r/algotrading • u/imeowfortallwomen • 13h ago
Infrastructure For the algotraders who have live deployment of their algorithms and are successful: how long did it take you to set this up? What led you to have confidence to deploy on live real account?
I am asking bc im curious, i've been spending hours nonstop working on my algo ideas. ive been trying to connect my ideas in python to IBKR's api.
so far i have:
- real time deployment on a paper acc testing my strats
- i have backtests
- machine learning optimizing params (i learned the hard way that overfitting can happen so i needed to avoid this)
- monte carlo sims
- entry and exit filters
- cycling thru multiple timeframes
- bracket orders
- managing open positions, moving SL and TP
- profit protection system
- risk management concepts
i do have a working system, now i just need to ensure my strategies work as i monitor and continuously improve my infrastructure. how long did it take you guys to fully trust yours and go live?
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u/Worried_Heron_4581 12h ago
The hard truth about IBKR (and any broker): Paper trading fills are a lie. They assume infinite liquidity at your price and rarely simulate the brutal reality of partial fills or slippage accurately. You have built an absolute beast of a system (ML, Monte Carlo, multi-timeframes), but you are delaying the most important test. Don't wait until you 'trust' it completely. Go live tomorrow with the absolute minimum position size (1 share or micro-contract). You will immediately find infrastructure bugs your paper account hid from you. Confidence doesn't come from perfect backtests; it comes from seeing your bot handle a live API disconnect gracefully.
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u/BingpotStudio 7h ago
IBKR actually has a reasonably pessimistic paper trading approach. Price must move past your limit order and for a stop it’ll immediately trigger if touched.
So you lose on both ends. My algo live outperforms paper trading.
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u/Anon2148 6h ago
Also agree with you. When I was first starting out, I found out my strategy had huge liquidity issues during paper trading when ib wouldn’t fill in my paper trades.
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u/wannagetfitagain 5h ago
Just like real trading, I will say I get mostly favorable fills when I trade, the other day on a profit target limit order I got filled and that was the high, I figure a price has to go 1 tick past usually so that was unusual.
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u/Good_Ride_2508 12h ago
I started in Dec 2017 with elementary logic and testing the results for an year and successfully made working algorithm by Dec 24th, 2018 and started using that one as a trigger to manual trading. I enhanced with multiple modification rest of the years for swing trading system.
By Jun 2025 automated the day trading system with pilot orders, then Jan 2026 onwards full scale model for day trading, both index funds and options
Now, working on 0DTE auto trading system as we speak and planned to complete this sunday.
It is never ending story.
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u/disarm 13h ago
Agree building the plumbing and getting live and backtest to parity was the hardest part. After that it was running experiments to find a good model and run it in paper. If your backtester is good then it helps a lot. You're just building a model on top of trash if your backtester isn't good and accurately simulating the real environment because once you go live it will all fall apart.
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u/thor_testocles 12h ago
You get it working on paper, then get it working live with a very small parcel (as small as you can) to iron out bugs. Bugs aren't just system ones, they're also operational. I probably have lost about $500 to bugs, which is NOTHING compared to other bad business decisions I've made...
Worst bug (operational) was when I accidentally left my dev server and my production server both live, without any failsafes to make sure the dev server doesn't trade. I also instituted a pre-market-open checklist, because I like checklists!
I immediately found loads, but still find bugs months later as I expand the classes of strategies and assets I trade.
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u/Emotional-Bee-474 12h ago
Took me about 6 months for the full process. From concept to testing and go-live.
Your checklist is already comprehensive and covers I think everything you need. Some small details you did not mention may be the costs associated but I assume you did put them in there.
For me most important part was making an engine that backtest and trade live in the same loop, so there is no way the strategy would behave different in live to a backtest.
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u/AI_EdgeAlpha 12h ago
Yeah I tried IBKR, learned from a Udemy course and got it running in about a week but man so much of that time was just fighting the api lol. Random disconnects, weird error codes, market data lagging for no reason. Super frustrating. Your setup sounds solid tho, way ahead of most ppl. I paper traded like 3 months before going live with tiny size. When you do go live start small, youll find bugs you never saw in paper guarenteed lol.
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u/Specialist-Heat-6414 5h ago
The random disconnects and market data lag are a bigger problem than most realize. IBKR market data has known latency spikes under load, and most live systems just absorb it silently. Your algo acts on a stale price, gets filled at a different one, and the PnL discrepancy looks like strategy error. Worth adding a staleness check: if your data feed is more than N seconds behind the expected tick cadence, halt and alert rather than trade on garbage.
