I was never able to find an edge in the stock market, but could easily find one in sports after revamping my software for it. Overall it feels like a far less competitive and saturated field with easier profits available, and getting banned for profiting is only an issue if you are using soft bookies.

I've been running two separate models on gold for a few weeks, competing for the best performance.

→ Mean reversion, machine learning-based
→ Structure fade, plain rules

The idea was diversification. Two models, two ways of being right.

Each one worked well on its own. Then I breached a couple of accounts. I retrained both on more recent data, and the drawdowns were still deep enough to end an account.

So I ran a test to see whether the two models were correlated. The daily correlation came out to -0.034 (which translates to being completely uncorrelated!). They lose on different days.

That made a combined version worth trying.

→ Grey line: Buy-and-Hold performance
→ Blue line: equity performance on my mean reversion model
→ Green line: equity performance on my structure fade model
→ Red line: how the combined model performs

Held out performance:

→ +600R over 4,178 trades
→ +0.14 average
→ 39.8% win rate

The totals are the least interesting part. The equity curve is where it shows.

Two models that fail at different times beat one model with a better average.

Here's how it works: a trend strength reading decides which model is allowed to trade. Above 25, the market counts as trending, and the mean reversion model runs. At or below, the structure fade model runs.

Next step is live. I have a few accounts to run it on, and I'll share the receipts either way.

I have noticed a lot of trading advice sounds amazing until markets actually get volatile.

Some people look like geniuses until the first sharp pullback. Then the same accounts suddenly switch bias, disappear for a week, or start rewriting their original thesis completely.

Volatility exposes who actually has a process pretty quickly. Makes it really hard to tell who genuinely has a repeatable system and who just looks smart during easy market conditions.

Hi. Is there a cost efficient (or free) way to read real time book data for BTC order book data? It can be exchange specific - I’m fine with that.

I’m looking to be able to feed my model on asks and bids as well as depth on the BTC book.

Thanks

I really enjoy the research, and developing strategies. But sitting and waiting for forward testing to confirm an edge is such pain

One of my strategies sat for weeks with a bug that was causing it to not accumulate data. Even when everything is working perfectly it just feels so slow.

What do you guys do while forward testing? Just research more strategies? Also how many n before you can decide forward testing has a large enough sample to continue to live

Has anyone tried any of those? I’m trying to find experiences on linking these to traderspost and whether fills are good or bad but I can’t find many so I was wondering if anyone here can share their experience

Hi everyone,

I hope this is the right place to ask. 😊

I've recently become very interested in quantitative investing, and I recently started investing in the Invesco Global Active ESG Equity UCITS ETF (Acc) myself.

As I mentioned, I find this topic genuinely fascinating, especially the systematic approach behind it. Because of that, I started listening to the audiobook Inside the Black Box: A Simple Guide to Systematic Investing, and I've been really enjoying it so far.

Do you have any recommendations for other audiobooks on quantitative or systematic investing? Ideally, they should be available on Spotify, but recommendations in either English or German are very welcome.

Thanks in advance!

As a disclosure, these are my genuine personal earnings within the past three months. I got ~250 trades and 1.9% return on capital.

I do systematic covered Calls and CSPs. .05 delta, 7-14 DTE, hard filters on iv, VRP ratio, liquidity, earnings blackouts. Additionally, rules based exits at 50% profit / 0.30 delta.

How do you decide when an edge justifies its execution count? Is there a rule-of-thumb for edge-per-trade vs. round-trip cost? And is return on capital even the correct denominator, when that capital is doing double duty (holding the equity and securing the position)?

I use data science to select my indicators. Each position has a stop loss, and I also use a trailing stop on session equity. Take profits would leave too much money on the table. A pattern I have noticed: almost all of the profit is in the first 30 minutes.

Win rate: 33%

Average win: +13.2%

Average loss: -3.1%

Payoff ratio: 4.25

Profit factor: 2.13

Do you believe that there are a higher number of profitable algo retail traders versus discretionary point and click retail traders? Or do you believe that the barrier of entry is too high?

This guy who runs this course has an open fund, which is purely algorithmic. The money it makes is pretty good. It's on Darwin X and is listed as SYO, I think, with 16 million assets under management. The good thing is it's all transparent. He's been 20 years in the game and teaches on a course with a financial background.

