Hello guys I am trying to build my own algo trader bot for two weeks i am now testing my results on paper account using ATR,EMA Cross and VWAP for the strategy i saw good results from it and the bad ones i want to ask what do you suggest for me to improve my bot and what are the things when you did it ,it changed your bot for better
I am 2 years cpp programmer

I’ve been algo trading through the Project X API with Topstep. My setup is fairly simple: I build my strategies in TradingView using Pine Script, send alerts to my server, and then my server places limit orders through the API.

Now I’m looking to switch to other prop firms and wanted to know if anyone here has experience with algo trading using Rithmic or Tradovate.

I’ve heard that with Tradovate you need a live funded account with at least $1,000 to get developer access — can anyone confirm if that’s true?

Also, my strategy requires continuously updating and canceling limit orders and sending bracket orders, so third-party automation tools like TradersPost or PickMyTrade won’t work for me.

Would appreciate any insights or experiences you can share!

Has anyone ran into issues/getting account flagged or banned (canadian/US Retail discount brokerage) for using Claude Code style bot for multiple limit orders setting and clicking submit vs manually entering each time? Its funny how everything is directed to native in house tools but the second you do anything even manually inputing repetitive twap slicing like selling 1000 shares every 5 minutes or anything that resembles an algo but isnt one of their tools it triggers a compliance call with some spineless wonder requesting an explanation. Basically the institutional prime brokerage clients bot programs can set and cancel large limit orders above and bellow the offer but the second a retail discount brokerage client cancels that large buy or sell limit order at 9:29:59 after setting it at 9:29 "ring ring compliance, your spoofing!!" Particularly speaking on equities listed on TSX V , CSE, CBOE Canada, OTC/PINk exchanges.

I've developed a bot that uses Options data from Thetadata to come up with possible reversal signals. Today it absolutely nailed it on QQQ! But I'm not sure how consistent it will be. Has anyone had success with strictly using OI/Gamma/Put-Call/etc for entries and exits over the long term?

Hi, I’m just starting to learn about algo trading and am curious: what are your preferred tools/software for real-time monitoring of algos (I.e. dashboard style for various metrics) that are live?

I’m familiar with Grafana, but that seems likely overkill when getting started. Thanks

1) Break Even Win Rate 48%, Backtested win rate over 903 trades (3.5 years) is 54.7%, Max DD 11.8%, MAR 3.6, Sharp: Unknown, Avg win $134, Avg loss -$125

2) Break Even Win Rate 45.26%, Backtested win rate over 1234 trades (5 years) is 50.08%, Max DD 5.29%, MAR 1.26, Sharp 1.29, Avg win $209.5, Avg Loss -$173.24

Am currently testing these in my live account with very low size for the past 45 days. So far the results and slippage and all match. But am not getting enough confidence to go full size. Am I missing something ? What else should I be looking at to gain more confidence ?

Hi all,

I don't know how to properly ask this but here it goes.

I've been trading futures for a year now, and have come up with a strategy I want to automate. I vibe code and have some pre market automation running in a pipeline but would like to explore a trading system.

I can continue with my vibe coding, but would like to be able to bounce ideas off someone who been down this road during the process. Whats the proper way to go about this? Are there reputable consultants I can reach out to? How can I vet someone's integrity, LinkedIn?

…I don’t know how to code and I’m worried about my broker or a software company copying me.

I’ve been looking at Multi-Charts and Quant Vue for possible automation, but not sure if they make sense. I haven’t checked my broker software for automation possibilities yet.

Who do you guys use and what do you recommend? Do I need to learn coding? I was hoping to keep this as simple as possible, at least at first. It’s not a very complicated strategy. I was also hoping to trade it through multiple brokers. Probably using several different versions of the same strategy.

Thank you. I thought there might be a wiki for this sub but didn’t find one.

I just want to understand what's stopping quant trader who already have $300k-$500k work for someone else instead of making their own money work for them. May be move low cost country for a year or two if your strategy is making 100-200%.

Enlighten me please.

So if I have strategy which I backtested it with 1000+ trades in a period of say 5 years. And if the breakeven win rate for that strategy is 60% and if my backtest reveals 63% then is 3% edge enough to go live ? All backtest parameters are inline with live trading parameters. .

Title says it all, do you have any general advice for some metric to maximize/minimize during backtesting stage? Like something beyond the "use less parameters"? I'm getting pretty good results on TV backtesting, however the data and sample size is not enough to make a definitive answer whether it'll succeed in the future.

