I'm a CS student and have been pretty interested in algo trading and quantitative finance, particularly HFT. I'm also interested in ML methods of trading (esp. RL, I do research in that). But, as a college student I don't really have a ton of money to throw at trading.

I want to start deploying algos that use real capital, but I can't afford to put my entire life savings into it, so I'm trying to figure out if its realistic to expect any substantial returns with under $100.

From what I understand, HFT has some pretty significant barriers to entry, esp. when it comes to infrastructure, latency, data, and competition from trading firms. Even outside HFT, it seems difficult to develop profitable strategies with such a small amount of capital once you account for fees, spreads, and other costs.

I've tried trading small amounts in Polymarket + Kalshi, but it feels like I'm competing against people with far more capital, better infra, and more sophisticated models. I'm also based in the US, so I can't trade on any lower liquidity prediction markets (at least none I have found).

I'm particularly interested in exploring whether I could use ml, statistical modeling, or other computational approaches to identify trading opportunities. I'm willing to put in time to learn and build systems, strategies, and experiment, but ideally I'd like to work toward something that could be consistently profitable rather than treating it purely as academic exercise.

Main questions:

1. Is there any realistic path to profitable algo trading starting with <$100?
   

2. Is HFT something an independent developer can realistically break into, or is it effectively inaccessible without institutional resources?

3. Same as 2, but for AI/ML methods, given firms have much more data and compute.

4. If yes to 1, what markets/strategies someone with limited capital could have a chance?

5. Would trading tiny amounts be better than paper trading with larger amounts?

6. If it isn't viable to trade <100, what is the minimum amount of capital I should have (and be willing to risk) to start trading?

To be clear, not expecting to make a fortune off of $100. More interested on if there is a realistic way to start this small or if I should hold off on live trading.

Any advice is greatly appreciated!

Hey guys,

Just want to check, what platforms do you recommend to use to test my algo specifically for paper trading?

I know there's alpaca.

I'm not sure IBKR allow for API access for paper trading (haven't check yet)

Is there anything else or anything you recommend?

Preferably something free

Hi all, I've been researching signal combinations and strategies for the past year, back testing / running Monte Carlo simulation with 1-minute bar data from 2010-2026 and seemed to have finally stumbled on a method using MNQ that produced around 53.5% average CAGR. Compounded growth shown with 50K starting capital ending at around 53M. Recent daily activity, monthly results, stats and comparison against baseline QQQ shown for reference.

For context: the 53% / $53M is compounded, pre-tax, and runs about 3.5x leverage on the futures. Without compounding, 1 MNQ contract, Jul 2010 to Oct 2026, $3 round-trip fees: +$85K total, Sharpe 1.15, max drawdown $7.8K, 15 of 17 years positive, near-zero correlation with QQQ. 2010-15 was data I never used while building it, and it still worked, about half as well.

Starting live tests beginning next week. Feel free to ask questions if you want more details. Will post meaningful updates when possible. If anyone has feedback on how they conducted live tests or methods of further proving out promising back tests please share. Thanks!

I'm making a bot for trading, and based on market conditions, it decides how much I need to short vs. normal trading. Ex:(20%/80%). I have an idea of what values I need, but I don't know what is necessary or how to use them practically to decide the ratio of shorting to normal trading.

Every value will be weighted based on how practical it is. (Don't worry about this because I will run many variations in a simulation to decide what is best) All help is appreciated, as I have tried everything and have just about given up. (Even if it is unrelated)

Hi,

I'm testing short-dated credit spreads & iron condors on index ETFs in my trading system.

With simple pricing assumptions, the backtests looked good.
But every time I made the pricing more realistic, the edge got smaller.

Real commissions per leg, actual implied volatility instead of estimates, and skew across strikes instead of one flat volatility.

Now I can't tell if there's still a real edge or if I'm just looking at noise or if it's just luck..

For those who've been down this road...

Is short-dated premium selling on index ETFs, worth pursuing, or are costs and execution eating most of the edge??????

How do you test these strategies properly? Do I need historical options quotes, or is there a reliable way to model pricing without them?

