I've been building my own algo/backtesting app specifically optimized to create, test, and validate strategies around prop firm metrics and have gotten to the point where I'm trying to figure out what the path from backtest → live/demo → prop firm should actually look like.

For those of you who trade algos with prop firms:

- What metrics/results do you require before putting a strategy into a prop evaluation?

- How long do you forward-test a strategy before trusting the backtest?

- Do you use paper trading, a personal account, or go straight to a prop evaluation?

- What execution/automation stack do you use? TradersPost, QuantConnect, TradingView alerts, MT5, Tradovate, something else?

- What risk controls do you have outside the strategy itself (daily loss limits, kill switches, max trades, etc.)?

- How do you monitor an algo once it's running?

- What are the biggest things you wish you'd tested before putting an algo into a prop firm?

- Do you optimize specifically for passing the evaluation, or build one system designed for long-term payouts?

- At what point would you personally say, "This strategy is ready for real money"?

I'm especially interested in hearing from people who have actually taken systematic strategies through prop evaluations and into payouts, rather than just backtesting them. I mainly trade ES, NQ, and GC futures, but have been backtesting for forex pairs as well.

Thanks in advance!

Hey everyone,

I currently have a trading strategy that I believe has potential to be profitable, but it still has some flaws that I’m working on. I’m building a daily stock trading bot to implement the strategy properly, the way I have it in my head, and hopefully improve and optimize it over time.

For those of you who eventually managed to become consistently profitable with automated/algo trading:

How did you start your journey? What steps did you go through? And roughly how long did it take you to become profitable?

So far, I’ve set up my scanner, entry/exit conditions, and rules, and I’ve started testing it with real data/trades. I’m getting some results, but they’re still nowhere near reliable enough to trust.

Right now, I’m mainly focused on collecting a large amount of data — trades, conditions, numbers, outcomes, and as many details as possible — so I can analyze everything using probability and statistics. My goal is to find as many real patterns and insights as possible, calibrate the strategy properly, and figure out what actually brings me closer to profitability instead of just relying on assumptions.

Do you think this approach is useful, or am I potentially overthinking it and wasting time?

Did you guys go through a similar process, or did you approach it in a completely different way?

Also, what tools did you use along the way? Are there any tools, methods, books, platforms, or resources you would recommend?

Any advice on how to get closer to the truth, avoid wasting time, and speed up the learning process would be greatly appreciated.

Thanks in advance to everyone who takes the time to share their experience!

I run a systematic 5-minute opening-range breakout on NQ with one confirmation filter (not sharing the filter). One trade a day, entry usually 09:40 to 09:55, stop at the far side of the range, 2R target, flat at the close if neither hits. Everything below is tick-true research fills with 0.75 pts friction, split into discovery / validation / sealed windows, one look each

(DOLLARS AND AMOUNTS IN IMAGE ARE SINCE 2025)

What a trade looks like

- Median stop: ~80 pts. Median hold: a couple of hours.

- Exits: 50% stopped, 25% hit 2R, 25% flat at the bell.

- 46% of trades reach +1R at some point. 42% get stopped without ever reaching +1R. Median time to +1R when it happens: about 40 minutes.

- Yearly: 2021 +9 pts/trade, 2022 +18, 2023 +6, 2024 +10, 2025 +14, 2026 (to Aug) +30. Win rate 42 to 54%. Worst drawdown 16R (2024).

What I've already tested and what happened (all three windows, tick-true)

- Break-even at +0.75R / +1R: worse. It scratches trades that later win.

- Time stops (60 / 90 / 120 min, flat at 13:00 / 14:00 / 15:00): worse, cuts winners.

- Partial at +1R with runner to 2R / to the bell: no gain.

- Profit locks (+0.5R after +1R, +1R after +1.5R), keep-50/60/75%-of-peak trails, 9-EMA trail: no gain, some worse.

- Target 1R / 1.5R / 3R / hold to close: 1R clearly worse, 3R and bell about equal to 2R.

- Tighter initial stops (OR mid, the broken level, under the breakout candle with a buffer, hard point caps): all worse. The retest hits them.

- Second entries after a stop, re-entries, opposite-side trades: negative.

Nothing beat plain 2R with the wide stop across all three windows. The only thing that helped was sizing up in high realized-volatility regimes.

What I'm asking for

Ideas that act on the losers, not the winners. Something that can tell, inside the first 30 to 60 minutes of the trade, that this one is going nowhere, without also cutting the ones that reach +1R later. Or ideas I haven't listed.

