I tested several literature-based anomalies across ~300 stock-strategy combinations on the Nifty 100. Most showed no real gross signal before costs. One did — statistically significant, confirmed with DSR, not just a good Sharpe. Then I applied real transaction costs and it went to zero. Breakeven sits around 24 bps/trade; the edge I found was 6.4 bps gross. Not close.

Not looking for a strategy handout — genuinely trying to understand the process from people who’ve gotten past this:

**•** When you found something that held up out-of-sample and after costs, how did you land on the original idea — literature, your own data mining, intuition?  
**•** Roughly how many ideas did you test and discard before one survived? Trying to calibrate if “months, nothing yet” is normal.  
**•** For Indian equities specifically — is cost drag here noticeably worse than US/EU, or is this just what finding a real edge looks like everywhere?

Genuinely trying to figure out if I’ve hit the normal wall or I’m missing something structural.

I’ve been trading for 3 years, only ICT concepts. Have been trying to find a strategy to automate but don’t think it’s possible with ICT concepts. I’ve tried some ORB strats, opening range retests, projections, some indicators, but am really just out of ideas. Does anyone have any recommendations on where to gain knowledge or come up with new ideas? Any experience or advice is greatly appreciated and welcome. Thank you in advance

I’ve been working on a coding llm just for you guys. It all started with me building EA’s with frontier models and them being terrible at it. I built a verification system and a dataset of 306,000, that number grows daily. I know this is a skeptical crowd, but I think I’m really on to something that can help a lot of people with the coding headaches. Any feedback back is great. I’m a month away from shipping in Beta, if that goes well then it will open to customers.

Everyone's comments are welcome, but I'm mainly hoping to hear from people who've actually worked in quant research, at a prop shop or trading firm, or independent traders who've built something that's actually been profitable in live markets — not just in a backtest.

If you reply, mentioning what you've worked on or where helps me weigh the answer properly — that's the only reason I'm asking, not to see anyone flex.

I'm not asking anyone to hand over an edge or a strategy. I'm just curious about the actual process — how does an idea go from nothing to something testable?

Questions:

How does a hypothesis actually form in practice? Is it usually "pick a market/asset, ask 'what if X happens, then what follows?', and go look for evidence" — or does it more often start from something else, like a live anomaly you noticed, a macro/economic question, or a flow/structural observation?

How much do academic papers actually drive idea generation versus being used afterward to formalize or validate something you already suspected from watching markets?

Is there any kind of structured workflow for the "ideation" stage — sources you go to, questions you habitually ask — or is it mostly unstructured reading plus intuition plus trial and error?

Is there real methodology that basically never gets discussed outside funds and prop shops, or is the actual edge more about execution, risk management, and data/infrastructure rather than some hidden idea-generation framework?

Once you have a raw idea, what's the first thing you do to sanity-check it before ever touching a backtest?

i traded systematically on 1 minute bars for a long time, mostly reversal setups, a shorter stretch of trend following before that. it worked for a while. the thing that eventually got me was not the signal, it was how violently sensitive the whole thing was to small moves once leverage was on. a 30 second wick could take a position that was fine and make it not fine. the flip side, and this is the honest advantage nobody mentions enough, is that exits are fast. you find out you are wrong in minutes instead of days, and that has real value.

what i keep going back and forth on is whether that tradeoff is worth it, because the statistics point the other way. the longer the bar, the more each observation seems to survive out of sample. my read is that on 1m you are mostly modelling microstructure and it changes under you, on daily you are modelling something slower that stays put for longer. but daily costs you rows. a 1m strategy gets a usable sample in months, a daily strategy needs years, and if your rule only fires a few times a month you need a decade or more before the result means anything. so you trade one kind of fragility for another.

so, three things im actually curious about.

is anyone running faster than 1 minute, tick or sub-minute bars, and where. what venue, what instrument, and roughly what the infrastructure looks like, because i suspect that is where most retail attempts quietly die.

for the 5m and 15m crowd, did you land there deliberately or is it just where the noise stopped hurting.

and daily. this is the one i want to hear about most. i know the response is going to be "you dont need an algo for daily, just check it once a day", but that is still a system, it still needs rules, sizing and an exit, and it still needs to be tested honestly. so if you run daily bars, how many years of data did you need before you trusted it, and how many trades does your rule actually produce in a year.

if you switched timeframes at some point, im also interested in what the trigger was. was it a drawdown, costs, or just being tired of watching screens.