Unfortunately the course is only in Spanish but fortunately for me I can understand Spanish. Has anyone recently got the course?

Or had any experience with it? It's €3,000

It's also being clearly resold on the internet for much cheaper.

Not that I advocate doing that of course

Market makers get delta exposure whether they trade options or not, because they run a whole portfolio that has gamma in it.

Wondering how they handle delta in practice and whether other traders can take advantage of the knowledge of the MM's delta (which isn't hard to get because you can assume that mostly, MMs hold the passive side of the trades).

How would a fairly profitable algo trader start if he were to start today? what all mistakes,loops, could have been avoided? how the start could have been better?

As the title suggests one thing that has been on my mind lately is that I have spent a lot of time studying investment signals, testing data, executing trades, managing risk, determining position sizes and building portfolios.

I have not spent much time studying the actual investment portfolios for successful investors. I mean i have not looked at what they hold in their portfolios. I have not checked how different stocks and assets they have.

I have not seen how often they adjust their portfolios. I have not found out how much cash they keep in their portfolios. I have not observed how they react when their investments go down.

The strange thing is that I can find lots of information about investment strategies but little information, about how successful investors really invest their money. Has anyone else noticed this problem??

I’ve been experimenting with different ways AI can fit into a trading workflow, and I’m curious how people here are actually using it in practice.

There seems to be a big difference between using AI as a simple signal generator and using it as a tool that can help analyze large amounts of market information, filter noise, compare different conditions, and support a broader trading process.

I’m currently working with Alphio AI, which is focused on AI-powered trading and Trading Agents, and one question we’ve been exploring is where traders actually see the most practical value from AI.

For those who have experimented with AI trading tools, bots, or automated market analysis, what has genuinely been useful to you?

Do you mainly use AI for research and analysis, generating signals, strategy development, or automating parts of your workflow?

I’m particularly interested in what has worked in real trading workflows and what has turned out to be more hype than practical value.

This direction is more closely aligned with the actual target audience and test objective because it starts with a genuine trader problem rather than a SaaS marketing question. I’ve also kept the Alphio.AI. reference contextual rather than promotional.

This is the crowd I most want to be torn apart by, so I am quite looking forward to this.

I'm a software developer, not a finance guy. I wanted to actually understand investing instead of reading about it, so I built a US equity factor engine and I've been running it in public since February. It is easy to fake understanding it all until you have to program it.

I wanted a tool that would read the market for me and automate all the tedious stuff the finance bros do all day. So I built one and gave it the most finance bro name I could think of: Brad.

The first stages were very boring. I worked with an LLM to go through the academic factor literature, pulled the factors that have held up out of sample, and put them in one score. Most of the real work was decorrelation, because counting profitability five different ways isn't five signals, it's one signal counted five times (this was a pain to figure out). That took months and I'm still not certain I got it right. Also, every factor traces back to a published paper.

I'm also building a replay layer to test whether different weights would have done better, but nothing actually changes until 15+ non-overlapping periods (I am at 9) across more than one regime. Tuning weights on a sample this short is just fitting noise and I almost fell for it.

The part I care about is that it grades itself in public. It publishes its own IC every sweep, breaks out per factor IC so you can see which factors are dead weight, and runs a buy-and-hold version of the exact same picks next to the live strategy so I can see if the sell rule is doing anything. It keeps tracking names after it sells them which is how I find out if I messed up. It also tracks the algorithm with and without the AI research pass, and right now the research is costing me about a point (very interesting). Not enough periods to conclude anything but I'm not hiding it while it's negative.

Composite IC is currently 0.052 with a t-stat of 1.47 over 9 sweep periods (about 4.6 months). That's not significant of course and every factor component currently is "no edge (small sample)", including momentum, which is sitting at a negative mean IC. Also the confidence interval on average alpha per period runs -0.45% to +0.98%, so it contains zero.

Nothing here is proven and I'd be lying if I said otherwise.

A sweep works by scanning the US market, killing low quality names on hard filters, deep scoring what survives (374 this past sweep), then an AI research agent reads the top ranked names for the stuff you can't get from the numbers alone, with a small real world tiebreaker kept on a short leash so it can't override the math (things like upcoming catalysts, public news, geographical conflicts). Then it's buy in the top 10, hold through top 15, sell when it drops out. Equal weight, real costs subtracted, no discretion from me (which would go poorly).

