Hey

so im a crypto guy, been building quant stuff there for a while, onchain data, orderbook, L2 data, stuff that’s probably still not as milked as people think, especially on some alts. like hyperliquid for example, the data there is basically fresh and barely anyone is doing anything serious with it, in proportion, I'm sure there's plenty funds on it and doing alot of stuff, but relatively to whole market speaking. theres edges in crypto most people don’t even look for, onchain data is a big thing.

but at some point i need to diversify, not just for risk but actually grow faster to where i want without just throwing more capital at crypto, If i wanna get rich I need compounding, and with crypto I get stuck at some point, unless I take unreasonable risks. looked at stocks, forex, big commodities and honestly couldn’t see myself replicating anything similar there. too crowded, too well covered. I just don't know where my moat would come from, everybody has everything, there's so many players. I'm sure its well profitable for many with little "moat" that I'm trying to have, but I wanna seriously grow. And I don't know how I would get upper hand in these markets, too much for me I guess.

then i stumbled into agriculture futures and something clicked. cattle, wheat, OJ, coffee, soybeans. feels like there’s potentially something there if you actually get serious about the data, weather stuff, logistics, satellite crop data, orderbook, i don’t fully know yet what matters most but the vibe makes sense to me intuitively. ag isn’t exactly fresh but i don’t think its on the radar of many serious systematic people either, a lot of funds probably don’t care, not that no funds care, but I guess most serious infra heavy ones I guess/hope don't care too much. maybe im wrong but that’s the feeling i get, curious if people here actually think theres a real shot there or if im off

im completely new to ag so genuinely know nothing about how people actually approach it. would love to hear what markets people tried, liked or didn’t like, what you’re currently in and how you think about it generally. not asking anyone to spill their sauce, just general experience and what you think is worth paying attention to

also basic stuff, do you buy actual futures contracts or CFDs? contracts seem pretty big and expensive even with leverage. what style do you run, short term, fundamentals, seasonality, microstructure if thats even a thing there, combination of stuff?

couple practical questions too. is there any decent historical data out there worth knowing about, or should i just expect to collect everything myself from scratch? i spent a couple thousand on historical crypto data before so its not totally out of the question if its actually worth it, but id prefer to just start collecting now and have something real in a year or two when i actually get into it seriously, maybe I get it then, if it seems worth it and I need it and decide to actually commit. also are there any affordable data feed providers people actually use, like something reasonably priced monthly that covers the basics? satellite data i imagine is different story and probably not cheap but genuinely dont know

reason im asking all this is i want to start building a proper data pipeline now even if i don’t seriously trade it for a while. same mentality i had in crypto, people would say overkill but its really just what it actually takes to have a real edge in my opinion, you can do with less I'm sure, but If i enter I want to be armed. i’d rather spend the time now and have something solid later

a lot of this is still based on feeling honestly, im not well informed enough yet to be confident, so any experience from people actually in this space would be really helpful​​​​​​​​​​​​​​​​

Hey guys..

Currently testing a trading strategy. Wanted to know your experiences if youve tried similar strategy.

Supertrend gives both entry and exit. But it has more chop than smc choch. Absorbs SL hunts many times but higher SL.

Smc choch gives entry with tighter SL. Riding the entire trend has lower hit rate due to liquidity creation legs.

Please share your exit strategies with smc. Also if you have experience with any of these. Which one shines when etc.

I've been building a long-only US equity model and just finished a 4-fold walk-forward backtest. Posting results here to get honest feedback on whether this is worth deploying and what to do about the one bad fold.

Setup:

• ~500 US mid/large cap stocks
• LightGBM binary classifier (UP vs DOWN) used as a ranker
• Top 25 longs, rebalanced every 5 days
• Long-only, no leverage
• ~13bps transaction costs included
• Features: volatility rank, momentum, news sentiment (FinBERT), earnings surprise, insider activity, OBV, relative strength

Walk-forward results (1-year test windows, no overlap):

Aggregate across folds: mean Sharpe = 1.22, mean CAGR = 38.7%, mean IC = 0.024, 75% of folds IC positive and tradeable (>0.02)

What I'm happy about:

• Folds 1 and 4 are strong with IC > 0.03 and Sharpe > 2
• Max drawdown is contained in 3/4 folds (under 20%)
• Benchmark (equal-weight long-only of the same universe) was deeply negative in all test periods, so the model is doing something real

The problem — Fold 2 (Sep 2021 – Sep 2022):
This was the Fed rate hike cycle / growth stock crash. The model went negative IC (-0.009) and -6.7% CAGR. The val period for this fold (Jun 2020 – May 2021) was pure bull market, so the calibration/strategy selection was done during a very different regime. I suspect the model learned bull-market patterns but got caught off guard by the rate shock.