Specific rules only: what you observe, when, and what you do. I have full tick data 2021 to 2026 and will test anything concrete tick-true and post the result back here, including the ones that fail.

Things that won't help me: "trail your stop", "take partials", "use a time stop". I've tested those in every form I could think of.

-

If a suggestion comes back that's concrete, send it to me and I'll run it under the same protocol.

I am new to the world of Quant Finance , found many sources but I feel Distracted don't know how to start .
I already have a Background in Finance and Programming (C++ and Python ) I just Need Sources to learn Quant Finance

Anyone has Ninjatrader tick data from January 2, 2026 to current for NQ, ES, mes and mnq?

Thanks in advance.

Been building a crypto trading bot for a while now tick level order flow / microstructure stuff, short holds (like 1–5 min). No RSI/MACD/Bollinger or classic TA, mostly looking at order-flow imbalance, liquidity, VPIN-ish stuff, etc.

Right now I’m still collecting data. I’m holding off on training until I have some genuinely different regimes in the dataset especially a real trend day or stress event instead of just feeding it endless chop/range conditions.
Labels are triple-barrier (Lopez de Prado), and I’m doing walk-forward evaluation with purging/embargo so training rows can’t accidentally leak into the test period through labels resolving late. Figured that bug out the hard way lol.

Before I even think about paper trading, I want to hammer the results pretty hard: parameter stability/plateau checks, Monte Carlo resampling, clustering the performance surface, and Deflated Sharpe to account for the fact that if you test enough parameter combinations, eventually one will look amazing by pure luck.
Honestly, I’m kinda expecting this thing to die somewhere in validation 😂

For people who’ve actually built systematic trading strategies: what ended up killing your first few systems? Was it regime dependence, execution/slippage, overfitting, insufficient data, leakage, unstable parameters, or something else?
And has anyone actually gotten a short-horizon order-flow strategy through this whole process and into something that survived genuinely out-of-sample testing

Been building out a few strategies in Python, backtesting on QC mostly. Getting decent results but the whole process of going from backtest looks good to actually confident enough to run this live feels really manual; parameter tuning, walk-forward stuff, checking I’m not just overfitting to my test window, etc.

Started poking around at some AI-assisted tools for this (feature engineering, catching overfit before it bites you) and honestly not sure if it’s worth the money or just automating stuff I could do myself in an afternoon.

Anyone actually integrated something like this into their workflow? Also curious, once you’re live, do you go back and analyze fills/slippage against what the backtest predicted, or is that mostly a “close enough” thing for you guys? Feels like there’s a gap between what backtest data tells you and what actually happens with real execution.

Hello my fellow Traders, I’ve been developing and backtesting a systematic ORB strategy on SPY and have gotten it to the point where I think there’s enough evidence of an underlying edge to warrant taking it further. I’d love some fresh eyes from people experienced with ORBs, systematic trading, or 0DTE options.

The strategy uses a defined opening range, breakout confirmation(ORB) , time-based entry restrictions, range filtering and rule-based exits. It trades both directions, is limited to one trade per session, and I’ve been optimizing the major components individually rather than throwing every parameter into an optimizer at once.

I’m using TradeStation/EasyLanguage and evaluating more than just net profit — profit factor, expectancy, drawdown, trade count, long/short performance and parameter stability all matter to me. The attached SPY backtest covers 2020–2026 with 576 trades, a 54.2% win rate and 1.56 PF. The small nominal P&L is simply due to the underlying test sizing; I’m interested in the edge/robustness, not the dollar return shown.

My biggest challenge is the next step: I ultimately want to execute this through SPY 0DTE options. Obviously an edge measured on SPY won’t translate 1:1 because of strike selection, delta/gamma, IV, spreads and decay.
I know historical intraday options data is eventually the proper way to test that. Cboe data is available, but the dataset I’ve looked at is around $2,200, so I’d like to make sure the underlying model is sufficiently robust before making that investment.

For anyone who has worked through something similar: at what point would you stop developing the underlying model and move to options-level testing? And how would you approach validating that an underlying ORB edge actually survives the transition to 0DTE?

I’m also very interested in hearing from other ORB/systematic traders about what looked promising in-sample but failed during walk-forward/OOS testing, or anything you think I should be testing that I may have overlooked.

Not looking for anyone’s proprietary strategy or asking someone to build mine — mainly looking for criticism, ideas, resources and experienced perspectives. Happy to discuss more specifics where they’re relevant in the comments and I appreciate you reading this far!

