single symbol" is essentially a mirage — a number you can only reach by overfitting (tuning so tightly to the past that it fails the moment it goes live). The literature is blunt: real standalone edges are modest, and anything spectacular is either a secret or an artifact. So we re-pointed the lab at what the math actually rewards: a diversified basket plus volatility-targeted sizing.
To be clear: this is paper / research only. Nothing here is live, and none of it is a recommendation to buy, sell, or hold anything. Promoting any strategy to real money is a deliberate human decision — never automatic.
Honest auto-trading field notes
We'll keep publishing what the lab learns — the wins, the dead ends, and the boring truths. No signals to sell, just the honest work in the open.
Follow @kaai_quantum →These mirror the Pine formula 0.5 × 0.65 × equity/price. Calibration writes back to the bridge config once wired.
TradingView alert → webhook → bridge → IB Gateway. Real-time, in your hands.
Hidden Magic
The strategies that actually move — distilled from the live engine and pointed at every stock, not just COIN. The same edge that drives the auto-trader, quietly wired into your KAI Invest Journal & Superformance so the best buy/sell moments find you.
A door that opens once the track record is proven
✦ Edge across every stock
The live strategy, generalised — surfacing the strongest buy/sell setups market-wide, fed straight into your screener.
Inner circle✦ One-tap auto-execute
Send any Invest-Journal idea to the engine with calibrated sizing — research to position in a click.
Inner circle✦ Signal boost → Superformance
The auto-trader's sharpest signals become an extra ranking layer in your daily screen.
Inner circle✦ Carry it anywhere
The most effective, continuously-updated strategies — portable to other platforms you trade on.
Inner circle