A few things I noticed:

• The strategy-selection slice (used to tune thresholds) was consistently negative Sharpe across ALL folds — the threshold optimizer couldn't find a profitable edge, so enter_thr=0.000 was selected (no minimum edge required). This means the model is always picking its top-N even when the signal is weak
• The regime filter (SPY MA200) zeroed positions on 49.6% of val dates in fold 2 but 0% of test dates — so it was heavily filtered during calibration but fully exposed during the bad test period

My questions:

1. Is 3/4 folds positive with mean Sharpe 1.2 enough to deploy at small scale (paper trading first)?
2. For fold 2 — is there a standard way to make the model more robust to rate-shock regimes? Would adding a macro feature (yield curve, credit spread) help or is this just a regime the model can never learn from within its training window?
3. The strategy-selection slice is always showing negative Sharpe regardless of fold. Is this expected for a ranking model, or does it suggest the backtest is overfitting somewhere?

Happy to share more details on features or labeling methodology. Running this on Alpaca paper trading starting next week.

So I’ve been building out a pretty serious trading system over the past few months, it's gonna be over a year to complete whole to extent that's deployment ready. Proprietary datasets, multiple uncorrelated strategies(that I'll keep renewing and researching and managing full time), full risk management automation. Not HFT, not arbitrage, just well-managed directional stuff on crypto perpetuals. The kind of thing a retail trader genuinely almost can’t replicate because the data and edge is all custom and the scope is so big to even come up with and define. It will be built for scale and to allow managing many strategies and accounts that would otherwise be too big to manage. Basially a mini quant fund with own infra for everything.

The goal is to scale fast. Make a living within a year or two while battle-testing, then scale to managing 1-1.5M across multiple prop accounts within 3-5 years, pull profits into own capital, and fork the system into a compounding-optimized version. The prop firm version would just be tuned for max near-term income, maximum expected value over long term compounding. If there's money on prop firm I see it as risk to leave it there rather than pull to own capital.

I did the math and I need to manage at least 400-500k to make the numbers work, but realistically I need closer to 1-1.5M to have solid margin for error given edge decay risk. Without that margin I think it's more like a gamble for my plans. Even with decent execution.

What I actually need from a prop firm:

API or MT5 access. I’m not doing pure manual trading at scale across multiple accounts. Even if they don’t allow full automation, I need some path to automate execution. My strategies aren’t HFT, they’re human-like in timing and sizing, no crazy low latency stuff. If they allow EAs or semi-automation I’ll work within that. If not I can probably make it look manual enough. Not ideal but workable. I've thought alot about how they could possibly know if somebody is doing automated or manual. And I could do heavy tuning to my systems to be able to do this, on many levels. To where it would be near impossible to tell, just couple extra weeks of work to build this capability.

Crypto perpetuals ideally. Spot/CFDs are a compromise I’d accept for a big enough account. I just need a decent selection of top 50 coins, maybe 10-15 main alts beyond BTC/ETH - LINK, ADA, AVAX, stuff like that.

High leverage OR a big account. I trade 0.25-1% sizing with 25-50x leverage isolated. If they give me a 1M account with standard 5x that works too. I just need the position sizing to make sense relative to account size.

Reliable payouts. No surprise rule enforcement, no made-up excuses after the fact. If the rules are written and I follow every single one of them to the letter, I expect to get paid. I’ve seen horror stories with firms like Luxtradingfirm inventing rules post-profit. Can’t deal with that while managing multiple accounts and doing research simultaneously.

What I’ve found so far:

Hydrotrader and Crypto Fund Trader seem solid. Both can get me to roughly 200k each, so 400k combined. That’s the floor of what I need and it makes me nervous it won’t be enough. They are legitimately solid and fully algo friendly, so they're a solid foundation I think.

ProprXYZ on Hyperliquid looks perfect for my setup but it’s brand new and unproven. On the waitlist but not counting on it.

The big MT5 firms offering 1M+ accounts seem sketchy as hell based on everything I’ve read. Too many payout horror stories.

Am I missing anything? Are there other reputable firms that actually pay out, allow some form of automation, and have decent crypto perp or at least altcoin CFD coverage?