If you decided its not worth pursuing, what did you move on to instead?

Not looking for signals, just interested in how people with experience approach this and decide whether there's actually something worth pursuing or if it's time to move on.

THNX

Found a crazy money printing ea and I’m testing customised files on my demo account which works pretty well.
I’m feeding my live account every month now with very little risk and getting around 10-30% win a month now with very little risk.

Alright so I have a strategy that works well in MR and also the inverse works well on trending. I'm working on a mode where it switches between them.

Ideas so far

Vol regime

Adx

Slope

Ultimately it's a regime detection problem but the ideal version would be responsive enough to catch the macro regime flips reliably

Since it may not need to catch low tf regime changes it should be relatively simple

The vol regime I've used before looks to be a little too restrictive when scaled up to the macro tf

What have you used to detect macro regime to flip between MR and trend followers?

Any ideas are appreciated

I ran one breakout completely unchanged on NQ, ES and gold a few weeks ago, 15 minute candles from January 2021 to September 2026, 100k account, one contract each. It came out up 20% on NQ and down 54% on gold. A commenter pointed out the thing I should have caught myself, which is that one contract is a different amount of risk on every market.

A full NQ contract moves $20 per index point, ES moves $50 per point, and gold moves $100 for every dollar the price moves. A stop that sits a sensible distance away on each chart turns into a different dollar amount on each one, so the three legs of that test were never taking the same risk per trade.

That means some of the gap between plus 20 and minus 54 could just be sizing, and a fixed contract test cant tell you how much. The cleaner version sizes every trade so the stop costs the same dollars on all three markets and only then compares them.

Its like comparing two delivery drivers on fuel cost when one drove a pickup and the other drove a hatchback. The route matters and so does the vehicle, and you have to even out the vehicle before the route tells you anything.

For anybody running this on an eval the number to look at changes as well. A trailing drawdown doesnt care where the account finished, it cares about the first day it touched the loss limit, so that date belongs beside the final return in any comparison like this.

Before anything I am very new to trading bots and using claude to create bots ,

I have seen a lot of posts in here where people have created bots and been testing for many months and even years.

I have created few bots that are running on my laptop and have been doing trades for few weeks ?

What kind of progress would you personally like to see on demo account for you to put actual money in it ?

The best bots i got so far has got 100% win rate which i started last week only few trades which could just be luck..

2nd best is on 67% win rate started 2 weeks ago.

I am planning on letting them run for atleast 3 months and see how it goes.. what do u guys think..

Hi everyone,

I'm currently learning algorithmic trading, and I'm a bit confused about which approach I should use when building a backtesting engine: event-driven or vectorized backtesting.

I have around three years of minute-level historical data stored in my database. I want to analyze and backtest different trading strategies using this data.

Here's what I'm thinking:

1. Strategy research and backtesting: Use a vectorized approach with libraries like NumPy, Pandas, and TA-Lib to analyze historical data efficiently. My concern is that processing such a large dataset using an event-driven approach could be relatively slow.
2. Live trading: Once I've analyzed a strategy and am satisfied with the results, switch to an event-driven approach for live trading and tick-level data processing.

However, I'm unsure about one thing. If I finish analyzing the historical data up to a particular date and then move to live trading, should the live engine continue processing from that point onward, or is there something important I'm missing in this workflow?

My questions are:

• Is this a good approach, or am I making a mistake in the design?
• Are there any other considerations I should keep in mind when designing the backtesting engine?

I'd appreciate any suggestions or insights from people who have experience building algorithmic trading systems.

Thanks in advance!

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Edited

Thanks for the all the responses i will go with the hybride architecture where i will use both approaches !!!

Hey everyone!
I’ve been running my own algorithmic trading strategy for approximately 1 year, and I’d love some feedback from experienced algo traders, quant developers, and people who have scaled automated trading systems.

Over the past year, the algo has generated approximately 2.5 million cents in trading profit on a cent-denominated account with Vantage Markets. I’ve attached a screenshot of the MT5 account history.