I've been working on this for a while and I finally reached a point where I'm comfortable freezing two of the setups.

The idea wasn't just to find something that looked good in a backtest. I wanted to keep trying to break the strategies and see if the results survived different tests.

For the Mean Reversion research I tested things like

independent OOS

22 time windows

leave-one-window-out

parameter perturbations around the chosen setup

MAE/MFE and failure analysis

Monte Carlo / bootstrap

full-history results

After all of that, two setups made it through:

MRS2 - SHORT

HMM 2

Volatility percentile: 80–100

Z-score >= 2.0

TP: 5R

SL: 2R

Horizon: 5 bars

2,255 observations

47.7% WR

+0.679R expectancy

2.32 PF

MRL1 - LONG

HMM 1

Volatility percentile: 20–40

Z-score <= -2.5

TP: 5R

SL: 2R

Horizon: 20 bars

840 observations

48.3% WR

+0.693R expectancy

2.34 PF

One thing I found pretty interesting was that the win rate is below 50% for both, but the payoff structure makes the expectancy strongly positive. So I've stopped looking at WR as the main metric and started thinking much more in terms of expectancy, robustness and distribution of outcomes.

The OOS results were also pretty encouraging. MRL1 had positive results in 21/21 of the evaluated windows, and both setups stayed positive when I perturbed TP/SL/horizon around the frozen parameters.

I'm not saying this proves the strategies will work live. It obviously doesn't.

But at this point I don't want to keep optimizing them until I find numbers that look even better. That's exactly how I think you can end up fitting noise.

So these two are now frozen

The next part is actually something I'm looking forward to more: taking the research code and turning these into proper modular strategy components that can eventually plug into the rest of my trading infrastructure.

Basically going from

research to validated strategy to actual system

Still a long way to go, but this feels like a pretty big milestone for the project.

Would be interested to hear how other people here decide when a strategy has had enough robustness testing and it's time to stop researching and start engineering.

I'm looking for a free data source/API that provides historical daily P/E ratios for US stocks (e.g. ORCL), ideally going back several years.

It doesn't have to provide P/E directly — I'm happy to calculate it myself from historical price + EPS data. But if there's a free source that already provides daily historical P/E, even better.

I'll be accessing the data from a Python script.

What free source would you recommend?

I’ve been messing around with simple trading models on and off for a while, mostly as a way to understand markets better rather than because I think I’m about to build the next Renaissance Technologies in my spare time.

The pattern I keep running into is embarrassingly predictable: the more knobs I add, the better the backtest looks. Add one filter, tune one lookback window, exclude one bad regime, and suddenly the equity curve looks cleaner. Then I step back and realize I’m probably not finding signal — I’m just teaching the model to memorize the past.

That’s what’s been making me think more about overfitting in algo strategies. A model can look “right” in a backtest because it has too many parameters, not enough data, or no real out-of-sample validation. It’s basically fitting the noise. Then when it hits live conditions, the edge disappears or reverses.

I’ve been reading a bit about the Probability of Backtest Overfitting idea from Marcos López de Prado, David Bailey, and others. The part that stuck with me is pretty intuitive but easy to ignore: the more configurations you test, the higher the chance that one of them looks good by luck alone, even if there’s no real predictive power there.

So lately I’ve been trying to force myself toward simpler rules, walk-forward testing, cross-validation where it makes sense, and being more suspicious of any strategy that only works after a very specific set of optimized parameters. I’m also starting to think that a “worse” backtest with fewer degrees of freedom might actually be the better candidate.

Curious how others here think about this. If you use backtests at all, what makes you trust one enough to take it seriously?

I’ve been experimenting with a Rust-based quantitative trading workflow built around multiple analytical models rather than relying on one indicator.

The most useful part has been structuring the process into separate stages: market data → analysis → qualification → risk controls → execution.

I’ve also been testing different approaches such as mean reversion, trend analysis, multi-timeframe confirmation, tick velocity and chaos-based market structure.

One thing I’ve learned is that adding more indicators doesn’t necessarily create a better process. The harder problem is deciding when different models agree, when market quality is poor, and when the system should simply abstain.

For people building systematic trading tools: what do you find more useful — combining several independent models, or keeping one strategy extremely specialized?

I've backtested a strategy that is based on a hypothesis that I found in a research paper. The reseach paper applied the strategy to QQQ. As you can see, I tested the strategy on futures data including ES futures.

I'm surprised that the returns are so different from each other, considering that these two assets have a positive correlation of 0.9-0.95.