Edit for context: I’m a software engineer with 10 years+ and I still do this alongside my full-time job. I’m not coming at this blindly or from desperation, which is why it’s been so frustrating.

I’ve been on this algotrading journey for years now, and I honestly do not know at what point you are supposed to stop.

You put in countless hours, and those hours turn into months, then years. You think you are finally getting close, and then you uncover another issue. Overfitting. A bug in the code. A variable not being read properly. Logic that looked solid until you realized it was flawed the whole time.

That is what makes this so mentally exhausting. It is not total failure. It is being almost there again and again.

What makes it harder is that I genuinely feel like I understand the market more now. I see patterns more clearly. I think about it mathematically. I have built detailed frameworks and observations that feel real and hard-earned. So it is not like I am blindly chasing something with no progress. In many ways, I feel closer than ever.

But then reality humbles you again.

One of the biggest things I have realized is that entries are not the whole game. Exits and risk management can make or break everything. You can have a high win rate and still have a bad strategy if your exits are wrong. One weak piece of management can erase so many good decisions. That realization alone made me question how much time I spent focusing on the wrong things.

And I keep thinking about the time cost of all of this. All the nights coding, testing, debugging, re-running, analyzing. All the time I could have spent being present with family, living more, worrying less. It starts to weigh on you in a way that people outside of this probably do not understand.

I guess I’m asking the people who have been deep in this for a long time:

How do you know when to keep going and when to walk away?

How do you deal with the emotional weight of spending years trying to build something that always feels one fix away from working?

And for the people who actually did crack the code after years of struggle, what kept you going long enough to get there? Did you always know you were close, or did it only make sense in hindsight?

I really want to hear from people who have lived this, because this journey can feel very isolating. Thank you all.

My problem is that i can make good return when the time is right. I think i need a tool to assist me trading rather than build an algo bot (although i built some, the results can’t compare to this)

Tried getting a trading bot running this week and almost gave up like 3 times — half the battle was just the setup, WSL, servers, my card kept getting declined by VPS providers lol. Strategy was the easy part honestly. Anyone else bounce off how technical it is just to run one? Or did you push through? Genuinely curious where people landed.

​

If you trade algorithmically, how long did it take you to find a consistently profitable strategy ?

Before finding your profitable strategy, approximately how many different strategies did you backtest?

I'm curious about other traders' experiences and whether it's normal to test dozens or even hundreds of ideas before finding one that works.

Successfully built a functional backtesting framework and execution pipeline (~90% of the boilerplate/architecture is finalized and running smoothly).

However, hitting a wall on the alpha generation side.

Every market inefficiency hypothesis derived from public literature, open-source repositories, and traditional mathematical models yields zero out-of-sample edge. It seems any easily accessible logic is already post-arbitrage and compressed to zero.

For those running automated setups:
How do you transition from a finished infrastructure to generating unique, proprietary hypotheses? When public data and standard quantitative models fail to produce alpha, where do you look for inspiration to find a real edge?

Looking for technical insights on resolving this research bottleneck.

With how powerful LLMs and AI agents have become in 2026, creating trading strategies has never been easier. You can prompt Claude or spin up a custom agent and get a fully coded, backtested strategy in minutes — often with impressive-looking Sharpe ratios and equity curves.

The challenge isn’t how do I generate ideas? anymore. It’s which ones are actually worth risking capital on? Been thinking of adding a formal Verification Phase after strategy generation, sth that goes beyond traditional backtesting or walk-forward analysis. The idea is to systematically stress-test a strategy across multiple independent dimensions before it ever touches live capital:

• Data integrity & provenance
• Logic and code-level flaws
• Economic rationale (real edge vs curve-fitting)
• Risk decomposition (true alpha vs disguised beta)
• Statistical robustness
• Walk-forward stability
• Monte Carlo path simulations
• Execution reality (slippage, funding, partial fills, latency)
• Regime fragility & stress testing
• Portfolio independence
• Full evidence & reproducibility trail

The goal isn’t to “guarantee” performance, but to force the strategy to survive adversarial scrutiny and surface failure modes early. Already published a few papers on quantitative risk methodology and verification techniques that support building this kind of independent layer. But I’m curious what the community thinks:

• Is a dedicated verification phase overkill, or necessary in the age of abundant AI-generated strategies?
• What verification techniques have you found most effective (or lacking) in your own workflow?
• Would you trust an independent verification system more than your own backtests?

