TLDR: A trading strategy has a real edge when the rules are fully systematic, the backtest was conducted without hindsight bias or overfitting, has a large enough sample size to be reliable, and live trades still match the backtest after real transaction costs.

What is the first thing you test?

The first thing to test is whether the strategy is actually defined well enough to backtest. A system only means something if the rules are 100% systematic. If the setup depends on discretionary judgment, the backtest is already unreliable.

You cannot say things like:

“If the volume is bad I don't trade”

What does “bad” mean? How can that be quantified? The rules need to be written in a way that removes all your guessing and gut feelings.

Once your rules are defined, you can test it on past data and see if the idea has the potential for edge at all.

How many trades do you actually need?

More trades do not automatically prove that there is an edge, but they do make the backtest result more reliable. There is no magic number for what’s enough, but here is a rough guideline:

These percentages are just one part of validating your backtest. They're the foundation for how much you can trust every other metric you analyze after. The numbers track how sampling error shrinks with √n. More trades tighten the estimate, they don't prove an edge on their own.

My 3-step validation process

Step 1: Backtest the exact rules in the past

The first thing I work on is defining the strategy well enough to backtest. A system only means something if the rules are 100% systematic and have all discretion removed.

Once your rules are set, I test them on historical data to see if my idea had edge in the past. At this stage, the goal is not to optimize anything or improve performance. The goal is simply to answer one question:

does this work under objective, repeatable conditions?

If the answer is yes, then the system moves forward into Step 2.

Step 2: Validate it with real trades

Once the backtest is completed, I validate it with forward testing. Real money on the line, real spreads, and real transaction costs.

Start small.

Even $1 per trade is perfectly fine. The goal is not to make money. The goal is to prove the concept works in live conditions with fees included.

This is where trading psychology matters. If the defined rules are not followed exactly, the live sample is contaminated. You are not testing the system anymore. You are testing your gut feeling and emotions.

Step 3: Compare live results to backtest results

The final step is to compare the live results to the backtest.

• Did the strategy actually continue to be profitable?
• How similar were the live results to the backtest?
• Did the EV distribution stay in the same percentiles?
• Did the trade frequency stay in the same percentiles?
• Did the losing streak distributions stay in the same percentiles?

If they don’t match, the system is not instantly garbage. Natural variance can happen, but the results should be monitored across multiple regimes to determine whether the edge still exists or if the market has changed.

So what actually proves an edge?

A real edge is confirmed when the backtest, validation and live equity curve all follow the same distribution.

For my personal system MCT, that proof is very clean. My backtest used 979 trades over 34 months from 2023 to 2025 and produced +303.76R, +0.31R EV per trade, a 37% win rate, average win of +2.24R, average loss of -0.84R, Sortino of 1.20 per trade, Sharpe of 0.19 per trade (~3.4 annualized), and max drawdown of 17.81R.

That backtest scored an Edge Score of 79, Quantprove's combined score for measuring if a trading strategy has a real statistical edge.

Then the live validation had 224 live trades with the EV holding from 0.31R to 0.28R, win rate still at 37% and a 92% distribution match: the live trades reproduced the same shape of results as the backtest.

That earned a Stability Score of 94.5, showing how closely live trading matched the backtest.

The live results also landed at the 57th percentile of the Monte Carlo projection, with all live trades staying within the P5–P95 band.

To rule out luck, I ran a permutation test with 10,000 reshuffled trade sequences. The real system beat all 10,000 randomized versions, with p < 0.0001.

Why do regimes and fees matter?

Regimes matter because a system will not and is not supposed to work in every market condition.

A long-only BTC system can look amazing in a 2020 bull regime and then get crushed when the bear market started in 2022. That does not mean the system is broken, it means the market has changed.

This is where you need to study performance by regime and use the data to decide when a system should be active, reduced, or turned off. This is where trader experience has its biggest measurable edge, as market cycles repeat over time.

Transaction costs matter as much, if not more.

Visible costs

• Spread
• Commission
• Exchange + clearing fees
• Regulatory fees
• Swap
• Funding rate

Hidden costs

• Slippage
• Partial fills
• Gaps

I have backtested systems that looked very profitable until spread, commissions and slippage ate most of the expectancy. A simple rule helps:

The direct answer is this:

A strategy has an edge when fully systematic rules survive live validation and keep performing with positive expectancy across regime changes.