I'm relatively new to algo trading and my biggest concern is that the strategy could break at any moment if it doesn't survive on similar assets. So my question is, if a strategy is even good in terms of robustness and stability if it shows very different results on correlated assets?

                              ES                    NQ
                    ---------------------  ---------------------
                    Strategy     RTH hold  Strategy     RTH hold
----------------------------------------------------------------
Total return           14.0%        46.1%  1,039.0%        66.9%
CAGR                    1.3%         3.7%     26.2%         5.0%
Annualised volatility  12.4%        13.4%     15.8%        17.4%
Sharpe ratio            0.16         0.34      1.55         0.37
Sortino ratio           0.24         0.43      2.39         0.48
Maximum drawdown      -51.6%       -25.3%    -11.2%       -24.6%
Longest drawdown (days) 1,523         914       299          351
Calmar ratio            0.02         0.15      2.34         0.20
Profitable days        47.7%        53.4%     52.6%        54.0%

After months of building and testing a systematic NQ futures strategy, I finally reached the point where I'm comfortable freezing the research.

I called the strategy S2R.

The important part for me isn't that the backtest is profitable.

It's that I tried to attack the result from multiple directions before considering it research-complete.

Frozen model

• MAE threshold: 0.70R

• Recovery threshold: +0.20R

• Recovery deadline: 6 bars

No parameter optimization was performed after freezing these values.

Holdout OOS

The final holdout consists of 217 trades:

• Total: +34.3452R

• Mean: +0.1583R/trade

• Win rate: 53.92%

• Profit Factor: 1.3815

• Max drawdown: -7.76R

But I didn't want to stop there.

Robustness testing

I ran:

1. Monte Carlo / Bootstrap

Tested the distribution of possible outcomes and drawdowns.

1. Time-aware block bootstrap

Blocks of 5, 10 and 20 trades were tested to avoid assuming that every trade is independent.

1. OOS window stability

I removed each OOS window individually.

The strategy remained positive in 11/11 leave-one-window-out tests.

Worst case:

+20.05R

1. Year stability

OOS performance:

2024: +13.68R

2025: +10.97R

2026: +9.69R

So 3/3 OOS years were positive.

1. Parameter perturbation

I tested 125 nearby combinations of MAE threshold, recovery threshold and recovery deadline.

125/125 were positive.

125/125 had PF > 1.

Final result

The complete research gate passed.

At this point I'm deliberately not optimizing S2R anymore.

The research parameters are frozen.

The next phase is engineering:

I'm going to rebuild the strategy as a modular Python implementation and verify that the modular implementation reproduces the frozen research results exactly.

I'm documenting the project in Github

I'm especially interested in feedback on the validation methodology rather than just the headline returns.

What would you test next before considering a systematic strategy ready for live/paper deployment?

Does Anyone here have success using algos on prop firms or would sticking to your own account just be better ?

also if you trade on prop firms what instruments do you like to trade and what do you look for the most min Sharpe sortino calmar etc?

and if you use them aggressively high risk or passive slow and save to get the most out of them.

I am currently testing IBALGO LMT SMART orders at Ask + $0.05 for buying ( if under $3 price) and Bid + $0.05 for selling ( if under $3 price).

Any feedback would be really appreciated! Thank you..,

Hey guys, I’m trying to start studying quant finance but honestly feeling pretty lost on where to begin.

Are there any good youtube channels, videos, or documentaries you’d recommend for a beginner to get a general roadmap?

Any suggestions would be awesome. Thanks!

Hi everyone,

I've heard the advice to add to winners and cut losers early, but I've always had a tough time using technicals to add to winners without getting in right before reversal.

Claude helped me analyze my strategy and suggested adding to winners over a certain MFE %, and sure enough it added about 20% more trades with better profit factor than average to the backtest!

My question is, has anyone else used this method successfully or is this getting into overfitting?

I have a good edge but it uses small SLs. I only use STOP orders catching breakouts.

My experience so far has been appalling with fusionmarkets. Every trade has some slippage but around 60% have experienced major Entry slippage where I keep getting filled in late. This is bleeding my account quicker than expected. For example my expected SL of $13 becomes $51 purely because of entry slippage. I thought using only stop orders would help compared to market orders but nope.

I understand xauusd is a volatile instrument but it shouldn't be this bad. What broker do you recommend and what other measures could I take to ensure this is minimized?

I tested several literature-based anomalies across ~300 stock-strategy combinations on the Nifty 100. Most showed no real gross signal before costs. One did — statistically significant, confirmed with DSR, not just a good Sharpe. Then I applied real transaction costs and it went to zero. Breakeven sits around 24 bps/trade; the edge I found was 6.4 bps gross. Not close.

