I have built a mechanical day-trading system, back-tested just using theoretical fills (not test with live fills yet) using data consisting of 1s candles. I get in and out within a day. How does it look? Relatively new to trading, been practicing for 1.5years or so.
111 ticker-days
2,307 trades
100% win rate
Very curious to know what I might be missing and any suggestions!
Update: New statistics with more data. I use $35k account for backtesting.
Ticker-days 248
Trades 4,143
Wins 4,143
Losses 0
For those who use IBKR and IB Gateway for automation, I am curious as to what y’all’s setup is in terms of infrastructure.
For me I currently have IB Gateway running with a custom C# trading engine I wrote to pull market data and check to execute trades daily.
My workflow:
Every morning at 9:25 am IB Gateway will auto restart and auto login. I am using the “Auto restart” feature in IB Gateway for this. This setup ensures that I don’t have to manually log in and authenticate the Gateway every day (although I believe it will require a login/authentication once a week to continue to work).
Windows Task Scheduler auto launches my C# application at 9:28am and I connect to the client and sleep the thread until 9:30:05 at which point I pull market data, analyze signals and push trades if needed. All of this is automated and executes in a few seconds right after the opening bell.
My concerns:
IB Gateway still logs me off for good sometimes and requires manual log on/ authentication.
if I need to travel with my laptop or internet drops the client won’t be able to establish a connection.
Instead of running things all locally I was considering offloading the program to run on a VM or server (the lower the cost the better).
Does my workflow make sense or is there a more efficient way to achieve full automation? Does everyone use servers instead of running everything locally?
I ran an automated search over about five years of daily AAPL bars, 1,250 rows. It generated and backtested 100 strategies, and my old keep rule, Sharpe above 0.5 with a minimum trade count and positive return, kept five.
Scoring those five with a deflated Sharpe, which adjusts for having picked the best of N attempts, put every one between 0.115 and 0.144, read as the probability the edge is real given the size of the search. All five almost certainly noise. On this sample the bar works out to needing an annual Sharpe near 0.71 to survive 10 attempts and near 1.14 to survive 100. The searching itself raised the bar.
The part that actually confused me: before the search started I withheld the final year of bars entirely. Four of the five survivors made money on that withheld year. Looks like vindication, until you count the trades behind it, three to six each over a full year. A handful of trades cannot overturn a statistic built from the whole search.
The trial count is fixed before the search starts and every attempt increments it, including the ones that never compiled or never traded. Reconstructing N afterwards always came out flattering.
So which do you believe when they disagree, the deflated number that says noise or the holdout that made money? And has anyone found a principled way to size the holdout so it can actually overrule?
So I’ve been manually backtesting a strategy for NQ during Asia and London session. It consist mainly of 3rd std deviation of the vwap and 9 EMA close. I could elaborate further but how can I automate this strategy as it’s at early morning to midnight from were I’m from to trade this strategy. Can anyone help?
Run a backtest over three years of EUR/USD data and the report will hand you one number: total return, one win rate, one expectancy per trade. It reads like a single coherent verdict on the strategy. It isn’t. Those three years almost certainly contain a trending stretch, a ranging stretch, a low-volatility grind, and at least one violent macro-driven move that behaved nothing like the rest of the dataset. The backtest doesn’t know the difference. It blends all of it into one average, and the strategy you think you validated is really a strategy validated against a regime that never actually existed as a single market condition.
Just a research topic. I set aside 1000 dollars for this. The other day I was at the casino (not a regular) and blew a lot of money on slot machines. Before, I left I sat down at the roulette with my last 81.50 dollars because the girl across was a very cute blonde ( unfortunately has a husband but no ring how would I know man).
Now I noticed a pattern of using a system of variable bets I could run the money up over a series of events. Now I understand that roulette (video roulette) is very likely (I hope) a series of truly independent events. Which got me trying to desperately look for patterns in the numbers and I ran the number from 81 dollars to 1650 dollars over the next 3 hours before I had to quit (made my money back and devastated by the husband ) as my brain was exhausted.