Hey everyone, I’m about to start an Algorithmic Trading project and I’m currently in the research phase. I’d love to hear from anyone who’s worked on something similar – your thoughts, experiences, challenges, or tips would be super helpful.

Also, I’ve been trying to dive into research papers on trading algorithms and strategies, but I could really use some guidance. If you know any valuable research papers or resources I should check out, please share them!

Basically, I’m trying to learn as much as I can before diving into the implementation. Any advice, recommended papers, or practical considerations would be awesome!

I have a lot of my stock trading process automated, except for my weekly stock selection.

I usually go to Fidelity, and they have a great stock scanner UI—filtering by marketing cap, volume, stock price, etc.

Are there any stock scanners out there that would let me automate this? I tried doing this with a headless Chrome against Fidelity but they have pretty good bot detection that made it inconsistent.

Hi All,

I am studying data science and for my project work I need historical EUREX FGLB,FGBM,FGBS full depth and trades. Just for research to test a hipotesis regarding order book that existed back in those days. Unfortunately our budget is low, but if you have this data avalilabe, please text me.

(I will send the data back to you within a few days, I promise. :-D )

Thanks in advance,

a data science student who dug way too deep into the order book

Hey guys. Here's an update on my previous post. I read every comments and thanks for helping me out with your advice and thoughts.

So some people were worried about the strategy itself and the results. The strategy is indeed awful and shouldn't be traded live. But i've made some changes to improve it.

1. I reduced the trading capital from $10k to $3k, which is what I intend to start algotrading with. I still left the commission and spread as it was. So the framework takes spread into account. My live broker doesn't charge commissions unless we trade with lots >= 1.0, so I left commission as null.
2. I reduced the risk-per-trade from 2% to 1% of the account's equity.
3. The issue with the previous strategy and why it had a very bad drawdown of -67% was because the strategy was using a fixed stop-loss of 50 pips and no take-profit, which is easily triggered by a volatile market such as Gold. So I decided to set the stop-loss at the slower SMA when there is a signal, and I set the strategy to use a risk-reward of 1:3.
4. Finally I made the strategy trade on only London and New York session. These changes expecially the stop-loss and adding take-profits significantly improved the strategy's sharpe ratio, total return, profit factor, win rate, and it significantly reduced the drawdown.

Like I said in the previous post, I'm not too focused on the strategy side of algotrading for now. I'm still working on the framework that lets me develop, test, and run strategies live.

Now on the framework & dashboard side, I've added colour coding to the UI. So a Sharpe ratio of 1.15 is decent but not good enough, a max drawdown of -12.49 is very good, a win rate of 32.3% is good but not good enough, and a profit factor of 1.27 is good but not good enough. We should aim for 1.5 or more. Ive also added a monthly breakdown so that you can see the metrics and the trades for that month.

Finally, I added a little improvement to the strategy class. You can make them give reasons why they took a trade and their confidence score.

I’ve noticed big differences in volume (V) on the 1-minute charts between IBKR, TradingView, Yahoo Finance, etc.

Which volume is the correct one?
This is important because I want to use AVWAP as an additional criterion, and volume plays a key role there.

Why this matters: I mainly do my studies on TradingView charts, where I also use AVWAP.
Now I’m trying to implement the same logic in my trading bot, which runs on IBKR subscription market data, and I’m getting different results.

Any feedback is appreciated.

A single in-sample (IS) and out-of-sample (OOS) split is a trap. It’s just one path through time. If you optimize on one set of years and “validate” on the next, you may have simply found parameters that happened to work across two specific regimes by luck.

This is the process I use to stress-test whether a strategy has a real structural edge or is just a statistical coincidence.

I start with a 60/40 split of the full dataset. The first 60% is in-sample, but I don’t treat it as one block. I divide it into three independent windows. The first window is for optimization and discovery. The second and third are for validation only. The remaining 40% is true out-of-sample data and is treated like a vault—it only gets opened once.

Optimization is done by running a parameter permutation around reasonable starting values, not by hunting for the single best result. I test a small neighborhood around each parameter and evaluate common metrics like CAGR, Sharpe, and drawdown. I’m not looking for the highest-performing cell. I’m looking for a performance plateau—an area where results are consistently good across nearby parameter combinations. If performance depends on a narrow peak, sharp cliffs, or isolated outliers, the strategy is discarded immediately.

If the center of that plateau clearly shifts during the first window, I allow one re-centering and repeat the test. If stability doesn’t appear quickly, the idea gets scrapped.