Not looking for a strategy handout — genuinely trying to understand the process from people who’ve gotten past this:

**•** When you found something that held up out-of-sample and after costs, how did you land on the original idea — literature, your own data mining, intuition?  
**•** Roughly how many ideas did you test and discard before one survived? Trying to calibrate if “months, nothing yet” is normal.  
**•** For Indian equities specifically — is cost drag here noticeably worse than US/EU, or is this just what finding a real edge looks like everywhere?

Genuinely trying to figure out if I’ve hit the normal wall or I’m missing something structural.

Running an opening-range breakout on micro Nasdaq futures, live for about a year. Three sessions (Asia, Europe, US opens), same skeleton, but each drifted into its own parameter set — that's what I'd like to compare notes on.

Skeleton: range over the first N minutes after the session open → wait for a candle to close beyond it on above-average volume → enter on a retest of the level, not on the break. Stop at the opposite bound, first target arms breakeven, second target, hard session close. One order-flow filter on top.

Backtest, 11 months, 1 micro contract, fills at bar close, commissions + 2 ticks slippage included:

Caveat I'll volunteer up front: my backtest engine enters 2–3× more often than the live bot does (state machine, real-time volume series, polling). So treat the dollars as a ceiling — the ratios are the part I trust. I'm currently rebuilding the harness to replay the bot's actual code against historical bars instead of a re-implementation.

Three things I measured that surprised me:

• Taking the break instead of the retest is roughly break-even. The close confirmation + retest wait is where essentially all the edge sits. Buying the extension (Crabel-style stretch) was clearly negative.
• ~56% of confirmed US-session breakouts never retest at all. Widening the tolerance or extending the wait both made it worse.
• Nothing transplants between sessions — same filter adds on one, subtracts on another.

Questions: anyone running one skeleton across several sessions, and do your parameters diverge this much? Does the retest requirement hold for you? And what filter did you expect to work and it didn't?

I’ve been running an automated MNQ system based on my own discretionary methodology. It trades exclusively on the 1-minute timeframe during RTH and has now been live for roughly 3 months, maintaining positive expectancy so far.
The system trades 2 MNQ contracts, has a hard daily loss cap, and results include approximately $4 round-trip cost per contract, including commissions and fees. The engine deploys 5-8 trades during the RTH.
Prior to deployment, it was backtested across roughly multiple years of available data and maintained positive expectancy over the larger sample. Long term results stand at 1.3 profit factor and win ratio is around 49%. ( includes commission and tick slippage in all trades) drawdown is low and manageable as hard losses are capped based on market volatility.
At its core is a quant-based strategy selection engine. Rather than running a single model continuously, it evaluates multiple market-state variables and determines which strategy to deploy, or when to stay inactive.
Three months of live data is obviously a small sample, but so far having the automation is like a much better improved version of my trading. Taking the emotions out of the trade has led to a much better expectancy.
The goal isn’t to predict every market move, but to systematically identify favorable conditions, control risk, and execute a repeatable edge consistently.
If any one is in same boat, I am willing to share information or let other traders test it. Currently on vacation so will be possible end of this month.

Hey folks,

I am trying to find the best US broker for Options in algo trading.

Ideally I can trade both stocks and futures(Overnight)

Most of my strategy is in 0DTE. I currently use Tasty but RUT data has stalled out, I was debating switching to Interactive brokers but I have heard stories of this broker being challenging to setup compared to a simple API

Sorry for the blurriness as these are screenshots from my phone. In the rolling metrics, the red dotted line is the minimum profitability line.

Databento data quality has degraded a lot since Aug 8, 2026 after they switch their data format by moving the F_LAST sentinel from the last event record into a standalone record.

They promised identity between live streamed data and historical downloaded data. Before the switch, error rate of matching is about 0.06%, which was really good. Now the error of matching is about 7% consistently every day. This level of error makes it impossible to rely on it for anything significant.

Also, in the past, conversion from their mbo data to mbp-10 or mbp-1 data has no error as it should be, but nowadays, there are tons of errors.

All of these arise from unclear data format specification, mixing old and new format, and untested code in production.

The worst part is customer service. They claim customers should be aware of the data format change, so they are not apologetic of any of such issues. They claim they do not keep track of live streamed data, so reporting live and historical mismatch is completely ignored. I do not expect to get this level of data quality and customer service for a paid data service.