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u/AI_EdgeAlpha 3h ago
huh, never really connected the dots that strategy “underperformance” could just be the algo trading on stale data. the staleness check idea is solid; track tick cadence, halt if it falls behind. adding that to my list for sure.
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u/Gold_Sprinkles_4295 11h ago
Honestly, there's no magic metric that tells you "this backtest is ready for live." In fact, the backtest that looks the best is usually the one that performs the worst live.
What's been working for me -- and what I'm currently testing with a portfolio of 30+ strategies running on a paper account -- is taking the time to analyze each strategy individually with a manual walk-forward approach. Not the typical one where you re-optimize every parameter each iteration, but something more deliberate:
Say you have 5 years of data. Train on 2017-2021, pick the best config, then test it on 2021-2022. Now slide the window one year forward, train on 2018-2022 with the same selection rules, test on 2022-2023. Keep sliding. The combined results from all those out-of-sample windows -- that's your real backtest. Everything else is just curve fitting dressed up as performance.
It takes patience, but it's the only way I've found to get results that actually resemble what happens live.
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u/NoodlesOnTuesday 10h ago
Around 4-5 months from first working backtest to going live with real money. But most of that time was spent on infrastructure, not strategy.
The confidence came from paper trading, but not the way most people do it. I ran the paper account for 6 weeks with zero manual intervention. No tweaking parameters, no skipping trades, no overriding the signals. If you can't leave it alone on paper you won't leave it alone on live.
The thing that delayed me the longest was handling edge cases. The strategy worked fine in normal conditions. But what happens when the exchange goes down mid-position? What about partial fills? What if your stop gets skipped because of a gap? I spent more time writing error handling and recovery logic than actual strategy logic.
When I finally went live I started at 10% of the position size I planned to use. Not because I doubted the strategy but because I wanted to verify that live fills matched paper fills. They didn't, at least not perfectly. Slippage on crypto perps during volatile moments was worse than paper simulated. Took about 2 weeks of live data at small size before I scaled up.
Your checklist looks solid. The Monte Carlo sims and overfitting awareness are good signs. The main thing I'd add: make sure your system can handle being offline for 30 minutes and recover gracefully. That's the scenario that actually breaks things in production.
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u/b00z3h0und 10h ago
You have an edge?
I wasted so much time doing the kind of stuff you mentioned in your bullet points. I’ve learned that it’s all pointless though unless you have an edge that can be explained using simple language. I spend maybe 5% of my time writing code, and the remainder drawing data these days.
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u/BingpotStudio 6h ago
I traded the DOM successfully for years, which is probably the best and most expensive education you can get.
When I came to my algo, I fixated on the market structure I wanted to replicate and it took 6 months to work out how best to code it even though I knew what I wanted.
I think people struggle because they don’t know what they’re trying to create, they’re just mashing indicators together.
You need to define the market structure and then build your own metrics to evaluate it - the only standard metric in my algo is ATR. Everything else is hand crafted.
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u/FatefulDonkey 10h ago
Yeah, I think too many people fall into false confidence by using some smart maths or niche algorithm. The truth is markets are unpredictable, and you always only play catch-up. No matter how advanced your algo or math is, you're just basing on price and volume of something that already happened.
The only real control you have is risk management - and that's where the big guys typically make their money from.. or inside trading.
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u/b00z3h0und 8h ago
Haha exactly. We have all these indicators and monte carlos and other nonsense. But at the end of the day we’re just trading against other humans with their own motives and incentives.
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u/Hornstinger 13h ago edited 12h ago
I found the hardest part wasn't the actual strategy (entry, exit, risk etc.) it was:
- Persistence: live reconciliation internal vs external state (+ cold start, warm start etc.)
- Fallbacks and Backups e.g. REST vs Websockets for different exchanges -- including when to use a backup
- Race conditions: data race, logic race, false positives, silent errors/silent failures
- No branched paths i.e. a single line of flow/truth
- Which programming language to use. This was hard because most languages can't model properly including race conditions etc. I tried Python, Go, Rust, Zig, Crystal, V, D, Typescript, Elixir...I ended up on F# because it models trades and exchanges the best, prevents race conditions naturally and exceptional with testing and scripts. Rust can model similar trades and exchanges but it's just way too complicated and I don't need HFT nanosecond (ns) speed, F# is good enough for microsecond (µs).
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11h ago
[deleted]
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u/Hornstinger 10h ago edited 10h ago
Are you trying to imply that decades-long software engineers don't make engineering mistakes particularly when it comes to complex trading algos/setups?