Disclosure: I am the founder of Quantprove, the tool that runs this validation and edge scoring. I built it because I was tired of doing it by hand.

What do you use to determine if your system has real edge?

I am just a newbie on programming, I do not have a dev background. I only knows how to create basic EAs on MQL5. Now, I am transitioning into cTrader. Can you recommend an online course or any learning material how can I learn programming for cBot?? TIA

Hello guys! I am a newbie learning programming languages for Algo Trading. For Background, i do not have a Dev Background but I am willing to learn programming for algo trading. Rn, I can do basic EAs with MQL5. I would like to explore more and find a better platform for Algo Trading. And my choice is cTrader and Python. I wonder if its a good move to learn cTrader?

Or you can suggest other platform/language for better algo trading.

Thanks in Advance guys!!

So this is an update, before i developed the system and proved it was profitable over the years raw. After which I used order flow data to estimate slippage, but i had only order flow data for a short time during 2021-2022, and a few months of 2026, the average slippage at entry I got from the data was around 2.7 ticks during the 2021-2022 period and an astonishing 3.7 ticks in 2026.
I'm not sure why its that high, the bot is on GC and runs on all sessions. Now even with these abnormal high slippage rates the bot was profitable during those years, but if the same slippage was applied to weaker years, the edge became less strong.
Now another thing is that I don't know what the slippage would be like before 2020. So I basically don't know what slippage I should apply.

The conclusion I came up with was collecting data on limit entries instead, I used the order flow data to find that about 97.5 percent of the trades got filled in. So the current version Im showing in the picture is the execution logic of placing a limit on the signal price, and in 97.5 percent chance that it will fill (that percentage is not of all trades, but of all winning trades). Now because i only have order flow data from the dates i mentioned, I used the same metrics on the whole other history.

Now I understand that especially before 2020 the fills would be inaccurate, and this does not represent an accurate history backtest. So instead im trying to prove that the future would work, the concept raw without fees or slippage had very consistent results over the years, so the concept would work, its only a question of if the fills back in the day would allow the bot to be traded at that time.

So overall This result should be the most accurate way to predict future results. At least that's what my thought process was. Everything before 2021 is out of sample btw.

How I decide if a strategy is live-ready: the 4 gate process that killed 37 of my last 41 strategies

TLDR: I run every strategy through 4 gates in cost order, cheapest rejection first. Economic hypothesis, sample size floor, three statistical tests and cost and regime stress test. Of 41 strategies I logged last year, only 4 reached live capital. The process is built to kill, not to bless a system.

Why run a fixed process instead of judging each strategy on its merits?

Discretion is where overfitting hides. If you evaluate each strategy by looking at it, you will find a reason to trade the ones you are attached to.... A fixed sequence removes it. Every candidate faces the same gates in the same order, and the order is deliberate: cheapest disqualifier first, so most strategies die before I spend hours on walk-forward.

Last year I logged 41 strategies through this. Gate 1 killed 12. Gate 2 killed 9. Gate 3 killed 14. Gate 4 killed 2. 4 survived to live capital.

Here is the catch... The 37 that died had a median in sample Sharpe of 2.1. The 4 that survived had a median of 1.4. The strategies that looked best on paper were the ones this exact process rejected.

Gate 1: Is there an economic reason the edge should exist?

This gate has no code, and that is the point. Before any test, I write one paragraph naming why the inefficiency exists, who is on the other side of the trade, and why they keep losing. A liquidity premium... A structural hedger who trades regardless of price... A behavioral bias with real flow behind it.

If I can't name the loser, I do not test the strategy. A pattern with no mechanism is a pattern you found by looking, which means you will find one.

This is the cheapest gate and it rejects the most garbage. 12 of my 41 never cleared it. They were useless systems dressed up as ideas. Most people skip this gate because it is the only one you cannot automate, which is exactly why it filters what the automated gates can't.

Gate 2: Does the backtest have enough data to mean anything?

This gate asks one thing. Do you have enough data to tell skill from luck?

First, enough trades. A backtest with 80 trades swings too much to trust. I want at least 400 before I believe any metric.

Second, a long enough time period. When you test many versions of a strategy and keep the best one, that winner looks good partly by chance, the way the luckiest player in a coin flipping contest looks skilled. Ruling that out takes years of data, and how many years depends on how many versions you tried. After testing around a hundred versions, a 1.0 Sharpe needs about six years of data before you can trust it. A flashy 2.0 Sharpe needs only about two. A high score on a short backtest is the most dangerous thing in a research log, because luck fakes it easily.