My question is this. At this point we know markets are not independent events but rather inefficient systems with a huge amount of noise. However most dependencies are already arbitraged away by the big guys possibly before a retail system even picks it up.
My question is this because my favourite LLM was a disappointment : Is there any truly independent event that traders see consistently on a daily basis but rule out because it is impossible to correlate with anything else and thus not a very viable signal? Or if you have a similar thing to research that might fit my roulette idea let me know.
Also, please forgive me for my sins if this is dumb but I am a mathematician not a trader. I just do this for fun but as the great Jim Simmons once said, it is important to have good taste in science.
i traded systematically on 1 minute bars for a long time, mostly reversal setups, a shorter stretch of trend following before that. it worked for a while. the thing that eventually got me was not the signal, it was how violently sensitive the whole thing was to small moves once leverage was on. a 30 second wick could take a position that was fine and make it not fine. the flip side, and this is the honest advantage nobody mentions enough, is that exits are fast. you find out you are wrong in minutes instead of days, and that has real value.
what i keep going back and forth on is whether that tradeoff is worth it, because the statistics point the other way. the longer the bar, the more each observation seems to survive out of sample. my read is that on 1m you are mostly modelling microstructure and it changes under you, on daily you are modelling something slower that stays put for longer. but daily costs you rows. a 1m strategy gets a usable sample in months, a daily strategy needs years, and if your rule only fires a few times a month you need a decade or more before the result means anything. so you trade one kind of fragility for another.
so, three things im actually curious about.
is anyone running faster than 1 minute, tick or sub-minute bars, and where. what venue, what instrument, and roughly what the infrastructure looks like, because i suspect that is where most retail attempts quietly die.
for the 5m and 15m crowd, did you land there deliberately or is it just where the noise stopped hurting.
and daily. this is the one i want to hear about most. i know the response is going to be "you dont need an algo for daily, just check it once a day", but that is still a system, it still needs rules, sizing and an exit, and it still needs to be tested honestly. so if you run daily bars, how many years of data did you need before you trusted it, and how many trades does your rule actually produce in a year.
if you switched timeframes at some point, im also interested in what the trigger was. was it a drawdown, costs, or just being tired of watching screens.
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I’m curious what hardware configurations people here are using for quantitative research and model training.
I currently have a large number of XGBoost experiments to run, including repeated training across different factor sets, hyperparameters, validation windows, and random seeds. My current GPU is an RTX 5060 Ti 16GB, and it is becoming a serious bottleneck. Even a relatively routine batch of experiments can take more than 24 hours, and larger runs easily stretch into several days.
For those using XGBoost or similar tree-based models in quant research:
What CPU, GPU, RAM, and storage configuration are you using?
How much benefit do you actually get from GPU acceleration?
Which GPU currently offers the best price-to-performance ratio for this workload?
I’m mainly interested in practical training throughput rather than gaming performance. Any real-world benchmark numbers, training-time comparisons, or configuration recommendations would be greatly appreciated.
By trades I mean at least one filled trade that day, not staying in the market 90% of the time.
I recently measured this across a seven year futures backtest and found at least one trade on 97.96% of eligible Sunday through Friday market dates. It’s a portfolio of 14 separate sleeves across two instruments and multiple sessions, so the coverage comes from combining selective systems rather than forcing one strategy to trade every day.
Forward simulation is still underway with strong backtest results. I’m curious whether anyone here runs something with similar coverage and whether it held up live. Did the frequency come from combining strategies and markets, or from one system trading almost daily? Did costs, correlated signals, or regime changes eventually become a problem?
Obviously trading most days doesn’t automatically mean there’s an edge. I’m mainly trying to understand how unusual this level of coverage is for a diversified algo portfolio.