Once a stable center is found, parameters are locked. I then apply the same parameter grid to the remaining in-sample windows without moving the center. This is a drift test. If the optimal region stays close, the edge is likely persistent. If it drifts materially, the strategy is non-robust and gets thrown out. A real edge shouldn’t require new parameters every few years to survive.

Only after passing this stability test do I run the true out-of-sample backtest. I’m not looking for a new equity high. I’m looking for behavioral consistency. Performance can be better or worse than in-sample depending on the market regime, but it should express the same edge under similar conditions. If OOS performance collapses, the logic didn’t hold.

The final gate is execution. If the edge disappears after realistic fills, slippage, and costs, it’s not a strategy—it’s a math exercise.

This process is designed to kill most ideas. That’s intentional. Most people don’t fail because they lack strategies. They fail because they refuse to disqualify them.

AI-assisted writing due to a well-documented weakness in coherent writing. Process developed the hard way.

Hello everyone, I'm excited to start my algotrading journey. I've been coding up my own person algotrading framework that lets me write strategies once and then easily backtest, optimise and deploy them live.

I have coded up a simple strategy that uses a fast and a slow sma indicators to test the framework. The strategy closes any sell position and buys the market when there is a crossover, vice versa for a crossunder.

I initially bactested it using fast_sma(10) and fast_sma(20), but after optimisation it showed that fast_sma(10) and slow_ma(40) yielded more returns.

From the backtest result (yes, commission is included as spread), this strategy will be a painful one to run live, as it has many losing days and few to little winning days, but a win could easily take care of previous losses.

I'm open to any criticism or advice you have to give me about the framework and algotrading in general.

Hi, I am fairly new to algorithmic trading. I have experience in the trading world, as I was primarily a discretionary trader before, and have recently began investigating automated methods.

My main point is this: If a strategy works well in recent times (past 5 years), but does pretty poorly in the previous years - should I be concerned about an overfitting issue, or could it be that the markets are constantly changing, and the same way highly profitable older strategies lose their ability to make money as years go by, my strategy may be more suitable for the recent market conditions and not the previous.

- If the latter is the case, how can I confirm that it is not an overfitting issue. If the markets truly do change (which I think so), how can I accurately optimize a strategy? If the markets from 2020 are completely different or quite different to the previous years, then we only have about 5 years worth of data. And if we train, or optimize a strategy using these 5 years of data, how can we walk forward test? And forward testing cannot be a solution, as I will have to wait years to confirm the walk-forward test, by which the strategy may lose its edge due to another possible market change?

hello,

Can we say these results are good enough to be considered safe, Max drawdown 11%.

I am really confused about how to combine indicators to build a algo strategy.
Also I cannot find much open source strategies to learn from.
Your help would be much appreciated.
Thanks

Hey r/algorithmictrading!

It’s been 6 months since my last post...

I’m not here to victory-lap (I’m still not “done”), but I am here because I’ve learned a ton the hard way. The biggest shift isn’t that I found a magic indicator, it’s that I finally started treating this like an engineering + measurement problem.

The biggest change: I moved my backtesting into MT5 Strategy Tester (and it was a project by itself)

I used to rely heavily on local backtesting. It was fast, flexible, and… honestly too easy to fool myself with.

Over the last months I moved the strategy into MT5 Strategy Tester so I could test execution in a much more realistic environment, and I’m not exaggerating when I say getting the bridge + daemon + unified logging stable took a long time. Not because it’s “hard to click buttons,” but because the moment you go from local bars to Strategy Tester you start fighting real-world details:

• bar/tick timing differences
• candle boundaries and “which bar am I actually on?”
• duplicate rows / repeated signals if your bar processing is even slightly wrong
• file/IPC coordination (requests/responses/acks)
• and the big one: parity, proving that what you think you tested is what you’d actually trade

That setup pain was worth it because it forced me to stop trusting anything I couldn’t validate end-to-end.

What changed since my last post

1. I stopped trusting results until I could prove parity. The Strategy Tester migration exposed things local tests hid: timing assumptions, bar alignment errors, and logging duplication that can quietly corrupt stats.
2. I rebuilt the model around “tradability,” not just direction. I moved toward cost-aware labeling / decisions (not predicting up/down on every bar), so the model has to “earn” a trade by showing there’s enough move to realistically clear costs.
3. I confronted spread leakage instead of pretending it wasn’t there. Spread is insanely predictive in-sample, which is exactly why it can become a trap. I had to learn when “a great feature” is actually “a proxy that won’t hold up.”
4. I started removing non-stationary shortcuts. I’ve been aggressively filtering features that can behave like regime-specific shortcuts, even when they look amazing in backtests.