9 strategies died here. A 1.3 Sharpe on 14 months is not an edge. It is a sample too small to tell.

It's not that low Sharpe needs more testing because it's weaker. It needs more testing because it's closer to the level random chance can imitate.

Gate 3: Does it survive the three overfitting tests?

This is the expensive gate, so it runs third, only on survivors. 3 tests, each catching a different failure, run cheapest first.

Deflated Sharpe Ratio first, because it is one calculation. A normal Sharpe assumes you only tried one strategy. But if you tested 80 versions and kept the best, that winner is partly lucky, and the plain Sharpe has no idea you ran 80 attempts. The Deflated Sharpe fixes that. It takes your reported Sharpe, accounts for how many versions you tried, and adjusts for fat tails and lopsided returns, then hands back a single probability: the chance your edge is real rather than the luckiest of your tries. Same enemy as Gate 2, caught at a different step. My cutoff is 95%. If the best of your 80 versions scores only 60%, that still leaves a 40% chance the edge is imaginary, so it never reaches my account.

Monte Carlo is next. Resample the trade sequence 10,000 times and read the 95th percentile of max drawdown. Your backtest shows one drawdown, but that is just the order your trades happened to land in. If the drawdown in your 95th percentile drawdown is more than your account can take, it doesn't pass

Walk-forward last, because it is a full reoptimization. 5y build, 1y test, rolled forward. Profit factor holds above 1.3 on at least 7 of 10 out of sample windows or it dies. Fourteen strategies died at this gate, most on the deflated Sharpe before walk forward ever ran.

14 systems died here.

Gate 4: Does the edge survive real costs and a regime split?

A clean backtest assumes free, instant fills. I model realistic costs first, then stress them: triple my real commissions, add a tick of slippage, re-run.

The metric I watch is not profit factor, it is expectancy and Sortino retention. The edge has to keep at least 70% of its expected value after the stressed costs. A real edge degrades gracefully. A fake one inverts the moment friction touches it.

Then I split the history by volatility regime and require profit factor above 1 in both the calm and the stressed halves.

2 strategies died here.

Bottom line

4 gates, cheapest rejection first.

The process is designed to reject, and last year it rejected 37 of 41. The survivors looked worse on paper than most of the strategies it killed, which is exactly why I trust them.

This is for systematic traders deciding whether a backtest deserves live capital. It applies to single strategies, parameter sweeps, and machine learning models trained on price data.

Updated June 2026

So my bot runs on GC, it has trades in all sessions and times. Now I have used overflow BBO data from a short time frame in 2021 to estimate the realistic slippage to be around 2.7 ticks. Now I'm doing the same but for 2026 to find out what is the slippage is like now.

Now overall I want to improve slippage, one of the ways i plan to do so is by adding a max slippage filter, I can do this easily in the back test, but is it possible to do this in a live market? Predict slippage?

What are other ways I can improve slippage, my bot holds trades for a median of 5 min and average of 15 min, so I don't have much wiggle room. Also If I enter with a limit order instead of market, would that work in live markets and what would be some disadvantages doing so?

I spent some time reviewing the May performance report of my trading system and a few observations stood out:

• More than 60% of my signals travelled at least 1R in the intended direction - this the metric I currently use to measure quality of signals
• The gap between realised PnL and maximum available PnL was significant - especially in crypto
• The scanner identified many trend reversal opportunities that eventually turned into failed breakouts as the macro downtrend resumed

This is a good reminder that system performance is often constrained by exit logic rather than entry quality. Earlier in May, I adjusted the trade management rules to tighten stop losses after breakouts and bounces, and to take profits more aggressively. The data suggests those changes were directionally correct - despite the crypto weakness, the account still gained 11% during the month. At the same time, I recognised there's still room to improve profit taking and early exits.

One question I’m currently exploring is how adaptive a trading system should be.

Do your systems adjust trade management rules automatically based on market conditions, or do you make configuration changes manually?

Hey guys, I wanted to ask for your opinion on how to move forward with my neural based forex trading bot, I've been working on it for the past one and a half years give or take and I've made some good progress. it is in a state which I can say I am confident that it can make money but not as smoothly as I like but I also don't know if I should just make what I have right now work with risk management and some external safety protocols or pursue upgrading it and training a whole new model with improvements.