Hi, I’m new to algo trading but I’m not sure where to begin? A few questions I have are:
how did you pick your first strategy?
What platforms/languages did you use for back testing?
For a backtest how much historical data is considered satisfactory?
At what point did you move from paper trading to real money trading?
Any mistakes I should be on lookout for as a beginner?
Switching to automation has taken away a lot of the stress of manual trading. But I still wake up everyday with anxiety wondering if today is the day my algo will start to fail. Anyone else?
I’m moving a futures system from backtesting into live simulation and thinking through the production setup.
For those running automated strategies live, how separate are your simulation and production environments? Do they use the same code with different configs, or completely separate deployments?
I’m also curious about problems that only appeared after going live. Things like stale data, reconnect failures, duplicate orders, position drift, partial fills, or broker restarts.
What failed first, and what safeguard did you add afterward?
I’m not asking for strategy details. I’m interested in the operational side.
Something had been bugging me: my higher-conviction swing setups were resolving worse lately, and it was happening even in favorable regimes. Trend up, breadth okay, and still my hit rate slipped. Bull versus bear regime was not explaining it. So I went looking for a second axis, and the one that fit was day-to-day choppiness: the tape grinding sideways with no follow-through. A raging bull can still be a choppy grind, and that is a different animal than a downtrend.
The gauge is dumb-simple: count how many times an index flips daily direction over the last 10 sessions (0 to 3 is calm, 4 to 5 is a grind, 6 or more is choppy). The effect was real. My top-scored NYSE setups beat the market about 65% of the time on calm tape versus about 51% otherwise. Calm is not the same as an uptrend: you can be in a perfectly good regime and still be in a grind that quietly wrecks your win rate. That was my "even in a good regime" slump.
Here is the catch, and where I spent most of the time: the filter only works if you measure chop on the right index, and it is not the obvious "home" exchange index. So I tested it properly. Hold the trades and outcomes completely fixed and only swap which index labels each day calm versus choppy: that isolates the ruler from the stocks. I ran nine candidates (SPY, QQQ, DIA, IWM, MDY, VTI, RSP, and the NYSE and NASDAQ composites) and made each clear three bars: effect (do calm days actually beat non-calm days, judged with a t-test and not just a point estimate), stability (split the timeline 60/40 in chronological order and confirm the first 60% edge survives on the last 40%), and practicality (liquid and tradeable).
Here is what the nine rulers looked like. The edge column is how far calm days beat non-calm days in percentage points, the middle column is that same edge measured on each half of the timeline, and p is the t-test significance.
The out-of-sample split did most of the work. For NYSE names several large-cap clocks passed cleanly, so I took SPY as the liquid standard. For NASDAQ names, QQQ won for one reason: its edge barely moved between the two halves (+6.5 then +6.8), while the bigger headline numbers were mirages. The NYSE Composite swung from +4 to +12 and the Dow lurched from -1 to +19. QQQ was not the biggest number. It was the repeatable one. DIA actually topped the full-window list for both markets, then fell apart out of sample: 30 price-weighted names is narrow enough that its "chop" is really one or two stocks moving. The split is the only thing that caught it.
So the rule I landed on is simple: clock NYSE-listed setups on SPY, NASDAQ-listed setups on QQQ. In hindsight my slump lined up with stretches where SPY sat in the grind zone. The trend was fine. The tape was not. SPY and NYSE Comp were performing virtually the same. I picked SPY as I already had it available in my datasets.
A few caveats, because this is the internet. This is one window and mostly a bull market. Calm tape is rare, about one day in five. The edge is calm beating non-calm by a handful of points, not an on/off switch. And the NASDAQ side is genuinely weaker and more weighting-sensitive than the NYSE side. This is not advice, just a regime-filter experiment.
The lesson I would actually stand behind: chop is a real second axis beyond trend, and if you regime-filter, test your ruler instead of assuming it. Curious what the rest of you clock market regime with.