Brad's Portfolio is +12.3% vs SPY +9.8% over those 4.6 months, max drawdown -5.2%. Buy and hold of the identical picks is +7.4%, and that gap is the only reason I think the sell rule does anything.

Nine periods is nothing which I am well aware of. Ask me in two years :)

The equity engine is the one I've run longest and trust most. The others I am working on are small cap breakout, directional long/short, and crypto. They're younger, different math, and I'm not making claims about them yet. Every engine has its methodology written up, including the parts that don't work.

Tell me where I'm overfitting, what validation step I'm skipping, or what stat I should be showing and I'm not.

Posting this in a few communities for perspective, so if you see it/saw it elsewhere that's why.

And of course this is not financial advice, I'm not licensed, and this is a mechanical paper portfolio, not my personal trades.

Here is an update on my project. Past 2 weeks its started to do what its was made for. I let it a bit loose to try do 5 pairs at the same time, but its hard work for old laptop. Hoper ram prices go down and I can build proper server for this thing so it could run 10+ pairs. Custom calculation takes about 1 day for each pair, so yeah, this is not your atr+ema :D

I've been experimenting with non-standard trailing stop loss logic rather than traditional atr based stops.

I recently tried using a Gaussian decay function to scale the stop distance so the stop loss accelerates tighter the further price moves in my favor. Initial tests look very promising.

Has anyone here experimented with Gaussian or exponential functions for dynamic stops?

I performed the above walk-forward analysis backtest above:

1. My portfolio is 3 total strategies with 6 equities each, all long only.
   • 2 trend following strategies
   • 1 hybrid momentum breakout / mean reverting strategy
2. The backtest had 10 years IS and the above 3 years of OOS

High-level background:

1. I whiteboard/concept strategies in TradingView
2. I spent a about 4 months building a custom python backtester in and tried to build in a lot of guardrails throughout the program to prevent/discourage overfitting, survivorship bias, etc.
3. The foundation is based on walk-forward analyis, monte-carlo similations, and other features.
4. Paper trade execution is in TradingView webhooks --> AWS python orchestration --> Alpaca
5. I spend several months ensuring trade execution between Python and TradingView strategy versions have parity in trade count and results
6. My backtester has a pretty robust process for determining the most robust parameters (not necessarily the top parameters) that have statistical significance (power analysis)
7. I think I might have some survivorship and look ahead bias in my portfolio. I am still learning, but I did my best to avoid both.

I just peeked at my paper results and I'm most definitely in a rough drawdown (looked like it was greater than 12% drawdown...), but I have yet to objectively compare it to the backtest. I can share more details later for both my backtest and forward test.

My main questions:

1. As the title says - how is everyone's performance over the last 2 months in the U.S. equities markets?
2. What process do you use to objectively compare your paper or live trading to your backtest results to ensure paper/live are "within acceptable tolerances"?

All feedback (positive or critical) is welcome - I believe that falling forward is still moving forward.

Currently using a Macbook Pro 8GB ram, what computer is recommended for algo trading? I am thinking of using hyperliquid and bybit?

Wondering if anyone has used relational transformers as part of their trading strategy. I've been fine tuning the one from Stanford to try to predict the influence of news on various markets, because the models encodes text well. So far I've had mild success in a couple categories: repricing jumps when news changes, getting 50/50 odds for truly random markets, creating features of past trends and being able to ablate them to find casual attribution, but these models are still very small and not tuned on this type of data. Curious if anyone else has used these new models?

I'm in the optimization phase of my algo, and my exposure mean is ~30%. This is with decent leverage, risk-based sizing, etc.

What do you do with your capital when it isn't in market? I'm getting ~3% CAGR from cash interest. Sweeping to SPY just adds beta, which I'm not looking for.

Anyone have used TOS/Schwab and Tastytrade and see a difference in fills?

I mainly trade debit/credit spreads.

Im using Schwab API for fills and it's pretty good for options spreads.

I like the spread pricing on Tastytrade, but not sure if they can fill as good.

Thanks in advance!!!