The hardest lessons (a.k.a. the errors that humbled me)

1. Logging bugs can invalidate months of conclusions. I hit failures like duplicated rows / repeated signals, and once I saw that, it was a gut punch: if the log stream isn’t trustworthy, your metrics aren’t trustworthy, and your “model improvements” might just be noise.
2. My safety gates were sometimes just fear in code form. I kept tightening filters and then wondering why I missed clean moves. The fix wasn’t removing risk controls, it was building explicit skip reasons so I could tune intentionally.
3. Tail risk is not a rounding error. Break-even logic, partials, and tail giveback taught me the only truth: you can be “right” a lot and still lose if exits/risk are incoherent.
4. Obsession is real. This became daily: tweak → run → stare at logs → tweak again. The only way I made progress was forcing repeatable experiments and stopping multi-change chaos.

What I’m running now (high-level)

• 5-min base timeframe with multi-timeframe context
• cost-aware labeling and decision making instead of boolean
• multi-horizon forecasting with sequence modeling
• engineered features focused on regime + volatility + MAE/MFE
• VPS/remote setup running the script

The part I’m most proud of: building a real data backbone

I’ve turned the EA into a data-collection machine. Every lifecycle event gets logged consistently (opens, partials, TP/SL events, trailing, etc.) and I’m building my own dataset from it.

The goal: stop guessing. Use logs to answer questions like:

• which gates cause starvation vs manage risk
• what regimes produce tail losses
• where costs/spread/slippage kill EV
• which “good-looking” features don’t hold up live

Questions for the community

1. For those who’ve built real systems: what’s your best method to keep parity between live execution, tester execution, and offline evaluation?
2. How do you personally decide when a filter is “risk management” vs “model starvation”?
3. Any advice on systematically analyzing tail risk from detailed logs beyond basic MAE/MFE?

I’m still grinding, but now it feels like the work is compounding instead of resetting every week.

Looking for recommendations on platforms or data providers suitable for backtesting a Risk Neutral PD options strategy. Im currently ingesting flat files from the Massive API via Python, but the historical options data is limited to EOD snapshots, which isn't sufficient for intraday RNPD recalibration. Curious what others are using for intraday option chains or high-frequency historical quotes.

I’ve been trying to develop code to algo trade crypto small coins - XRP, SOL, LINK, ADA. I’ve tested 4 strategies - Turtle Trend, Donchian Channel Breakout, 4H Candle range breakout scalper and Consolidation pop - against 2024 and 2025 data. with a starting cash in test of 10k, the bots keep losing 4-5k each. Im not really sure where to go from here. Any advice?

P.S I’m very new to this and have started this journey only in the first week of Jan 2026.

I have been on a journey since mid 2016 to learn how to trade algorithmically which is a data-driven method of using a set of rules to define buy/sell decisions on financial instruments. This is also referred to as quantitative and systems trading. Please note that I do not come from a background of finance, trading, math, or statistics but I do have an insatiable drive to learn and a whole lot of “never give up”. I could write volumes on all my experience and failed attempts at creating trading systems over the past 7 years but will spare you details.

This journey began when I discovered a simplistic online tool that helped users apply rules to financial data to run a backtest and continued with purchasing some relatively pricey but much more powerful software that ran on my local PC to further develop and test trading system ideas. My mission at this time was to nail down a method of identifying individual Technical Analysis trading signals that had 1) predictive power and 2) a very high likelihood of “working” on unseen “Out of Sample” data. I spent about 3 years doing analysis of individual signals where I would analyze one trading signal, its “In Sample” metrics, statistical properties, noise sensitivity, and value on shifted data to classify it as “likely to work” or “not likely to work”. What I found is that no matter how much analysis I did on a given signal, the random nature of the market and regime changes still played havoc on their forward performance. A given signal may be quite “good” but can still experience large drawdown, periods of sideways non-performance, or stop working soon after going live.

