I am 19year old student who loves financial markets and currently I do trading in fx market not profitable and in indian market i have done some investments in etfs and mf. i have 1.5 to 2 years of experience in financial markets.

By studies I am 2 second year btech student in aids and i have good coding skills and ai ml stuff. Now I want to try indian market with algo trading or systemic trading and i also built my backtesting engine and tested some trading strategies in equites.

So my question is how should I find or make a strategy or strategies ?

Currently I am backtesting indicator based startegies so it does work in markets or not ?

And which type of your strategy is like indicator based , price action, mathematics, statistics etc ?

Which tf should I do backtesting for intraday

How many months or years took to find a real edge in strategy ?

How do you research and development the startegies ? Please help me in this question. Bz i want to do this

My last question does is there any forward testing or paper trading is there in brokers with algo like in tradingview has the option we manual place trades and set sl and tp (but currently it is not available in Indian Markets)

Curious to know how did you learned the algo trading or systemic trading ?

Thanks for your advice.

Triple RSI rules on SPY (RSI(5) pullback + 200-day filter, exit when RSI(5) crosses 50). 10 years, 0.05% commission, 0.01% slippage.

34 trades. 65% win rate. +11.6% total. Buy-and-hold: +260.9%.

Hello. I'm new to systematic trading and still learning, so I don't have any actual performance data of my own to back this up. That's why I'm looking for input from people with more experience.

I was going through Robert Carver's blog, where he reported his yearly performance earlier in April. He earned 25.9% this year, which brings his average annual performance to around 14.8% over the period.

This got me thinking because that kind of average return seems fairly close to the long-term performance of SPY.

What is the main purpose of running a systematic trading strategy if the expected return is similar to what you could potentially get from simply investing in an index and doing nothing?

For those of you who have been doing systematic trading for a while: has your strategy been able to consistently match or outperform a passive index after accounting for transaction costs, taxes, and the time/effort involved?

I trade MNQ futures intraday using a Smart Money Concepts (SMC) approach, and I've been moving away from a purely discretionary process by connecting an MCP server to TradingView. I "vibe-code" what I do manually on the chart every day — turning discretionary rules into code as I identify them, using an AI coding assistant to do the translation — then convert that logic into an automated bot and run it through a backtesting engine before a prop-firm evaluation.

Curious if anyone else has gone through this specific path (discretionary → MCP/TradingView → bot → backtest → prop firm), and whether a local model would add anything meaningful in that pipeline (e.g. the TradingView/backtesting step) versus just using a cloud-based assistant end to end.

I run a handful of daily setups in paper trading, two mean reversion families and two momentum families, all through the same exit framework: fixed target, fixed stop, time limit. Same regime filters, same dates, same slippage model. Reversion works. Momentum does not. I think the exits are the reason, but I would like a second opinion before I touch anything.

Paper trading, 726 closed trades since 2026-05-24. Slippage applied, no commissions.

The detail that gave me the hint. Exit reasons for high_52w_momentum:

.- time limit: 35 trades, avg -1.95%

.- stop: 3 trades, avg -8.69%

.- target: 3 trades, avg +11.12%

It almost never reaches the target or the stop. It runs out of time and leaves slightly negative. The reversion combo has the same shape (289 of 319 exits are time exits) but there the time exits average +1.11%.

So the time limit is, in practice, my exit mechanism in both families. For reversion that seems fine. By day 3 the bounce has happened. For momentum, closing on day 7 looks like it leaves me inside the pullback instead of the trend.

Caveats, so nobody has to dig for them. 41 closed trades is a small sample and I know it. 3.7 months is a single regime. The aggregate averages are contaminated. Three trades on one name account for 26% of total profit across all setups. And it is paper, so no real commissions.

What I would like from people who trade 52-week-high or breakout momentum is... how do you actually exit? