How do market makers correctly delta hedge their inventory? Is it strictly hedging each delta every movement? Say we have 100 delta short so I hedge long. 2 mins later it drops to 85 delta. Would a market makers rebalance immediately or is there a threshold they’re looking at?

Is it just me, or is it kind of funny seeing people post here that they spent 6 months building a trading bot that makes 30% a year?

The Nasdaq is up around 30% this year. 😅

Rule #1: if you've actually found a profitable strategy, managed to automate it, and it consistently outperforms the major indexes over the long run, the last thing you should be doing is posting about it all over Reddit or trying to sell the bot.

If you've really built a money-printing machine, why would you risk ruining your own edge?

At this point, I don't really see much difference between people selling trading courses and people promoting the "amazing" trading bots they built.

Has anyone ever had LLM's that are monitoring and devising back-testing almost goal seek spreads that erode an edge even when you have months and months of actual trades to support more reasonable, pragmatic values? Not even an alpha, just a slight edge. This is even after I point it to the historical spread data. Time to get my tinfoil hat out

A "follow the smart money" idea I've been testing: can you rank traders by their public performance stats and just copy the top ones? I pulled a large sample of on-chain fill data to check. Short version - win rate and raw P&L are actively misleading as ranking metrics, and I'd like to sanity-check the method with this crowd.

The failure mode: a trader can post a 95-98% win rate with basically zero edge by only ever taking positions that are already near-decided. On a market sitting at 98c you put up 98 to make 2 - you win almost every time, and the equity curve looks flawless. But the edge is ~nil, and it's unfollowable: you can't get filled at 98c at any real size, and you're risking 98 to make 2. On an orderbook, by the time the "top" trader takes that level, it's already gone.

So a leaderboard sorted by win rate (or raw P&L) surfaces exactly the accounts you least want to copy.

How I filtered it into something meaningful:

- net cost basis (FIFO), not the platform's own realized-P&L field

- flag accounts whose fills cluster in the near-decided band (90-99c)

- entry realism: could you actually get filled near their price at size?

After filtering, the set of genuinely copyable accounts is much smaller and looks nothing like the raw leaderboard.

Cumulative P&L: public leaderboard ranking vs the same wallets after farmer / cost-basis filtering.

(The dataset is Polymarket - it was the cleanest fully on-chain fill data I could get - but the metric problem is general to any copy strategy ranked off a public leaderboard.)

How do you all handle this when ranking traders from public data - cost-basis and fill-realism filters, or is there a cleaner way?

keep replying to threads here about backtests and validation and it made me realize i don't actually have a clean way to tell which of my own setups is worth keeping vs which one is just along for the ride. if the account is up overall i assume everything's fine, but that's obviously not really true if one system is bleeding and another is covering for it. anyone actually separate performance by strategy or is everyone just watching the whole account like i am

I'm curious how everyone here deals with this.

Have you ever had a strategy that looked incredible in a backtest, only to completely fall apart in paper trading or live trading?

If so, what do you think was the biggest reason?

• Overfitting?
• Look-ahead bias?
• Survivorship bias?
• Slippage/commissions?
• Curve fitting?
• Data quality?
• Market regime changes?
• Something else?

Also, if you could add one feature to your current backtesting platform (TradingView, Backtrader, NinjaTrader, QuantConnect, etc.), what would it be?

I'm interested in hearing real experiences, especially from people who've had a strategy "pass" historically but fail once real money was involved.

Hey all, in this post I will be outlining the approach I've taken to my current infrastructure, data, and strategy, along with how I tested and how I've verified there's no alpha, for two reasons:

1. To help other algo quant devs to avoid my mistakes

2. Look into insight from smarter people than me.

So first things first, The Data Approach:

I started off downloading 1 minute data over all 13,000 tickers in the US stock market over the last 20 years, including some other macros such as Oil, Gold, Silver, some international ETFs, US ETFs, and VIX. This is effectively (2005 - 2026). This is my data I am training everything on.

From there I built parquet files, and caches for the 1 minute and 1 day time frames. Incorporated company splits, M&A, ticker renames, point in universe (keeping track of dropped and newly added tickers) in the S&P 500 for example. Validated data is clean.

Next, The BackTesting Approach:

I used both Combinatorial Purged Cross Validation, as well as Walk Forward Optimization (all built in house), to test my strategy. I would then also track deflated sharpe ratio, sharpe ratio, Max Drawdown, Cum Return, CAGR, amongst other metrics. I then developed a triple barrier labelling (which is based on the AFML book, and takes into account 3 barriers (profit taking and stop loss barriers, which are daily computed based on ticker volatility), and a third barrier ~ time (which I arbitrarily chose as 10 days) for a daily based trading strategy.

I also ran 4 models as baselines (S&P 500 Buy and Hold, Mom_12 (monthly rotating of highest momentum ticker per sector), and two others). S&P 500 proved to be the highest sharpe ratio and cumulative return, so that effectively is my baseline I need to beat, with a sharpe ratio of about ~0.5.

Next, Feature Set:

With the backtesting framework setup complete, I developed a set of 60 features, most of them technical or statistical indicators including (price, volatility, volume, return vs. stock's own return in a given period, return vs. s&p 500, return vs. sector average, and multiple other cross-asset correlation features).

Next, Models:

I only built two models to test up until this phase of the project. I used a LightGBM model in a supervised learning capacity, attempting to classify the daily labels across every 150 selected tickers, across my 20 year dataset. Keep in mind the triple barrier labels were computed pre-hand. CPCV would take care of look ahead bias.

I also built a linear regression model to attempt to estimate the time at which one of the 3 barriers would touch.

Next, The Dissappointment:

I ran my model with default hyperarameters, just to see how well it would be able to classify my labels. In all honesty, I anticipated it would be somwhere in the 60-70% accuracy and recall range, then with Optuna hyperparam tuning I could maybe get it up to 70-85%. These numbers are very humble comared to my grad school work where training on classification problems such as image classification, etc. would easily grant me 90%+ accuracy scores.

To my surprise, my model was only able to achieve around 50.5% accuracy, essentially a coinflip ~ zero alpha. In-sample validation showed 70% accuracy, and to further investigate, I tested which epoch gave me the best generalization accuracy ~ turned out to be epoch 2. Anything after that was overfitting heavily.

The linear regression model wasn't much better, effectively too much error to reliably generalize.

Of course there was a lot more future work to do in my algorithm, outlined in the next section, but I wanted to see even SOME promise from my classifier to be able to continue. Right now I feel completely devastated by these results.

Future Phases of my Project (On Hold for now until I decide next pivot):

1. Meta-labeling (based on AFML), a second layer on top of the models classification results

2. Optuna based hyper tuning of parameters

3. SHAP for interoperability of feature importance and model performance

4. Other interesting models (Transformers, Hidden Markov Models, Random Forests, etc.)

5. Risk Management Models

6. Execution Models (L2 based execution and fills)

FINALLY, Where I think I went wrong, What could be done better, And Opening the floor for discussion

1. AFML strictly talks about how time-based data such as (minute, hour, daily) etc. carries no significant alpha, and instead we should be looking at event driven information, which carries more information entropy.

2. I've seen a few people talk about tick-level data as where they've found success, rather than minute or hourly or daily time based data

3. Is my approach completely wrong? Is trying to predict triple barrier labels at 10 days out just a genuinely wrong approach given my feature set? What are typical classification predictions you try to make in your own algos? (Price, volatility, volume, imbalances, etc.)?

4. Finally, maybe I don't really need high classification accuracy, as Citadel I believe only achieves 51.5% accuracy, but at millions of trades, they're profitable in the billions. Maybe the real alpha is in the execution and risk management side of the algorithm?

5. I also tested across 20 years of 1 minute data across 150 tickers. Maybe sizing down my dataset could help?

I appreciate any, and all insight, PREFERABLY from smarter people than me who have ACTUALLY managed to produce profitable algorithms that trade in real markets.

(I'm not interested in how good your backtests are, I'm interested in insight from real-trading algorithms in the markets)

- Thank you for reading my long post. You are a real one if you've got this far

https://preview.redd.it/gnnh971um4bh1.png?width=1340&format=png&auto=webp&s=f2bdacb190774439f208e06a3e27e98cdf76b0a1

Okay so im successfully gating/standing aside during the mean reversion regime, and hitting profitable trades in trending regime pretty well on this instrument, but I cant help but notice it could be getting much better entries. Its using EMAs on entry so thats the place to improve. The benefit of this setup is it gets a confirmed breakout move but because of the lagging nature of EMA gets in later.

What methods can capture the move earlier? Everything ive tried hits the same wall and underperforms the current setup

Ran the simplest thing I could think of: go long when the daily close crosses above its 20-day average, exit when it closes back below, 8% stop. 3 years, $1,000, slippage included, no fancy filters.

• BTC**:** +79.5% — CAGR 21.9%, win rate 30%, max drawdown −29%
• SOL**:** +263.9% — CAGR 55.6%, win rate 36%, max drawdown −62%

What got me: both won under 40% of the time. All the profit came from winners being 3.8–4.7× the size of losers. Same exact rules, but SOL's volatility juiced both the return and the drawdown hard.

(Ran it through an MCP in Claude called Rix so I didn't have to write the code, but the takeaway is really about the R:R, not the tool.)

Makes me wonder how much a regime filter or a volatility-scaled position size would smooth that SOL drawdown. Anyone here run naive trend rules like this across assets

Does the "low win rate, high R:R" pattern hold up on the alts you've tested?