Important disclaimer: please read first. This article is for education and research only. It is not financial, investment, tax or trading advice, and it is not a recommendation to buy or sell any futures contract, option or other instrument. Every bot result below is simulated or backtested. Simulated results are prepared with hindsight. They do not include real slippage, market impact or every fee, and they cannot capture the emotional and financial risk of live trading. Futures and options on futures are leveraged. You can lose more than you put in, and option buyers can lose 100% of the premium paid. Where I priced an option leg myself rather than quoting the source report, I label it illustrative or model-priced. Past performance, real or simulated, does not predict future results. Speak to a licensed professional before you trade.
Every morning my research pipeline wakes up before I do. It reads the overnight news, writes an institutional-flow report across futures and options on futures, designs a handful of trading bots from that report, backtests them on about a year of four-hour bars, and then prints a stack of PDFs. This morning’s run finished at 09:15 on Monday, November 9, 2026. It produced seven documents and roughly 28,000 words of raw output.
This post is what I do with that stack. I don’t trust any of it on first read. I cross-check the numbers between documents, throw out what fails basic contract math, rank the institutional option structures by what they can pay versus what they can cost, and run a post-mortem on the bots. I’m giving paid subscribers the full working: the payoff diagrams, the formulas, the Python and the low-latency C++ I use to think about execution.
One promise up front. You’ll see the bots’ performance numbers in full, but not their exact trading rules: entry thresholds, parameters and the exact logic stay private. I’ll describe each bot at the level of “what market idea it trades and why it worked or broke.” That’s enough to learn from, and it keeps the part that is actually mine, mine.
The 60-second summary
If you only read this section, read these points.
1. The market setup. The report frames this week around one event: the October CPI release on Tuesday, November 10. Its CPI preview cites core inflation at 0.34% month over month against a 0.31% estimate and headline at 0.65% against 0.55%. CME FedWatch is quoted at an ~80% chance of a 25 bp hike on December 9, up from ~60% a week earlier. The VIX sits at 22.5 (one section says 22, another 18), VIX December futures trade at 23.5 in contango, and VIX futures open interest is up 32% week over week.
2. The best defined-risk structures, ranked by payoff versus risk (per contract, at expiry):
#1, VIX Dec 25/30 call spread. The 25 call is quoted at 1.20. With my model price for the 30 call, the debit is about $953, the maximum gain about $4,047, a 4.25 : 1 payoff. This is the trade the report repeats most, across three separate sections.
#2, Gold Dec $4,200/$4,300 call spread. Gold is the one market in the report where call volatility is bid (22% calls against 19% puts). Model-priced at the report’s 22% vol, it’s about $2,938 to make about $7,062, roughly 2.4 : 1. It’s also the only theme where a bot actually made money.
#3, WTI Dec $85/$80 put spread. Institutions were selling $95 calls (vol down 3.5 points to 38%) and buying $80 puts (vol up 2.2 points to 41%), and managed-money net longs fell by 18,000 contracts to 220,000. Model-priced, it’s about $1,731 to make about $3,269 (1.89 : 1).
Then, in order: an NQ 18,500/18,000 put spread, a SOFR 96.00 put as a hike hedge, a WTI $90 straddle, an ES collar for people who are already long, a BTC $70,000 call and a silver $52 straddle.
3. The bots: 2 of 6 made money in the backtest.
Micro Gold Safe-Haven Momentum (MGC) made $25,352.50 over 67 trades, with a 50.7% win rate, a 2.12 Sharpe and a 1.79 profit factor. It also had a 76.3% maximum drawdown.
The 10-Year Treasury steepener bot (ZN) made $6,441.96 over 52 trades, with a 48.1% win rate, a 0.84 Sharpe and a 74.7% drawdown.
Together that’s $31,794.46. The other four failed: S&P (−$12,653.30), WTI (−$4,989.43), natural gas (zero trades) and Bitcoin (a reported −$11.86 million that is clearly a units bug, which I’ll show you).
4. My honest read. Neither profitable bot passes my own rulebook, which caps drawdown at 20%. The AI planner that designed the bots promised Sharpe ratios of 1.4 to 1.7 and drawdowns of 10% to 20%. Reality delivered drawdowns three to seven times larger. The edge here comes from the filter, not the generator.
5. The quant and C++ toolkit. Behind the paywall: Black-76 and the Greeks, Kelly, Sharpe and Sortino, the expected value of spreads, skew and term-structure measures, order-book imbalance and microprice, Avellaneda-Stoikov quoting, VPIN, DV01 hedge ratios and correlation hedges. Then six self-contained C++20 programs, every one compiled with g++ -O3 -std=c++20 -Wall with zero warnings: a lock-free SPSC ring, a cache-aligned price ladder, microprice and imbalance, rdtsc latency timing, a zero-allocation memory pool and a Black-76 pricer.