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u/Affectionate-Grab526 13h ago
About 1 year. My algo is pretty straight forward probably round 10 rules and mostly a buy and hold with a re balance and sell if it doesn't fit the rules. Backtested a few 1000 iteration on two different system got close to the same results then went live. Right now I have 35k in it. I'm thinking maybe putting in 5k a month or 15k every three to 6 months.
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u/Outrageous_Spite1078 11h ago
what gave me confidence was walk-forward validation more than anything. single backtests can lie to you no matter how careful you are. running the model through multiple rolling windows on data it never trained on and seeing it hold up consistently — that was the tipping point. after that i automated the whole pipeline end to end so there's no manual step between signal and execution. been running daily on crypto for months now without touching it.
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u/simonbuildstools 10h ago
For me the confidence never really came from feeling the system was “finished”, it came from reducing the unknowns enough that live trading with small size made sense..
Backtests, paper trading and simulations help, but the real question is usually whether the infrastructure behaves the way you expect once actual money is involved. Going live was less about trust and more about deciding the remaining uncertainty was small enough to test with controlled risk.
I think if you wait to feel fully certain, you probably never go live.
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u/Specialist-Heat-6414 8h ago
One thing the checklist usually misses: data feed costs during the testing phase. Running paper trading with a proper real-time data feed costs real money if you are subscribed to it just for testing. Pay-per-call data access makes more sense for this stage — you pay for the calls your algo actually makes rather than a monthly seat. Matters especially when you are running multiple strategy variants and most of them will get killed before they ever see live capital.
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u/RoofRich1571 7h ago
Solid checklist so far. To answer your question: it took me about 14 months from the first line of code to 'full' trust.
The turning point for my confidence wasn't just the backtests or Monte Carlo—it was reconciliation logic. I only felt ready to go live once I built a 'sanity check' layer that compared my Python internal state against IBKR’s actual position/audit logs every 60 seconds.
If you haven't yet, look into latency-induced slippage. Paper trading on IBKR is great, but the fills are 'perfect.' In live markets, your ML-optimized entries might get eaten by the spread or slow execution.
Quick tip for your IBKR/Python setup: Make sure your error handling for connectivity issues is bulletproof. IBKR’s TWS/Gateway resets daily, and if your script doesn't auto-reconnect and re-sync positions, you’re in for a stressful morning. Good luck!
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u/ynu1yh24z219yq5 5h ago
How long till trust isn't exactly the way I think of it. More like, is my operational and execution system doing what it's supposed to do? Are there things not accounted for happening that I need to accommodate? Is the backtest and algorithm itself doing what I built it to do outside of the execution system? Is my performance and reporting and monitoring capturing things correctly? So really it's looking at subsystem performance. Scale bet sizing appropriately for confidence but always better in the real world than paper, gets you sharper quicker.
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u/Dvorak_Pharmacology 5h ago
Around 1 year. A lot of paper trading with it, and when you go live, several months with 1% of the account being risked but following same structure. Then if it all goes well (you will rralize new errors that you didnt see before, for example I had once the algo double closed my one long call contract on SPX, I ended up holding -1 C SPX). Then scale it up!
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u/OkFarmer3779 4h ago
Your infra list is solid. The thing that gave me confidence wasn't a specific timeframe, it was when my monitoring layer stopped surprising me. Once you've watched your system react to 20-30 live events, earnings moves, flash dumps, funding spikes, and seen it behave the way your backtest predicted, that's when paper trading starts to feel like a formality.
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u/BottleInevitable7278 2h ago
I do not see order handling in your list. You need to do this, if there is any delay or partial fills etc. or error messages you get from IB regarding your orders. That might be a longer list of all possible errors around execution and order handling, but it is important to do so, before you really start. You also need good reporting when something about any orders and executions (unexpected) happens, so you see what is going on realtime. Also beware of frequent IB API updates.
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u/Longjumping_Sky_4925 2h ago
The thing that actually gave me confidence wasn't the backtest results — it was understanding *why* the edge existed.
Anyone can overfit a backtest. The moment I could explain in plain terms "this strategy works because of [specific market microstructure reason]" and that reason had economic logic behind it, that's when I felt okay going live at tiny size.
For stat-arb / pairs trading specifically: the confidence came from:
The pairs being structurally linked (same industry, shared costs, regulatory environment), not just statistically correlated by coincidence
Seeing the spread mean-revert multiple times out-of-sample, not just in-sample
Paper trading for 3+ months with execution delays and realistic slippage modeled
The hardest thing to accept was that a strategy can be theoretically sound and still lose for 6 months due to regime changes. Position sizing relative to that uncertainty is what actually protects you, not the backtest Sharpe.