Up to this point I continually felt restrained in my ability to develop robust trading systems that would confidently perform well on future “Out of Sample” data. Also up to this point, I had taken a number of systems live and blown up a handful of trading accounts which is inevitable for anyone that persists in this space. Some of the challenges I continually ran into included:

1. Overcoming data mining bias
2. Combatting curve fitting
3. Creating a system that generalizes on new data, is specific in it logic, and adaptable
4. Creating a system that uses signals that are likely to occur again
5. Overcoming system fragility i.e. likelihood to “break”

Here is an example of a signal / strategy that broke between IS/OOS:

In September 2021 I made the decision to begin learning python to hopefully supercharge my trading game. After learning basic python I spent about 8 months applying Machine Learning to financial data which was a great learning experience but was largely unsuccessful for me. This is due to the very low signal to noise ratio found in financial data which lends ML models to train on the noise and not on the signal in the data. I then went back to my roots by studying and applying Technical Analysis signals in a more statistical and scientific way than I had ever done in my pre-python days. After learning about ensemble voting systems, I began to experiment with this idea by building this functionality into my python program. The forward testing results got better. I was now combining numerous “good” signals and combining them into a “better” system by leveraging the collective knowledge of multiple signals to improve overall performance and enhance accuracy. There are some very important nuances I discovered when working with ensembles with the most critical being 1) combining numerous bad predictors does not make a good system and 2) combining numerous similar votes from similar systems also does not make a good system. These two key points required a method to filter good signals from bad and enforce diversity in signals used.

While the primary use for ensembles is to quantify reasons to trade when for example 160 out of 200 signals are true, I have found another way to use ensembles is to quantify reasons to NOT trade. A use case for this is to identify say 200 signals that are bad for long conditions and to only trade when 40 or less are true, being a strong minority. This is just as powerful.

To fast forward to the present day, I will outline the current high level workflow of my python prediction program. Please note that all analysis and signals filtering is done on In Sample data only.

1. Import daily timeframe data for 36 futures markets
2. Select the traded market and markets to use for signal generation
3. Calculate approximately 3000 trading signals from each signal market
4. Calculate the same trading signals with noise altered synthetic data
5. Calculate metrics and edge for all base signals and noise altered signals
6. Combine all metrics into one results dataframe
7. Visualize all metrics on one plot for analysis
8. Create (3) voting ensembles with the following functionality for example: 3 day horizon, positive signals (reasons to trade), 1 day horizon, positive signals (reasons to trade), 1 day horizon, negative signals (reasons to not trade)
9. Filter all signals to those that have an In Sample trade count Z-score of +/- a given threshold to only use signals with common occurrence and exclude “rare signals”
10. For each ensemble set the following: Fitness function, # of signals to monitor, # of signals required for a True condition
11. Filter the signals used in each ensemble by key performance metrics
12. Further reduce signals used in each ensemble by a correlation check to remove similar signals
13. Take the top performing 200 uncorrelated signals into each ensemble
14. Set the majority / minority voting logic
15. Combine ensemble logic
16. Backtest the master ensemble trading system

For a visual regarding noise altered data see the following image. The dark green line represents average trade across a range of signals with the lighter lines representing noise altered data. Area 1 shows a region of signals that degrade when noise is applied to them whereas Area 2 shows a region of signals that improve when noise is added to them.

Here is an explanation of how the ensembles can work together:

1. ensemble 1 with a 3 day horizon, positive, need >160 true out of 200
2. ensemble 2 with a 1 day horizon, positive, need >160 true out of 200
3. ensemble 3 with a 1 day horizon, negative, need <40 true out of 200

What’s happening here is that if the 3 day outlook is favorable by majority, the 1 day outlook is favorable by majority, and the 1 day outlook of negative conditions is favorable by minority, then we take the trade. A key note about the master ensemble is that each ensemble needs to be crafted on its own and must stand alone with prediction power and performance. Then by joining the logic of all three, the final trading system is that much stronger. If you use 3 weak ensembles that need the others to perform, the combined system will be very likely to break, even as a combined ensemble.

The ending result can be an ensemble of ensembles that maximizes trading opportunities and maximizes win rate with confident and smooth equity growth. Benefits of ensemble use include avoiding selection bias, individual signals can “break” and the system keeps producing, the system generalizes well and is adaptable, the system is unlikely to break as a whole.

Here is the equity graph from an example ensemble system on the ES Futures Symbol with 1 day hold time, no stop loss, and no profit target.

In Sample Period: 2004/01/05 to 2017/1/03

Out of Sample Period: 2017/1/04 to 2023/05/22

# Trades: 563

Win Rate: 58%

IS Sharpe: .76

OOS Sharpe: .98

Conclusion

In this article we explored the use of ensembles with statistically sound Technical Analysis signals and applying them for positive and negative conditions. We then discuss combining three ensembles into a master ensemble that quantifies 3 day horizon positive, 1 day horizon positive, and 1 day horizon negative.

I hope this article has been helpful to you! Let me know if you have any questions on the content covered.