My current setup
● Broker: Vantage Markets
● Account: Cent account
● Instrument: XAUUSD (Gold)
● Execution: MT5 Expert Advisor (EA)
● Infrastructure: VPS
● Trading period: 1 year
● Reported trading profit: 2.5M account cents

The system is already running live, and I’m now exploring what it would take to scale it substantially potentially 100x the current trading size.
I’m trying to understand what actually needs to change when moving from a retail algo setup to something capable of handling significantly more capital. Is the current architecture sufficient, or would I eventually need a completely different execution infrastructure?
Questions for those who have experience scaling algos

1. Strategy scalability
   How do you determine whether a profitable strategy can maintain its edge at 10x or 100x the position size? What tests would you run before committing more capital?

2. Infrastructure and architecture
   I’m currently running an MT5 EA on a VPS. Can this setup handle substantially larger trading sizes if the broker and execution conditions allow it? At what point would you consider a separate execution engine, Python/C++ services, or a dedicated risk-management and order-management system?

3. Broker and execution limits
   For XAUUSD, how would you evaluate Vantage’s execution quality, liquidity, slippage, order limits, and spread widening as position sizes increase? Are there specific things I should test with the broker before scaling?

4. Risk management
   What safeguards would you add before scaling significantly? For example, independent exposure limits, maximum drawdown controls, daily loss limits, execution monitoring, and automatic shutdown mechanisms.

5. How would you scale it?
   If you had a working MT5 EA with a year of live trading history, what would your next steps be? Would you gradually increase position sizes, conduct forward tests at each stage, or build additional infrastructure first?

6. What becomes the bottleneck at scale?
   For those who have actually scaled automated strategies, what tends to break first: the strategy’s edge, execution quality, broker limitations, infrastructure, or risk management?

I’m not looking for a shortcut to multiply profits overnight. I want to understand how to scale this properly without destroying the existing edge or taking on risks I haven’t accounted for.
I’d especially appreciate advice from people who have moved beyond small retail setups and have experience operating automated strategies with meaningful capital.

Happy to share additional details about the strategy, drawdowns, trade frequency, and performance statistics if that would help.

Thanks!

Hi
I'm trying to understand something and would like opinions from people who actually know the statistics behind this.

If a DSR calculation assumes a standard deviation of Sharpe ratios of 0.5, but your measured data suggests 0.2, should you use the measured value instead?

Could the higher assumption be rejecting genuine trading edges, or would lowering it risk letting strategies that only got lucky pass?

I don't want to make the test easier just to get more strategies through. I want to know what the statistically correct approach is.

What would you do, and why?

I have been working on the ORB strategy EA for XAUUSD to win prop firm challenges, need opinion does the strategy and ea will work or should I give up as far now the backtest are not promising. Anyone build successful ORB profitable EA

instead of finding a algorithm which works why not copy someone who already figured it out?

curious to hear why this is a bad idea, hard to execute?

for example the hyperliquid leaderboards are their for anyone to view and analyze

Most of us trade with some version of the same rule: take the 15-min setup only if the bigger picture agrees. The 60-min trend is up, or the stock’s sector index is above its 20 SMA, or NIFTY closed green yesterday.

It’s a sensible filter. But it’s surprisingly easy to backtest it wrong.

The trap: peeking at an unfinished bar

Say your chart is 15 min and your filter is “60-min close above its 20 SMA”. At 10:15, the 60-min bar covering 09:15–10:15 is just finishing. At 09:45, it’s only half done.

Many backtests quietly use that bar’s final close at 09:45, which you couldn’t have known at 09:45. The result is a strategy that looks great on paper and falls apart live, because in the backtest it was reading the future.

How it should work

• Data1 = your trading chart (e.g. 15 min)
• Data2 = same stock on a higher timeframe (60 min), or an index/sector index
• Data3 = a third layer (e.g. NIFTY Daily as a market filter)
• Rule: at any Data1 bar, the strategy only sees Data2/Data3 bars that have finished. The daily bar becomes available on the day’s last intraday bar, not at the open.

