I am convinced prop firm trading is an entirely different ballgame than trading cash accounts. Traditional metrics like Sharpe, smooth equity curves and low max drawdown aren’t what you should be optimizing for to maximize prop payouts. I actually added new fitness functions to find more prop firm compatible strategies -probability of passing eval -probability of first payout -expected return per attempt It is best to think of prop evals as a call option. The option premium is the fee The max loss is the fee And the upside is essentially unlimited payouts Options love volatility. Why are you trading the eval like it’s your retirement account? The best strategy for your own capital is often the wrong one for an eval. Totally different games. You may not even want the same strategies to pass the challenge as the ones you'll trade once funded. That's why the prop firm simulator can sort by these different metrics. I'll be showcasing more of it later this week with a full write up.

A weekend read with some real data and ideas to test. A simple mean reversion strategy in SP500 that buys when price closes in the bottom 20% of the day's range and sells when price closes in the top 80% of the day's range. Look at the impact a market regime filter can have. Big jump in average trade and big reduction in time in market. A great candidate for a position sizing scaler (covered in the article below)

ICYMI full write up on detecting and using market regimes in your trading Stat of the day: check your moving averages

This prop firm math post was pretty controversial despite providing the math 🙃 So I couldn't resist ha! here's prop firm math part 2 47 unsuccessful attempts. 3 payouts. Still profitable. Consider LucidFlex 50K, assuming a $105 evaluation price. 50 attempts over time would cost: 50 × $105 = $5,250. Suppose just 3 eventually reach one maximum $2,000 payout request. After the 90/10 split, you receive $1,800 per payout: 3 × $1,800 = $5,400 $5,400 received $5,250 spent +$150 (before other costs). The other 47 attempts fail and produce nothing. 6% (3/50) reach a max payout while 94% lose. For this simplified one-payout model there is an Expected profit per attempt = Probability of reaching payout × $1,800 − $105. So break-even is approximately 5.83% The question is, do you think you can build strategies or (better yet) a portfolio of strategies that get to one payout 6% of the time? If so, you print. 150 on 5250 invested is 2.86% return. If you run this every month, you're north of 30% per year while failing 94% of challenges. Uncomfortable? Yes. Profitable? Yes. That's usually how trading works. These prop firms are mostly math. Many traders build one strategy, fail and claim it is a scam. Turn the odds in your favor by understanding your numbers. More on the BA prop simulator soon..

Prop firm math is CRAZY. A losing strategy can become a winning strategy under a prop firm structure. Imagine a strategy that: • Wins $200 on 90% of days • Loses $2,000 on 10% of days Trading your own capital, its expected daily P&L is: 90% × $200 − 10% × $2,000 = −$20 ❌ Now imagine a hypothetical prop arrangement: • $100 total fee per attempt • Five winning days to pass the evaluation • Five more winning days in the funded account to receive an $800 cash payout • Any losing day closes the account • No additional costs or payout restrictions Assuming independent daily outcomes, the probability of reaching that payout is: 90%^10 = 34.9% Your personal cash outcomes become: • Reach payout: $800 − $100 = +$700 • Fail before payout: −$100 Expected profit per attempt: 34.9% × $800 − $100 ≈ +$179 ✅ The strategy has negative trading expectancy, but this hypothetical "prop" contract gives the trader positive cash expectancy. Why? Because you absorb the evaluation fee instead of the strategy’s full trading losses. This opens up what strategies and portfolios can look like. It is a completely different design and optimization problem for strategy and portfolio development. This week I will be sharing more about the prop firm simulator I've built and what it can do.

The last week or so I’ve covered four different signals and filters for context-aware systematic trading. Here’s a recap for wknd readers. Gamma Exposure: option market maker positioning. Dealers will quiet directional price moves when long gamma and will extend directional price moves when short gamma. Commitment of Traders Report: understand how different players are positioned. Large speculators are net long and retail is net short is different than large specs net short and retail extremely bullish. Market Breadth: what’s going on under the hood. Is this rally well supported or only due to a few names. Will there be follow through? Event Conditioned signals: sequence matters. If last hour yesterday rallied hard, what does that mean for today’s open? How we got here is as important as where we are. Links in the comment for full explanation and example signals you can test. If you like this type of content let me know with a comment. I have more haha

Markets have memory. Most trading signals don’t. The same signal can mean something completely different depending on what happened earlier in the session. And what happened earlier changes what happens next. Think of a football game's score. 21–17 tells you the state of the game. It matters if the team down 17–0 has just scored three straight touchdowns, or was ahead 21–0 and is collapsing. That’s the idea behind event-conditioned signals. A trading signal conditioned on an earlier event. Examples: • Yesterday’s last hour was strong → does today’s first hour follow through? • Opening range closed higher → does the last hour tend to rally? • Price was above VWAP at noon → does that strength persist into the close? • Today’s opening range is unusually large → are breakouts more likely to continue? I've added a ValueWhen function that stores what happened at a specific time or event, then we can use that information later or even the next session. Available in the custom editor and the prompt to signal feature. This greatly expands the types of signals and strategies possible. I put together 168 ready-made event-conditioned signals covering: • Last-hour behavior • Opening-range behavior • VWAP checkpoints • Open / ORH / ORL relationships • Body-to-range ratios • High/low-of-day structure Then you can mix them with Build Alpha’s other built-in signals and/or your own custom signals and let Build Alpha + AI test which combinations actually survive validation. The more context your strategy has the more robust it can be.