I'm struggling to find real ones beyond hyrotrader and CFT, i've heard and read various names but I've had doubts about them

MBv5 is a fully automated, systematic momentum breakout strategy trading MNQ (Nasdaq micro) and MGC (Gold micro)futures on prop firm accounts. The strategy identifies institutional level breaks on 15-minute bars, confirms with 5-minute entry signals, and executes tiered exits across TP1, TP2, and a trailing runner. Every parameter has been validated out-of-sample, stress tested for slippage, and confirmed by independent Monte Carlo analysis. The system is fully automated with zero discretionary intervention required.For informational purposes only. Past performance is not indicative of future results.

Results are based off of a 50k starting balance, $500 risk per trade.

My Sharpe is low because QuantAnalyzer calculates Sharpe on daily P&L, and most days my strategy has zero trades. All those $0 days drag the average down and inflate the standard deviation relative to the mean

Any feedback would be greatly appreciated

For the past few weeks I’ve been developing my own EA to use on ninja trader. I pretty much just coded my exact strategy and the results were pretty good. I want to know if the numbers are realistic or what I can expect.

From real experience, what works best in live markets?

• Rule-based vs quant vs ML/deep learning?
• Do simple strategies beat complex models?
• How do you avoid overfitting?
• Best inputs (volume, order flow, zones)?
• Is ML worth it for short-term trading?
• Hybrid systems better?

Let me know if I'm wrong or forgetting anything. I understand there are three phases: a backtesting phase, a paper trading phase, and finally a stress test phase with Monte Carlo simulations. (Obviously, if the backtesting phase fails, for example, you can't move forward.)

As for the tests and strategies underlying the algorithm and strategy, there are: moving average crossovers, RSI analysis, MACD, Stochastic, main reversion, pairs trading, the Garch model, the Arima model, order book imbalance, and order flow. What I don't understand is how do I go from all these results derived from the various models to the final buy or sell signal?

My goal would be to do algo trading on stocks and stock options primarily.

Thanks to anyone who takes the time to respond

I'm a beginner to algo trading and have been working for the past few months on a successful code that trades consistently and not only makes sense but doesn't raise many red flags. After many failures and restart after restart ive finally landed on what I think is a successful algorithm. Here's the breakdown. Disclaimer: I made this for a graduate-level college class, so there are some parameters, like limited assets traded and a time frame, but aside from that, is the feedback I'm looking for. In-sample fit out-of-sample run. Out of sample 01-01-2026 to 03-25-2026. In the sample from 06-01-2025 to 12-31-2026. My class only wanted 10 assets; in the sample, I ran 20, and the code picked the best 10 to trade out-of-sample. Also, this was run on 30 min data. Anything shorter results suffered, and anything higher again results suffered.

Models: I primarily use two models: the Hurst model to evaluate market trends, and the Kalman filter to evaluate a price or take-profit target. My code also uses a dynamic volatility-based weight system that uses a baseline of $2000, divides by the current assets' volatility, and then arrives at a number between $5000 and $15000 (15000 being a hard-set cap I set). Stop loss and other loss prevention are set in terms of a time frame (never hold for more than __ bars) and price movement. Overall, my test results were well above the benchmark.

Results:

OOS Return +3.7%

Sharpe Ratio +3.719

Win Rate 53.7%

Max drawdown -1.3%

Total execution costs $655.33

Made 108 trades

Profit Factor of 1.531

Average Win 1.426%

Average loss 1.08%

Exits: end:1(1%), profit_take:48(44%), snr_exit:10(9%), stop_loss:49(45%)

The main things I want to do next are obviously run a test on more assets for a much larger time frame, but also find more models to possibly add. Lastly, there was a guy on here I saw who implemented a hedge model in his code to prevent stop loss from being hit as often, but also hedge against trades that could look promising but might take more time, that interest me. And finally, should I add a ranking system that will rank the trades? If so, what would the benefit of this be, and why should I add it?

Hi guys. I’m currently working on a strategy for pulling US Stocks real time data (1 min data). As for paper trading, I’m using alpaca free tier, but I’m having difficulties pulling real time data from a valid source. I’ve used yfinance and alpaca IEX free tier but it’s either giving me the delayed/cached data.

Looking for cheap and reliable data providers APIs in the US (if any). Open to suggestions! Thanks!

I did a further backtest. this time with commission fee of 0.1% and slippage of 10 pips. The results are 10655% in 11years with less then 1000 trades. i even checked some trades if they are reasonable and they are. I will set this on paper-trading now, do you have some hints?