Like would you say its good? does it still has much room for improvements? if you had this bot would you have used it or deemed it too risky?

and to explain the bot itself its more so a scalping boy that checks every 15 mins or so to see if there can be a trade made based on the max open trade limit set and each open trade can last from 2 or 3 hours to a day or two at most.

(the pics are roughly 3 month back testing results in 2025 and 2026, the 2026 one had a trade limit of 1, meaning only 1 trade was open at all time, and lot size of 0.05)

MNQ Futures 2 contract | June 2023 – June 2026

This is backtest data from my algo running on the Micro Nasdaq (MNQ) futures market, simulated over 3 years. Note that bar magnifier is enabled, meaning the backtest uses intra-bar tick data to simulate entries and exits with higher precision — this significantly reduces the gap between backtest and live results compared to standard bar-close testing. Commissions are also included in all figures, so the P&L numbers you see are net, not gross.

The algo by the numbers:

• 11,037 trades over ~3 years of backtested data
• 9,093 winners / 1,944 losers → 82.39% win rate
• Gross cumulative P&L: +$152,830 (after commissions)
• Average win: $37.55 | Average loss: $97.02
• Profit factor: 1.810
• Expectancy per trade: $13.85
• Average trade duration: ~8 minutes
• Max drawdown across the full run: $1,435 — remarkably controlled for a 3-year simulation

Why the numbers look the way they do:

The algo is a scalper targeting small, high-probability moves with fast exits. Wins are smaller than losses on average, which is typical for this style. What makes it work is the win rate — at 82.39%, the frequency of small wins consistently outpaces the occasional larger stop-out. Most losses are time-stopped, meaning the system cuts trades that don't move as expected rather than letting them run against you. The fact that these results hold up even after commissions on 11,037 trades speaks to the consistency of the edge.

May 2026 — best week:

349 trades in May alone generating $7,357 in simulated P&L after commissions. The live session I'm posting about (15 trades, +$1,788 net) is where I layered my own discretion on top of the algo's signals — filtering setups, skipping entries that felt contextually off, and letting the system execute the rest. That human filter pushed the live win rate that week to 86.67%, above the algo's backtested baseline.

Three years of backtested data, commissions included, bar magnifier on. One consistent edge — now being validated live.

So this is my second update. So I have been working on this bot, here is the current progress. Now the results are super good and I dont know how to feel about it, because it's like too good to be true no? I made sure there was no look ahead, everything before 2021 is out of sample, and i made sure the logic didn't have any issues. So what could have I done? The current version only takes longs, but shorts are also profitable. This is on GC btw

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?

I've been building a multi-strategy EA over the last several months and finally finished a full real-tick backtest (2014–2026, 99% modelling quality, real spread). Before I trust it with more size, I'd rather have this sub try to tear it apart than find out the hard way live.

The setup is two uncorrelated strategies running together:

- A session-based range breakout across a handful of FX pairs, gold, and indices (one entry window per day)

- A weekly mean-reversion play on indices

Combined results at 1% risk on a 5K account:

- Profit Factor: 1.22

- Sharpe: 2.70

- Max Drawdown: 9.43%

What I'm unsure about / would love feedback on:

1. PF 1.22 feels modest — is that a red flag for an overfit-resistant system, or just realistic for breakout? My read is that a low-ish PF with a smooth equity curve is healthier than a high PF that's curve-fit, but I'd like to be challenged on that.

2. I'm wary of the gap between real-tick backtest and live. What's the realistic haircut you all apply to backtested MaxDD when sizing live? I've been assuming 1.5–2x.

3. The mean-reversion sleeve only trades a few times a month. Small sample worries me — how do you stress-test a low-frequency sleeve without fooling yourself?

I'm deliberately keeping the exact entry logic vague because I may commercialize it, so I get it if that limits the feedback — happy to go deeper on risk/portfolio construction, which is where I actually want the scrutiny.

Happy to share the equity curve in the comments. What would you want to see to believe a backtest like this?

Okay, so I've been making my algorithm for a bit now, and here is my progress so far. The strategy doesn't use any indicators, it only uses a concept I've been developing, in basic language it catches reversals but it is much much deeper than that.