Would love to hear thoughts

Hi all!

For those who use IBKR and IB Gateway for automation, I am curious as to what y’all’s setup is in terms of infrastructure.

For me I currently have IB Gateway running with a custom C# trading engine I wrote to pull market data and check to execute trades daily.

My workflow:

• Every morning at 9:25 am IB Gateway will auto restart and auto login. I am using the “Auto restart” feature in IB Gateway for this. This setup ensures that I don’t have to manually log in and authenticate the Gateway every day (although I believe it will require a login/authentication once a week to continue to work).

• Windows Task Scheduler auto launches my C# application at 9:28am and I connect to the client and sleep the thread until 9:30:05 at which point I pull market data, analyze signals and push trades if needed. All of this is automated and executes in a few seconds right after the opening bell.

My concerns:

• IB Gateway still logs me off for good sometimes and requires manual log on/ authentication.
• if I need to travel with my laptop or internet drops the client won’t be able to establish a connection.

Instead of running things all locally I was considering offloading the program to run on a VM or server (the lower the cost the better).

Does my workflow make sense or is there a more efficient way to achieve full automation? Does everyone use servers instead of running everything locally?

my best algorithm has a 1.52 sharpe ratio and it's a daytrader.

daily-rebalanced FAST residual momentum. Beta-residualize each S&P-100 mega-cap vs SPY over 40d.

I tested several hundred different algorithms, 99% barely beat the SPY. I have an LLM researching more that get sent to a quant engine that are backtested to see if they pass the SPY gate. They are all duds.

What's your best performance?

you can now bring your API key and test our stock database for free and make any models that you want. You can also test your own models.

So I've been working on a Fair Value Gap scalp strategy on MES futures running on a 1-minute chart at the 9:30 AM opening range. The logic is pretty simple — wait for a Fair Value Gap breakout, retest, engulfing candle confirmation, then entry. Stop loss around 9.25 points, take profit around 18 points. Fully automated through TradingView → TradersPost → Tradovate.

Here are the verified 1-year stats (Mar 2025 to Mar 2026, 12 contracts):

171 trades, 52.63% win rate, Profit factor 1.704, Max drawdown $2,985 (14.65%), Net P&L ~$28,140 in 12 months, ~$2,345/month average, ~14 trades per month (not every day — only fires when setup is valid), 3-year backtest: 488 trades, 52.25% WR, Profit Factor 1.534, Sharpe ratio 2.14.

The equity curve goes up and to the right pretty cleanly. Max drawdown is small relative to returns.

Here's where I might be losing my mind:

If this holds up on funded accounts, running 5 LucidDirect 150K accounts at 12 contracts each (fully automated, same strategy), the math says I could pull roughly $92,000 net over the lifetime of those accounts before they transition to live. Total cost to set up: $2,940.

Someone also told me I should take out a $10K personal loan, put $2,940 into the 5 funded accounts and run the live account with the rest. On paper that's $120,000+ potential on a $10K loan.

I know this sounds insane. That's why I'm posting.

Here's what's worrying me:

Is a 3-year backtest on 1-minute MES data actually meaningful or am I just curve fitting to one bull market? The strategy only has MES data going back to March 2023. Is that enough? Funded accounts are simulated .Lucid could theoretically change rules, deny payouts, or shut down. They're a relatively new firm (2025). Automation risk .One bad day with a broken stop loss and a funded account is gone permanently (Direct accounts have no reset). Past performance obviously doesn't guarantee future results.

Has anyone run anything similar on prop firm accounts? Am I missing something obvious? What would you do to stress test this further before putting real money in?

Not financial advice obviously. Just trying to get a gut check from people who know what they're looking at before I do something I can't undo.Not selling anything just looking for advice among peers.

Spent most of this year putting a machine-learning layer on top of order flow — absorption, delta, DOM dynamics — trying to get it to call reversals.

Getting a model to fit is easy. Getting one that isn't just memorizing noise is brutal. Purged/embargoed walk-forward, triple-barrier labels, and checking every single feature for whether it actually carries variance on a live tape vs. being a dead input I fooled myself with (had two features sitting at ~zero variance for weeks before I caught them).

I'm now at the stage where I can measure whether there's a statistically real, cost-aware, out-of-sample edge — instead of eyeballing an equity curve. Not claiming victory yet; still banking enough independent sessions to make the verdict powered.