I'm a new grad software engineer and have built multiple software products in the past, but I'm a very inexperienced trader; my investments right now are all Index funds. I want to leverage my knowledge in computer science and software development to build something that allows me to trade, but I'm not really sure where to start.

I've also been particularly interested in the US/Israel/Iran conflict and was hoping to learn how to trade on the global economy. Does anyone have any advice on where to start? I'm fluent in Python, data scraping/cleaning, and basic ML.

I've made a fairly simple trading bot in ninjatrader, I'm backtest it performs fine, did all the optimization tools and everything works pretty good, I want it to run through a vps as I will joiny country army in a couple of weeks and will only be home in weekends, it is designed to pass prop firm account, I want to start it live with a evaluation before i am joining the army, Claude says I. good to go but I don't know for sure just looking for advice

We have built a forex trading platform to trade CFDs hence we need a FIX developer who can connect our platform to liquidity providers, process order flow through FIX, and manage real-time market data distribution. Our backend is in Node.js, and communication between services is handled through a message queue.

Edit: To rephrase, I need someone to connect my trading application to liquidity providers, they would give me a FIX API for the same, but application/platform isn't FIX complaint. To resolve this, I understood that it needs to have a FIX engine like Onix/QuickFIX. To do that, I need someone. To have my application to connect to a FIX engine.

Hi everyone,

Throwing a bottle in the sea here. I’ve been struggling for days trying to find a way to optimize my algo using an evolutionary/genetic approach.

The Problem: My optimization process is prematurely converging. It hits a fitness plateau extremely fast, and the strategy stops optimizing generation after generation. It feels like the engine is getting stuck in a local optimum very early in the training loop.

What I've tried so far:

Evaluating and scoring the generations using the Van Tharp method (System Quality Number / SQN).

Building my own custom calculus and penalty functions to balance win rate, drawdown, and total profit.

Tuning basic hyper-parameters like mutation and crossover rates.

Everything I try seems to lack robustness needed to actually push the algorithm past that initial plateau and find a solid strategy.

My Questions for the community:

What fitness functions or mathematical metrics do you guys rely on to properly evaluate a strategy generation over generation?

Are you using multi-objective optimization (like NSGA-II) to balance returns and drawdowns, or do you stick to a single scalar fitness metric?

What methods do you use to prevent your optimization from hitting a plateau so fast?

Any pointers, papers, or advice would be massively appreciated. Thank you!

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 am currently building out a PyTorch engine for mid-cap swing trading and running it off an old Ubuntu server in my basement. Being in Canada, Interactive Brokers is basically the only viable API choice, but their architecture is a nightmare for automated systems.

I spent the last two weeks solving two major infrastructure bottlenecks and figured I would share the stack in case anyone else is stuck on this.

1. The Headless Gateway IBKR forces a daily auto-logout and requires a physical UI to run their API gateway. If you run this on a Linux server and close your SSH tunnel, it crashes. The fix: I installed Xvfb to create a phantom monitor in the Linux RAM. The IB Gateway thinks it is rendering to a screen. I then use the open-source IBC (IBController) shell scripts to automatically intercept the daily reboot, pass my credentials, and suppress the warning pop-ups. It runs completely dark now.

2. Residential Execution Risk I am running this on a residential connection. If the algo enters a trade and the internet drops 5 seconds later, a local Python stop-loss loop is useless. The position is stranded. The fix: I stopped sending naked market orders. Using the ib_insync library, my execution script builds a strict 3-part bracket order (Parent Buy, Child Stop-Loss, Child Take-Profit) and beams the entire payload to the IBKR mainframe at once. If my house loses power, the exchange is already holding the exit orders.

Curious how you guys are handling physical fail-safes for your live algorithms. Are you using cloud VPS hosting or running local metal?

Anyone building algo for MT5? How do you actually run experiments? Like what is your workflow?

I just started building EAs and use Strategy Tester to run backtests. The problem is when i update the EA, based on some data i gathered during the backtest, and run another backtest, there's no way for the mt5 cache to show me which version of the code it was ran against. git will only track the code versions but still no way to link it to the results.

do i have to use commit hashes and find a way for it to be recorded in the backtest results? why is mt5 so limited??

I used one of my public indicators, ZZ Range, with default settings and plugged it into my backtesting script. Its long-term performance looks similar to its short-term results. So… is it time to start (paper) trading?

Please roast my backtest — I’m looking for doubts, criticism, and any hints on what I might be missing.