It also helps to label bars by their close time (like TradeStation). The 09:15–09:30 bar reads 09:30, and the last bar of every timeframe reads 15:30. Then Data1, Data2 and Data3 line up visually, and you can check exactly what your strategy “knew” at each bar.

A simple example to test yourself

• BUY when EMA 9 crosses above EMA 21 on the 15-min chart
• …AND the 60-min close is above its 20 SMA (finished bars only)
• SELL is the mirror image

Compare it with the plain EMA crossover. Sometimes the filter cuts the losers, sometimes it just cuts trades. That’s exactly what backtesting is for.

Where I’m going with this

I’ve been building this into RN TradeLab, the backtesting tool I work on. v0.24 is coming soon with:

• Data2/Data3 panels on the chart: same symbol on a higher timeframe, or any index/stock
• Strategies that can use Data2/Data3 directly, with a built-in check that they only see finished bars
• Close-time bar labels, so everything aligns across timeframes
• New candle styles (hollow, OHLC bars, Heikin Ashi, volume candles)

Not financial advice. This is about testing ideas properly before risking money on them. Happy to answer questions about multi-timeframe backtesting in the comments, even if you use a different tool.

I’ll keep posting what I learn while building this. Follow me if you’d like the updates and the v0.24 release.

Out of curiosity, Has anyone successfully turned a profitable strategy on the strategy tester into a profitable bot system?
I don't tend to use it but I see a a lot of hate on it and was just curious if anyone has been able to closely replicate its results in a live hands free setting. it does seem like it could be useful for certain things.
Slippage and commission can be factored into this tester which can help make results more realistic. And I know it can be easy to overfit with this tester but genuinely just curious on peoples experiences when attempting this.

I’m trying to optimize my algorithm but for using options, not shares. with options, there’s just so many choices and variablea. DTE, Strike are the obviously two, but how do you algorithmically choose that?

im particularly asking for day trading. For example, if im trading on the 5 minute candles, I wouldn’t choose 0 DTE, obviously. But do I pick 1 week out? monthly contracts? Do I go OTM, ITM, or ATM? If ITM, then how deep ITM?

can someone give me some guidance or at least some pointers to research about?

Does anyone have clean, high-quality historical daily QQQ constituent weighting data covering at least the last 12 months that they'd be willing to share?

I haven't found a high-quality dataset at an affordable price. Getting NDX weightings directly from Nasdaq comes with a nosebleed price tag. Thanks!

Lots of people go ahead and point out 'as much as possible' 'as much as you can really' 'at least (some amount of years that is gigantic)'. But what does the answer turn to when you consider an algorithm that trades across much shorter distances and scalping algorithms that go in and out of trades after 5 minutes? Depending on frequency, too. I would assume its important to test out all possible markets, crashes, etc... If youre trading so quickly and at such a small length of time, market conditions that show results after a full day essentially wont impact you at all, or so i think? Im not too experienced so any advice is much appreciated.

(and no i dont mind downloading years of data i just want to know if at some point it becomes redundant)

I took 156 markets listed on both Kalshi and Polymarket with the same settlement rules, and priced a 1,000-contract buy three ways against the live order books, fees included.

Extra cost per $1,000, compared with routing across both:

• Kalshi only: +$24.51
• Polymarket only: +$6.80
• Always the cheaper venue: +$0.69

Where routing really pays is size on thin books. 10,000 YES on Inter Miami to win the MLS Cup cost $2,173.52 routed, vs $3,297.77 on Kalshi alone and $3,310.31 on Polymarket alone. On its own, each venue’s book runs out and the price climbs. Split across both, it stays low.

So I built a smart order router that does this automatically. It reads both order books, adds each venue’s fee to every price level, and fills your order from the cheapest price first, whichever venue it’s on.

The smart order router is available here: github.com/Synpath-ai/synpath

Disclaimer: Not financial advice. Educational purposes only.

Every retail trader I know has been told not to hold over the weekend. I ran the same rules twice across 26 years of SPY to see what that instruction actually costs, and the gap is far bigger than I expected it to be.