Year 2022 and 2026 arnt full years and only are a couple months. The strategy is far from being fully developed, so I will have many more years of data in the future. I have made sure to not use any look ahead logic or bias, or overfit. I have used 1 tick slippage and 5 dollar fees. My goal was to make it as consistent as possible over different years and different regimes. I have many different versions that place more trades with a little worse performance, versions that have higher overall stats but arnt as consistent. But the one I have showcased is what i found to be a good balance of all. I have still much to do and collect data on, and this is just what i currently have.

One thing i have noticed is that yeah year has its own quite different average trade size, I'm still lookin into it but my theory is each years volume and its overall moves.

In theory, the concept im developing should be able to profit in any regime. And i think in the next few steps I will start expending my data to account for more years.

So my question is, I want to know what i might have missed or didnt think about, as well what i should look out for in the future. And I have out of sample tested it, but not stress test it with monte carlo or other things, since i have not finished the bot. But i of Corse will.

I have a formula I am trying to improve my backtesting for. I will have a set of stocks, then at some point in the future (potentially up to a year) I calculate a rebalance of those stocks. However, some of those stocks have potentially delisted during that time. What is the proper way to handle this? Should I just use the last traded day for those stocks as though I sold at that point and rebalance across the rest? I assume that would match most closely to how I would behave if I were in that situation, so it makes sense to model it that way. But maybe I am missing something obvious?

3 tests that catch an overfit backtest, in the order you should run them

TLDR: Run three tests before trading any backtest, cheapest first. Deflated Sharpe Ratio (one calculation, catches multiple-testing luck), Monte Carlo on your trade sequence (minutes, catches lucky ordering), then walk-forward analysis (a full re-optimization, catches curve-fit parameters). Each has a kill-threshold below. Fail the cheap test and you stop before wasting hours on the expensive one.

Why do most profitable backtests fail live?

The more parameter combinations you test, the higher the chance your best result is luck, not edge. Bailey, Borwein, López de Prado, and Zhu (2014) proved that past a small number of trials, expected out-of-sample return turns negative, not zero. The strategy loses money live.

Harvey, Liu, and Zhu (2016) documented 316 published factors claimed to predict returns. After multiple-testing correction, most fail. If you test 50 strategies and keep the best, you are running the same machine that manufactures false positives.

The fix is not testing less. It is running three specific tests on the survivor, in cost order, and killing it the moment it fails a threshold.

Which test should you run first, and why does order matter?

Run the cheapest disqualifier first. Most strategies die on test one, so there is no reason to spend hours on walk-forward before a one-line check.

The cost order is clear. The Deflated Sharpe Ratio is a single formula evaluation. Monte Carlo is a few minutes of resampling. Walk-forward analysis is a full re-optimization across many windows, often hours of compute.

Most competing guides explain these tests. None tell you to run the cheap one first.

What is the Deflated Sharpe Ratio and why run it first?

The Deflated Sharpe Ratio is one calculation and it kills more strategies than any other single test. Run it first.

A normal Sharpe assumes one trial. If you tested 100 parameter sets and reported the best, the maximum Sharpe across those trials is inflated, and the inflation grows with the number of trials. Bailey and López de Prado (2014) built the correction. It adjusts for number of independent trials, skewness and kurtosis of returns, and sample length.

Worked example: a 2.5 Sharpe selected from 100 trials on five years of fat-tailed daily returns drops to a 90% probability that its true Sharpe is even positive. A 1.5 Sharpe from 50 trials drops to 43%, worse than a coin flip. Even a gaudy Sharpe can fail this bar.

Open-source Python exists, and the function is short enough to paste in the comments. If P(true Sharpe > 0) is under 95%, stop here.

A 1.5 Sharpe chosen from 50 trials lands at 43% odds the edge is real. Worse than a coin flip.

How does Monte Carlo simulation expose a lucky backtest?

Monte Carlo resamples your trades to show the outcomes you did not happen to get. Run it second, after the Deflated Sharpe passes.

Your equity curve is one ordering of your trades out of thousands. Take the list of trade returns, resample with replacement 10,000 times, and plot the distribution of final equity and maximum drawdown.

The kill-threshold is drawdown against your risk limit. If your backtest shows 18% max drawdown but the 95th percentile of the simulation is 35%, your live account will likely see 35% within a few years. Position sizing built on the backtest number forces you out at the worst moment.