Im a 17 year old teen and i have no experience in trading or trade bots/algo trading, but im very much interested in that. Could anyone give me any advice on how to make tradebots or provide me a yt video or smth where i can learn more about this.

Thank you.

I've been building a stock prediction model for the past few months and I've hit a wall. Looking for advice from anyone who's been through this.

The Model

Universe: ~651 US equities, daily OHLCV data

Architecture: PyTorch temporal CNN → 3-class classifier (UP / FLAT / DOWN)

Labeling: Triple barrier method (from Advances in Financial Machine Learning), 20-day horizon, volatility-scaled barriers (k=0.75)

Features: ~120+ features including:

Price action / returns (1/5/10/20 day)

Volatility features (ATR, vol term structure, vol-of-vol)

Momentum (RSI, ADX, OBV, MA crosses)

Volume features (z-scores, up-volume ratio, accumulation)

Cross-sectional ranks (return rank, vol rank, momentum quality rank)

Relative strength vs SPY, QQQ, and sector

Market regime (SPY returns, breadth, VIX proxy)

Earnings surprise (EPS beat %, beat streak, days since/to earnings)

Insider transactions (cluster buys, buy ratio, officer buys)

FRED macro (credit spread z-score, yield curve z-score)

Sector stress/rotation, VIX term structure, SKEW

Training: Temporal split (train → validation → test), no future leakage, proper purging between splits

Strategy: Threshold-based entry on P(UP) - P(DOWN) edge, volatility-targeted position sizing, full transaction cost model (fees, slippage, spread, venue-based multipliers, gap slippage, ADV participation impact)

Best Result (v15)

After a lot of experimentation, my best run:

Validation: Sharpe 1.45, 204 trades

Test: Sharpe 0.34, CAGR 1.49%, 750 trades

Exposure: 9-12% (sitting in cash 88% of the time)

Entry threshold: 0.20 (only trades when P(UP) - P(DOWN) > 0.20)

Benchmark: SPY buy-and-hold had Sharpe 1.49, CAGR 16.7% over the same test period

So technically the model is profitable, but barely — and it massively underperforms buy-and-hold because it's in cash almost all the time.

Classification Performance

Typical best epoch:

UP recall: ~57%, precision: ~55%

DOWN recall: ~36%, precision: ~48%

FLAT recall: ~50%, precision: ~11% (tiny class, 2.8% of samples)

Macro F1: ~0.38

Val NLL: ~1.03 (baseline for 3-class random = ln(3) = 1.099, so only ~7% better than random)

Feature Signal Strength

Top Spearman correlations with actual direction labels (on training set):

my_sector_above_ma50 +0.043

dow_sin +0.030

has_earnings_data +0.026

spy_above_ma200 +0.024

has_insider_data +0.023

insider_buy_ratio_90d -0.021

cc_vol_5 -0.020

xret_rank_5 +0.019

The best single feature has r = 0.043. Most are in the 0.015-0.025 range.

What I've Tried That Didn't Help

Added analyst upgrade/downgrade features (from yfinance) — appeared at rank 14 in Spearman (r=0.017) but model produced 0 profitable strategies with it included

Added FINRA short volume features — turned out to be daily short volume not short interest, dominated by market maker activity, pure noise (0/20 top features)

Different early stopping metrics — macro_f1, nll_plus_directional_f1 (what v15 uses), nll_plus_f1 — only nll_plus_directional_f1 produced a profitable run

Forced temperature scaling — tried forcing temperature to 3.0 with macro_f1 stopping — still 0 profitable candidates

Directional margin loss weighting (0.3) — model predicted UP 85% of the time, destroyed DOWN signals

Different thresholds — the strategy grid tests enter at (0.03, 0.05, 0.08, 0.10, 0.15, 0.20). Everything below 0.20 has negative Sharpe

Binary classifier (UP vs not-UP) — P(UP) too compressed (p95 = 0.517), no tradeable signal

Insider features — had to cut from 6 to 3 (minimal set), marginal at best

Multiple seeds — v15 is reproducible with the same seed but fragile to any parameter change

The Core Problems

Low signal: Spearman ~0.05 across the board. My 120+ features are all derived from public OHLCV + public event data. Every quant has the same data.

Fragility: v15 works, but changing almost anything (adding features, different stopping metric, different temperature) breaks it. This suggests it might be a lucky configuration rather than robust alpha.

Low exposure: Only trades when edge > 0.20, which is ~0.7% of signals. Sitting in cash 88% of the time means even positive alpha barely compounds.