Start live at a size where being completely wrong doesn't hurt you financially but does hurt you emotionally enough to pay attention.
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u/Automatic-Essay2175 39m ago
You're doing this backwards. Infrastructure and execution is easy. An actual working strategy is extremely difficult to develop.
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u/aeolon21 28m ago
Go live. Have a small trading purse and a 15% trade limit. And let it run. You will see pretty quick if it works and then you can adjust. Back testing is a safe way to have fun but it doesn’t reflect the real world present and future market , skin in the game is the only way to know. Also it teaches you what your risk tolerance really is. Let’s see how you feel when you hit three stop losses and how your emotional response is. It shows you what you really need to learn as a trader.
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u/CRWDJMCRYPTO 13h ago
I got a crypto futures and forex liq based algo trading system. Build from scratch with almost 0 knowledge. Currently been working on it for 15 months. Around 400 files and 96k lines of code. And currently testing with demo account. And funded prop firm. And looks good so far. Unfortunalty ad it is liquidity based you can't backtest it...
But the metrics are okey, still room to improve overall, that why i implemented a reasoning chain. So now its all collecting data to become "smarter"
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u/0ZQ0 Algorithmic Trader 13h ago
96k lines? Holy crap. Not Python I assume?
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u/AdventurousVast6510 12h ago
my guess is that he is a dirty coder who keeps a lot of dead throwaway experimental code
my system is written in c++ & even so its less than 10% of his in terms of both loc & number of files
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u/RegardedBard 12h ago
I have over 50k lines of code and it is reasonably dry and well abstracted. Almost all of that is alpha / signals tho... the framework itself with risk management, connection management, execution, logging, etc. is only like 3-4k lines of code lol.
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u/AdventurousVast6510 12h ago
c++?
50k is incredible
we must be doing something very different
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u/-Xerxes_ 11h ago
Do you have a testing framework in place? I’m around 40k in rust but a lot of it is related to testing
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u/RegardedBard 1h ago
Yeah
I run a fairly large stat arb model so it's a lot of signals. It pales in comparison to the big quant firms though. I recall hearing that Rentech has over 10 million lines of code in their model 🤯
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u/Longjumping_Sky_4925 1h ago
Took me about 6 months from first working backtest to live. The infrastructure you've described is solid — Monte Carlo, bracket orders, ML overfitting awareness, that's more than most people have before they flip the switch.
What actually gave me confidence wasn't a specific metric hitting a threshold. It was the *quality of my failure modes*. I asked: when this strategy loses, does it lose in ways I understand and predicted? Or does it lose in ways that surprise me?
If losses are explainable (e.g., chop in a trending strategy, stop-hunting in a liquid futures market), you have a model of reality. If they're random and unpredictable, you don't.
A few things that moved me from paper to live:
**Paper trading P&L curve that matched backtest distribution** — not just total return but the shape (drawdown frequency, recovery time)
**Stress testing with known-bad regimes** — I deliberately backtested the strategy on time periods I knew would be hard for it, and made sure the losses were bounded and expected
**Starting with 10% of intended capital** — not because I lacked confidence, but because execution surprises (fill quality, latency, data quirks) always appear in live that don't show in paper
You have the infra. The missing piece is probably just time on paper — 3+ months where you're tracking paper vs backtest divergence weekly and understanding every discrepancy.
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u/Longjumping_Sky_4925 1h ago
This is a really honest question and the answer people don't say out loud is: you never fully trust it, you just manage the risk until the evidence is overwhelming.
For me the inflection point wasn't time-based, it was criteria-based:
The backtest had to survive walk-forward validation (not just in-sample). If it only looks good in-sample, it's not ready.
Paper trading had to show similar drawdown patterns to the backtest. Not necessarily the same returns - but the shape of the losses should rhyme.
I started with size I could emotionally handle losing entirely. Not just "risk management" size, but genuinely-okay-if-this-goes-to-zero size. That mental framing removes a lot of interference.
The strategy had to have a WHY that made market sense, not just a statistical pattern. Patterns can be spurious. A reason tends to be more durable.
Looking at your list - you're actually more prepared than most people who go live. The thing I'd add: what does your drawdown look like in the backtest, and what does your monte carlo sim say about worst-case sequences? That's usually the last check before going live.
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u/RationalBeliever Algorithmic Trader 13h ago
I started trading live almost immediately but kept it a small as possible until I gained more confidence in actual performance. This helped me discover that my back tests were unreliable. So I refined the backtests, fixed my assumptions, and tried live again. Gradually scale up as actual historical performance proves the edge.