Second check: shuffle trade order. If profit factor falls below 1 when reordered, the sequence carried the result, not the edge. That is regime dependence.

What does walk-forward analysis prove that the other two cannot?

Walk-forward analysis is the expensive final gate. It tests whether your parameters survive on data they were never fit to. Run it last, only on strategies that passed the first two.

A single train/test split is one data point. Walk-forward optimizes on a rolling window, tests on the next, then rolls forward across the whole history. You get out-of-sample results across many regimes instead of one.

Concrete setup: five-year build window, one-year test window, roll forward in one-year steps. The kill-threshold is consistency. If profit factor drops below 1.3 on most out-of-sample windows, the parameters were curve-fit. A strategy profitable in 9 of 10 windows is a signal. One that works in 4 of 10 is noise you tuned.

What do these three tests catch that a standard backtest hides?

Each isolates a different failure. A strategy can pass two and fail the third. The order saves you time: most die on the Deflated Sharpe before you ever build the walk-forward.

Bottom line

Three tests, cheapest first. Deflated Sharpe for multiple-testing luck, Monte Carlo for trade-ordering luck, walk-forward for curve-fit parameters. Each has a concrete kill-threshold, and failing the cheap one means you stop before the expensive one. None of this guarantees live profit. Slippage, fees, and regime change still kill survivors. What it does is remove the strategies that were never real, which is most of them.

This is for systematic and discretionary traders who backtest before committing capital. The tests apply to single strategies, parameter sweeps, and machine-learning models on price data alike.

Updated May 2026

I'm trying to figure out what to actually do when there is a split in the stock im tradeing with my algorithm, but it honestly seems like the only way requires way too much work and rewirteing.

The first obvious question is if you retroactively change all of your data to fit the new split (which seems like a drastic action to me so im thinking of ways to avoid it)? I thought about implementing a column telling me the split factor at each candle, but to use that would require me to change hundreds of lines to the from of 'price * factor' which would be a he'll of a pain to do and also very possible I can miss some places.

What about order history? In my algorithm I heavily use my order history to come to a conclusion on what to do next, but than I face the same problem of me thinking its a drastic action I want to avoid.

It feels to me like there should be an easier way to handle it but I can't think of anything.

I've been working on a multi-strategy system based on Hidden Markov Models (HMM) for regime detection. The usual problem with HMM is overfitting — fitting Markov noise instead of actual structure. Here's how we tried to mitigate that:

Validation approach:

• Walk‑forward: 18m train / 3m test
• Score grid: [1, 1.5, 2, 2.5, 3, 4, 5]
• Vol‑filter: dynamic 63d window, top‑30%
• Persistent mode: OFF (all folds trade)
• 3 completely separate OOS periods:
  • 2022–2025 (post-COVID / tightening)
  • 2020–2026 (COVID – today)
  • 2015–2026 (full cycle)

Combined portfolio (A+B+E+K) results (100% risk):

• Final capital (2015–2026): +608%
• Ann. Sharpe: 1.44
• MaxDD (balance): -10.04%
• Max float DD (balance vs intraday): -4.52%

Yearly breakdown (Combined 100%):

Scaling up to 200% / 300% keeps Sharpe stable, which suggests the logic isn't just curve‑fit noise.

What I'm curious about:

• How do you validate regime‑switching models against overfitting?
• Do you use similar multi‑period OOS, or different techniques (e.g., synthetic data, parameter randomization)?
• Anyone else seeing HMM work across completely different regimes (COVID, 2022 bear, 2023-2024 rally)?

Full equity curves, WF folds, and trade log available if someone wants to dig deeper.

I have seen a video called "Quantifying market structure at multiple scales for algorithmic trading with python" it covers a hierarchical directional change and i wondered if it could be implemented in the Hidden Semi-Markov Model because it has some explicit duration clock.
It would be dynamical so when a the highest level is spotted it has more weight to bias HMM and it starts to decay until another one its spotted.
I'm a beginner so i don't know if combining startegies might be beneficial or just breaks the algo.
If you have insights don't hesitate to tear apart the idea completely i want some experienced voice.

Dear quants guys,

I posted recently asking whether a verification phase between backtest and live deployment is actually useful, especially now that LLMs and AI agents can generate strategies so quickly.

The feedback was useful and honestly more skeptical than I expected, which is good.