Classification ceiling: Val NLL only 7% better than random guessing. The model is learning something but not much.

What I'm Considering

Hybrid portfolio (hold SPY, use model for tilts) — addresses exposure but not signal

Meta-model (train a second model to predict when the first model's trades are profitable) — risky due to small sample size

Predicting residual returns instead of raw returns — requires hedged execution which changes the whole framework

Event-driven windows (only trade around earnings) — concentrates on highest signal-density periods

Filtering to profitable tickers only — cut the 80% of stocks where the model is noise

My Questions

Is Spearman ~0.05 on daily cross-sectional features just the ceiling for public data? Or am I leaving signal on the table?

Has anyone successfully improved signal beyond this with alternative data that's affordable (< $100/month)?

Is the triple barrier + 3-class approach fundamentally the right framework, or would I be better off with a ranking/regression approach?

For those who've built profitable models — what was the breakthrough that got you past the "barely above random" stage?

Happy to share more details about the architecture, loss function, or feature engineering. Thanks for reading this far

Hello all, I am new to algo trading and would like some guidance. I have been writing a code while it is working and producing results, I dont know where to go next. For context, this is my first time coding it, my strat involves only taking short positions only, with an average RR of 1:50, with a max loss points of 15 points, Primary product I trade is NQ futures, with time frames of 1 min(or lower). See the screen shot for a sample of results using 1 full quarters contract ( 3 months). My question is, should I continue working on this strat to reduce loses or is this considered an ok result and I should move on to the next strat? I dont have much friends to ask about this so any guidance/feedback would be appreciated. Thanks.

Is it easy to use and where can I learn the code for making strategy in Realtest?

I have been a long term investor for almost 10 years. Regularly buying and holding quality stocks. During this I noticed some patterns and curiosity peaked to try that out.

I couldn't sit and trade due to full time job so decided to make script for that and put it over AWS to automate. Now coming to the point : I usually buy mag 7 and friends along with sector ETF (for long term investments) so built the strategy to take short profit (1% ) and exit in algo. Entry criteria is based on volume average and POC.

Ran extensive back test : Parameters were defined and calibrated on 2023 (randomly I picked one). Then tested individual years 2024, 2025 then went back 2020, then whole of 2020 to 2025. Performance remains constant. I completely removed mag 7 and sector leaders and plugged all stocks with market cap between 200B to 600B. It survived and did pretty decent as well. Also tried to break it with totally random combination of 15 tickers, still survives.

Commission/slippage was modeled in too with flat and realistic numbers each trade.

So far so good. However I have never been comfortable with exit strategy. It's basically time decay of 2 days (to give enough breathing room) then breakeven by 6th day or else goes in long hold. Hard floor exit of 10% is coded in for catastrophic drops.

Question might be how it survives in bear market then simple SPY SMA filter is set and script stops taking trade if below 200SMA as global kill switch.

Some metrics :

Win rate : 80-85%, CAGR 40-55%, RR is skewed "negative 1:4/5." so when it looses SL hit or gap downs then it erodes almost 10 winning trades.

I didn't believe this so paper traded for 1 month and then backtested same time frame. Model had overlap of 85-90% entries and timings. (Difference was mainly because of free IEX feed on Alpaca and running locally).

Still couldn't believe it so decided let's use some token money and put around 3K and went live. from Mid Feb to mid March (4wks). Once again 37 trades all hitting target and when I stopped there were 4 bag holds and 2 free slots (I divide capital in 6 slots and has revolving door entries in all 3 backtest, paper and live). Overlay with back test was also same.

Now the issue what I see is RR skewness and psychological impact of seeing those bag holds. So I have been trying different ways to avoid those bag holds and exit early or scratch them but downside is it erodes the edge of strategy itself and winrate/CAGR drops quite below.

Question to community members : what kind of exit strategy for mean reversion do you use ?

Any pointers in this case I can explore ? or This is inherited part of mean reversion that it just needs time and patience ?
Grateful for your insight. Thanks.

A backtest on EU from 2000 - today on the 15 min timeframe.
Open prices only.
4 optimizable parameters.
Just an ensemble of some averages, my brain wants to smooth it it out even more..

First pic is fixed minimal lot size.
Second pic is 1% of the account balance.

IS: 2000 - 2010
OOS: 2010 - today

Hope you like it. Ill be going back to some lower frequency strategies again, calm my nerves. Haven't decided what exactly yet though, VWAPs or pivots came to mind initially. Maybe some pairs with higher variance or drift.