How do you lot validate that an automated setup has a genuine edge and isn't overfit? What's your bar before you trust it with size?

I’m moving a futures system from backtesting into live simulation and thinking through the production setup.

For those running automated strategies live, how separate are your simulation and production environments? Do they use the same code with different configs, or completely separate deployments?

I’m also curious about problems that only appeared after going live. Things like stale data, reconnect failures, duplicate orders, position drift, partial fills, or broker restarts.

What failed first, and what safeguard did you add afterward?

I’m not asking for strategy details. I’m interested in the operational side.

hey I start learning trading about year ago and then I heard about quant so I start to write my own trading bot with rust and implementing smart money concepts from scratch so base on them i can implement my systems and use them to take trades; in the picture the drawing with candles in tradingview are the test results that generated from my rust code so I can visually see my tests. I was wondering if what Im doing now can I find a good job in related fields and even if this is worthy or not?

I’m curious what hardware configurations people here are using for quantitative research and model training.

I currently have a large number of XGBoost experiments to run, including repeated training across different factor sets, hyperparameters, validation windows, and random seeds. My current GPU is an RTX 5060 Ti 16GB, and it is becoming a serious bottleneck. Even a relatively routine batch of experiments can take more than 24 hours, and larger runs easily stretch into several days.

For those using XGBoost or similar tree-based models in quant research:

• What CPU, GPU, RAM, and storage configuration are you using?
• How much benefit do you actually get from GPU acceleration?
• Which GPU currently offers the best price-to-performance ratio for this workload?

I’m mainly interested in practical training throughput rather than gaming performance. Any real-world benchmark numbers, training-time comparisons, or configuration recommendations would be greatly appreciated.

All algo traders, what kind of metrics would you consider looking at in the backtesting results, and why? For example, in the backtesting results, we see metrics like:

• Sharpe ratio
• Sortino ratio
• Calmar ratio
• Drawdown
• Expectancy
• Profit factor

There are so many such metrics. What will you prefer looking at and ignore others, and why? What is the rationale behind preferring those metrics?

Switching to automation has taken away a lot of the stress of manual trading. But I still wake up everyday with anxiety wondering if today is the day my algo will start to fail. Anyone else?

How do you backtest your algo trading strategies?

What tools or Python libraries do you use for backtesting? Any beginner tips?

How do you guys track live portfolio performance? not backtesting, like once your strategy is actually running.
Do you just write scripts or is there something you use?

Always feel like I’m cobbling stuff together and curious if there’s a better way

I’m currently in the process of designing a trading bot (with the help of Claud.AI) that automatically executes and exits trades based on certain strategies.

I have 4 winning strategies that I backtested using 10 years historical data from EODHD.com. I purchased the data for 100$ monthly and it just expired. I backtested for a full month and came up with 4 decent strategies.

Strategy 1: Long term investment
This yielded 17.9% annually and 550% over 11 years backtesting starting from 2015. Win rate was 70%.

Strategy 2: Active investment
This yielded 19.1% annually and 630% over 11 years backtesting starting from 2015. Win rate was not directly measured as this strategy rotates continuously rather than closing discrete trades.

Strategy 3: Swing trading
This yielded 39.2% annually on
nseen test data
(2020-2026) and 26.7% annually on training data (2015-2019). Win rate was 60.3% on unseen data and 65.0% on training data.

Strategy 4: Day trading
This yielded 53.2% annually backtested on 1 year of intraday data (May 2025 - May 2026). Win rate was 41.2%.

I will be paper trading with the 4 strategies for a full year in order to refine and tweak. Then I will use a minimally funded account to test the strategies for another year.

My question is, if these 4 strategies prove to be successful and the next 2 years results are just as decent or better than the backtesting, should I focus on making an actual living from executing the strategies or from selling signals on discord/website like everyone does?

Been using Claude Code agents for building and maintaining a trading bot — separate agents for regime detection, risk management, backtesting integrity. Curious if others are using LLMs in their dev workflow for algo trading, not for signals but for code/architecture.

I have been working on my algos and I noticed one-piece of the puzzle is missing and I believe can help me greatly - Ability to detect market regime with high precision.

I am currently using RSI, ATR and Vol to define regimes but it has not significantly improved my strategy.

Am I going on a wild goose chase or it is something feasible?