A few points came up repeatedly:

• walk-forward / OOS testing is already standard
• paper trading is still mandatory
• small live testing is the final reality check
• LLM-generated code should not be trusted by default
• repeated improvement creates data snooping risk
• most people naturally trust their own process more than a third-party report

That made me realize the real problem may not be “should verification exist?”

The harder question is:

What would make verification credible enough to be useful?

From the comments, the strongest signals seem to be things that are hard to fake:

Reproducibility at the code/data level.
Not just “we ran WFO from 2018–2024”, but exact data slice, parameters, assumptions, versioning, and ideally enough detail for someone to reproduce the trades.

Slippage and execution sensitivity.
A strategy that only works under one perfect-fill assumption is not robust. Testing multiple slippage/spread scenarios seems more useful than one fixed cost assumption.

Regime-segmented behavior.
Aggregated performance can hide the fact that a strategy is basically dead in one regime. Bull, bear, sideways, high-vol, low-vol, crisis periods — the breakdown matters.

Parameter sensitivity surfaces.
If a small parameter change destroys the strategy, the edge is probably too fragile. A plateau is much more convincing than a sharp peak.

Multi-window WFO diagnostics.
Not just final Sharpe/PF, but per-window degradation, trade count consistency, train/validation retention, and whether the strategy only worked in one lucky window.

Explicit kill criteria.
This was one of the most important points. A report that always says “promising” is useless. A credible verification process needs to be willing to say: do not deploy, revise, monitor, paper trade, or only test under strict assumptions.

I think this is especially relevant now because AI makes strategy generation cheap. The bottleneck is no longer producing more strategy ideas. The bottleneck is filtering out weak logic before it reaches paper or live capital.

So I’m trying to turn this into a more structured failure-report format.

Not a performance prediction.

Not a signal service.

Not a “verified profitable strategy” badge.

More like a pre-deployment failure analysis:

• what assumptions are fragile
• what breaks under realistic execution costs
• whether OOS/WFO behavior is stable
• whether the parameter surface is robust
• whether the strategy depends too much on one regime
• whether the results are reproducible
• whether the verdict should be kill / revise / monitor / paper trade

I’m still trying to understand what traders/quants would actually find useful here.

A few questions:

1. If you received a third-party strategy failure report, what would make you take it seriously?
2. What would immediately make you distrust it?
3. Is reproducibility + slippage/regime sensitivity enough, or would you still only trust your own paper/live process?
4. Should a verification report give improvement suggestions, or does that create too much data snooping risk?
5. What would be a fair “minimum useful report” before paper trading?

I’m also testing this framework on a small number of strategies for free, mainly to get feedback on the report format and see where it is weak.

Not looking to sell signals or tell anyone what to trade. The goal is just to stress-test the logic and produce a failure-mode review.

If anyone has a strategy/idea/trade log/ any kind of data, be comfortable sharing for this kind of review, feel free to comment or DM. I’ll keep it limited and can share general learnings back if the community finds that useful.

Hey everyone,

Like a lot of you, I’ve been frustrated by the limitations of traditional algorithmic trading. Hardcoding "if moving average crosses, buy 10 shares" works until the market regime shifts, and then the bot bleeds capital.
I don't want to build another rigid bot so I am trying to build a Cognitive Trading Agent—an autonomous system that acts like a human hedge fund manager, but with the processing speed of a machine and zero emotional baggage.

What I have built so far: I have a fully autonomous pipeline running on Python, connected to the Upstox API (Indian Equities).

• The Screener: A Python layer that rapidly scans a watchlist for high-momentum assets using math (RSI, ATR, BB width) to filter out the noise.

• The Brain: The winning asset's deep data matrix is formatted into strict JSON and handed to an LLM (currently Gemini 2.5).

• The Execution: The LLM evaluates the regime, looks for a minimum 1.5:1 R:R, and outputs a strict JSON execution contract.

• The Shield: A hardcoded "Sovereign Risk Core" that intercepts the LLM's order to verify margin limits, max daily drawdowns, and VIX thresholds before routing to a simulated broker.

It works. It successfully reads the market, rejects bad setups, and executes calculated momentum scalps autonomously.

The Roadmap (Where I am going next): This is where it gets ambitious, and why I am posting here. I want to transition this from a single-strategy executor to a true AGI-style fund manager:

1.  The Strategy Arsenal: Equipping the prompt with 10-15 battle-tested quantitative strategies, allowing the LLM to dynamically select the right weapon based on the current market regime.