The rules are deliberately boring, because the only thing being measured here is the exit. Buy SPY at the close after two down closes in a row, hold five sessions, exit at the close. $10k account, January 2000 to March 2026, commission and slippage charged on both sides of every fill.

Held through the weekend: 605 trades, 56.9% wins, profit factor 1.42, +282.9%, max drawdown 34.6%

Flat at every Friday close: 727 trades, 53.1% wins, profit factor 1.05, +20.6%, max drawdown 49.3%

Same entries on the same days, and the only difference is that the second version goes home flat on Friday and buys back in when the signal returns. It handed back 93% of the return and the profit factor came in at 1.05, which is close enough to 1.00 that youre working for your broker.

The part I did not expect at all is the drawdown. Going flat for the weekend is supposed to be the cautious choice, and it finished with a worse peak to trough, 49.3% against 34.6%, because selling Friday repeatedly took it out of the bounce and had it buying back higher on the Monday.

____

Theres an obvious objection here and id raise it myself if this was somebody elses post. Flattening on Friday also means holding for fewer sessions on average, so the shorter hold could be doing the work here rather than the weekend.

So I ran a control with the exit moved to the Wednesday close, same entries and same costs: 739 trades, 55.1% wins, profit factor 1.29, +170.7%, drawdown 28.7%. Chopping the hold short midweek costs about a third of the return, and chopping it short on Friday costs nearly all of it, so the weekend itself is where the money goes.

Two things worth being honest about. This is one mean reversion entry on one index ETF and its long only, so a good chunk of what the Friday exit gives up is simply SPY drifting upward, and a lot of that drift lands between the Friday close and the Monday open. A short strategy, or a single name carrying real weekend headline risk, could very plausibly come out the other way. The SPY history in Agenticks runs from 2000, so i used the whole thing instead of choosing a window that suited the answer.

The cheapest version of this test for anybody holding swings is to take your own last two years of trades, cut every one of them at the Friday close, and total it up again.

Mother Superior has been running live for roughly two weeks. It is a short-term XAUUSD scalper/intraday EA built from eight separate modules, with an integrated recovery layer. The first nine traded days are currently at +12.22%.

It has already traded through a few red-folder news events and behaved well so far. Still, two weeks is two weeks. The first genuinely ugly market regime will tell me far more

than a green opening run, so we’ll see what happens.

I’ve spent roughly three months building the system. Each module has its own entry logic, Magic Number and basket management. The shared risk layer lets me limit simultaneous positions and includes optional equity protection.

Only 2022–2024 was used for development and parameter selection. The final system was also challenged on the history outside that window:

• 2017–2021: earlier untouched survival/stress history

• 2022–2024: development and parameter selection

• 2025–2026: formal frozen-parameter OOS

That gives seven calendar years outside the development window. I keep the older survival/stress years separate from the formally declared OOS period, but the same final structure survived both sides of the development sample.

The main continuous 2021–2026 real-tick report used a fixed 0.01 first lot with no compounding. Starting from $1,000, realised balance finished at $3,903.12 across 1,511 closed trades. Profit factor was 2.93 and every tested calendar year was positive. The number I care about most is the observed MT5 maximum relative equity drawdown: 12.82%.

For the Monte Carlo check I resampled the complete deal history 10,000 times in 50-deal blocks. This keeps some local clustering instead of pretending every trade is independent. Median ending balance was $3,896; the 5th–95th percentile range was $3,504–$4,318. Median maximum realised-balance drawdown was 4.1%, with an 8.0% 95th percentile.

The Monte Carlo only rearranges realised trades. It cannot reconstruct every floating basket or invent a gold regime that never appeared in the source data. That is why I still treat the observed 12.82% MT5 equity drawdown as the more relevant risk figure.

How much live history would you personally require before giving meaningful weight to the untouched tests and bootstrap results?

I work alone and rarely get to share the mini achievements that each day's dev brings, I find this a real shame.

So I am curious what are you building today? Or what was the latest thing you built which you're proud of that no one got to hear about?

For me, today im working on improving my display/GUI for certain features to make live monitoring easier.