The strategy that would have saved your worst drawdown is sitting in your reject pile. You threw it out because it looked bad. Nobody keeps an ugly strategy. This is one of the major flaws in how most strategy development works. We (system traders) typically evaluate strategies on a standalone basis. Best profit, best sharpe, smoothest equity curve, whatever. Then just mash them together in a portfolio and pray they work together. It’s why I built Portfolio Search You pick a saved portfolio and instead of generating the best single strategies, it generates strategies that contribute to what you already have. The fitness function (optimization goal) becomes marginal contribution to that portfolio instead of the strategy's own backtest. Portfolio Lift toggles every metric so you can see the percentage improvement from adding each candidate strategy. Portfolio View lets you click through and watch the combined equity curve change. The ugly strategy that you discarded but actually need is now discovered on purpose and presented with data. A more thoughtful way to develop.

The best trading research workflow is one you don’t have to sit and supervise. That’s the idea behind Sequence Workflows in Build Alpha. Instead of: Run a broad search on 10 years of data Wait, then manually Take the survivors and test the last 3 years Wait, then manually run Run Robustness Tests like Monte Carlo Wait, then manually Test on another date range as a final check …you can now configure the entire research pipeline once and let Build Alpha run it end to end. Each round can have its own: • Date range • Performance filters • Robustness filters • Advancement criteria Example: Round 1 → mine until you find 2500 candidates Round 2 → first out of sample test Round 3 → Monte Carlo Permutation Round 4 → Noise Test, Vs Random Round 5 → final out of sample test You can also tell it: “Advance when 200 strategies pass, OR after 3 hours, whichever comes first.” No more manual babysitting. No more coding. Set the sequence. Hit start. Come back and see either · surviving strategies -or- · a clear answer where things broke down Both are actually useful and save a lot of time.

What if your settings or prompts aren’t good? That’s the next-gen backtester in Build Alpha. Connect Claude, GPT, Gemini, Grok, Open Router, etc. Build Alpha begins generating candidate strategies. The connected model monitors the results 
• checks for performance plateaus
• changes settings, entries, and filters
• authors new signals to try
• advances winners to robustness tests
• rejects anything that fails 
• exports code to NT8, MT5, TradeStation, Multicharts, TradingView, ProRealCode or Python • stores its learnings in local memory so the software gets smarter each time you use it Some strategies work. Most don’t. You need to find out at computer speed. Search → test → reject → redirect → repeat. 24/7

Gamma Exposure is one of the cleanest examples of why market regime matters. Positive GEX and negative GEX can create very different trading environments. And probably why 4,731,956 signal services exist around it 😅 When dealers (MMs) are net long gamma, their hedging tends to lean against price moves: • sell rallies • buy dips • suppress volatility • encourage mean reversion When dealers are net short gamma, the opposite can happen: • buy rallies • sell declines • amplify volatility • extend trends That means the exact same trading strategy either has a headwind or tailwind depending on the gamma regime. But you're not checking... Below shows Gamma Exposure (GEX) against tomorrow's SP500 range. The more negative GEX, the wilder the coming day‼️ Non-price-based signals and alternative data like this are useful to combine with your actual entry logic and test whether your strategies behave differently in different market environments. Full write up below with 36 custom GEX signals to play with. Download and go. Spread between GEX calculated on SPX vs. GEX calculated on SPX components, Z-score, etc.

We all say the same thing about overfitting: the strategy was "fitted to the noise." So test that directly. Change the noise and see if the strategy survives. This is the entire idea behind the Noise Test. Your price history has two parts. Signal, which is the part that repeats. Noise, which is the part that happened once and will never happen the same way again. Every strategy you build is fit to both, and your backtest cannot tell which one it learned. So change the noise and re-run the strategy. Here's how it works: Pick how much of the data to adjust and by how much. Say 50% of the bars, by up to 25% of average true range. Opens, highs, lows and closes get nudged up or down inside that band. Same market, same trend, same volatility (and clustering) but a different roll of the dice. Create 1,000 noise adjusted data series and re-trade the strategy 1,000 times. Now you have a distribution instead of a single backtest. How to read it: PASS The noise adjusted results do not deviate from your backtest. Most (or all) stay profitable and the distribution sits meaningfully above zero. Your rules were tracking something real. FAIL The noise adjusted results fall off a cliff. Your original backtest sits way above the distribution and the rest cluster near or below zero. No edge. You fit the one path history happened to take. If adjusting the noise erases your edge, your edge was never there, and no amount of out-of-sample data would have told you, because your out-of-sample sample data has exactly the same problem: it is one path only. Reminder: Passing is not proof of edge. It rules out one specific failure and there are plenty of others, which is why this test is only one of a dozen tests (all automate-able) in Build Alpha. Build Alpha can also optimize your parameters across the noise-adjusted series instead of the single historical one, so you are selecting parameters that work across many versions of history rather than the exact history we lived. Your price history is one sample. Your strategy should work on the others too!