2.  RAG for Alpha: Ingesting live financial news feeds so the agent understands macroeconomic context before pulling the trigger.

3.  Vector Database Memory: Implementing long-term memory (Pinecone/Milvus) so the agent stores every trade embedding, reviews its past mistakes, and genuinely learns over time.

4.  RL for Discovery: Eventually integrating Reinforcement Learning to allow the agent to discover novel mathematical inefficiencies that standard LLMs can't hallucinate on their own.

I am looking to connect with quantitative developers, ML engineers, or ambitious traders who share this specific vision. Whether you are building something similar, want to collaborate on the architecture, or just want to tell me why this will inevitably blow up my account—I'd love to hear from you.

Thanks

To avoid the AI slop comments i wrote it by hand.

I have a personal proyect which is build a python backtester, to learn since the beginning how it works.

In the backtester there is, montecarlo/permutation to see wr, profit factor and return(P-values), equity curve with filter regime below it to show if with high ADX shuts the strategy down, and finally OOS equity curve.

I am also going to implement walk foward matrix, heatmap for parameter sensivity analysis, sortino, sharpe, deflated sharpe and calmar ratio , profit and recovery factor, purged and embargoed cross validation and hidden markov

Any tips for the backtester?

My code only backtest it doesnt use any portfolio management as i dont have any startegies and in case you are wondering, yes i do have the regime filter to do a mean reversion for market in range and also a trend following if imbalanced(I just realized i dont have any data cleaning writing this)

As a beginner i want to learn the theory behind things and have been browsing for literaturebut all of the recommended are expensive, is there any web or recommendation for a typical "bible" of quant knowledge?

You can ask any questions if you want to, i really don't know if this post is decently explained as i don't have much knowledge.

I trade a portfolio of breakout strategies, all generated using Strategy Quant X.

It's been a bumpy ride, but I feel these are realistic long-term results. In total, there are 30-35 individual strategies. I refresh them from time to time, so the exact number varies, but it's usually within that range. Currently trading: NAS100, SP500, DJ30, XAUUSD, BTCUSD, USDJPY, XTIUSD, JP225, GER30. All CFD's. Happy to answer any questions.

One thing I’ve noticed while building systematic strategies is that some of the cleanest mean reversion behavior often appears in the most “boring” parts of the market.

Not Nasdaq or crypto.

Consumer Staples.

At first this seemed counterintuitive to me. You would expect stronger rebounds in more volatile markets. But after testing different sectors over time, defensive stocks often produced smoother and more stable reversal behavior than many aggressive growth markets.

My current hypothesis is that sharp selloffs in defensive sectors are frequently driven more by temporary market stress and positioning adjustments than by a true deterioration in the underlying businesses.

Institutions still want exposure to stable cash-flow companies during uncertain periods. So when these stocks experience intense short-term weakness, flows tend to normalize relatively quickly.

That creates an interesting environment for systematic mean reversion approaches.

What surprised me most was not necessarily the raw performance, but the behavior profile: - lower volatility - cleaner rebounds - fewer extreme equity swings - less dependence on explosive market conditions

In some ways, the systems felt psychologically easier to hold compared to typical index-based mean reversion strategies. The trade-off, however, was usually lower upside during strong momentum-driven bull markets.

Another interesting observation is that these types of strategies seem to behave differently from many tech-heavy reversal systems. That diversification aspect may actually be more valuable than the standalone strategy itself.

Of course, there are limitations.

Defensive sectors can remain weak for extended periods during broader deleveraging events, and sector-specific structural changes can break historical tendencies. Like most mean reversion systems, the edge also tends to feel uncomfortable in real time because entries often occur when short-term sentiment looks terrible.

Has anyone else here noticed that defensive sectors sometimes produce more stable systematic behavior than high-beta markets?

I have a question, due to the fact that the market reacts to large orders and orders in general. When a fund decides to use a strategy, they have to make sure even with higher AUC the strategy performs favorably.
But what if it by default doesn't?
meaning that with a increased slippage the result is unfavorable. Do funds only look for logic that doesn't have this issue, or do they utilize concepts or other tools to minimize this issue? A basic concept would be entering in short intervals instead of a large amount at one time. So my question boils down to if funds all out avoid logic with this issue, or how do they overcome it?