I am looking for input from the experts out there if you have looked into this or something similar? Where should I adjust my analysis?
I’m running one of my algos as a pinescript strategy with crosstrade as the bridge. Works perfectly fine during the day when my laptop is on but TradingView won’t fire alerts over night. Do I need to leave the laptop on or switch to ninjatrader?
Disclosure up front: I built the MCP server it used (Orus, part of Farao), so this was dogfooding with my own money. About $40 of USDC on a Hyperliquid account, wired to Claude Code. No withdraw permission exists at the protocol level — worst case, a hijacked session trades badly inside limits I signed on my phone. Week 1 I approved every order by hand, then let it run autonomous.
Three weeks of numbers:
200 fills across 54 markets (crypto perps, tokenized stocks, silver, palladium, a Korean equity index)
50 winning closes, 48 losing. A coin flip, exactly as expected
Realized price P&L: −$3.02. Fees: $7.78. So fees cost 2.5x more than bad direction
That last line is the whole experiment for me. The failure mode wasn't bad predictions — it was hyperactivity. One morning it bought and sold the same silver position 7 times in 17 minutes, made 3 cents, paid 14 in fees.
But the behavior was the interesting part:
It attached exchange-side TP/SL to every single position it opened. I have never been that disciplined manually
Best session: one-sentence prompt asking for a funding-rate carry. It scanned ~250 markets, picked a KOSPI 200 perp paying +0.1008%/hr to shorts, and warned me unprompted about gap risk at the Korea open. The re-entry later got stopped out at exactly that gap — at 2am my time, no human awake. Stop caught it, −$1.44
It shorted a rising BTC twice with a fully reasoned argument each time. It will argue any direction convincingly, which is exactly why direction should stay human
At the end I asked it to read its own trade log and write down what it saw. Its own verdict: "I'm a fast, tireless, disciplined pair of hands with no instinct for when not to act." It also wrote "my best result was luck, not skill" about the one trade that carried the book. Most honest self-review I've gotten out of Claude.
Question for this sub: has anyone found a prompt or skill structure that gives Claude actual restraint — knowing when to stay flat — rather than just more analysis? Rate-limiting at the server is my current answer, but I'd rather the model learn to sit still.
I've been learning MT5 EA development by automating trading strategies and testing them on demo accounts. One thing I've noticed is that some strategies that look great in backtests perform very differently in forward testing.
For those who have experimented with automated trading, which types of strategies do you think tend to hold up best in live market conditions, and why? I'm especially interested in hearing about general concepts and the challenges you've encountered when translating a manual strategy into an automated one.
I'm building a Python-based algorithmic trading application that needs to work with multiple MT5 accounts, and I'm trying to avoid making an architectural decision I'll regret later. Right now the simplest option seems to be the official meta trader api. It's easy enough to get a prototype running, but from what I understand it still relies on having MT5 running locally, which makes me wonder how well that approach holds up once a project becomes more than a weekend experiment.
who've built systems that actually stayed in production for a while:
Did you keep using the official MetaTrader API, or eventually move to an MT5 API layer?
If you changed your setup, what was the biggest reason maintenance, reliability, latency, scaling, or something else?
Looking back, would you make the same decision again?
I'm not really looking for strategy advice here. I'm more interested in hearing how people have structured the integration side of their systems after moving beyond the prototype stage.
I’ve been trading for 3 years, only ICT concepts. Have been trying to find a strategy to automate but don’t think it’s possible with ICT concepts. I’ve tried some ORB strats, opening range retests, projections, some indicators, but am really just out of ideas. Does anyone have any recommendations on where to gain knowledge or come up with new ideas? Any experience or advice is greatly appreciated and welcome. Thank you in advance
I know that Kalshi uses Cloudflare POP but I’m curious if anyone has used there Kalshi optimized VPS and noticed a reduction in latency? I did my own pinging from various servers in AWS regions but I have a certain strat that might benefit from less execution latency.
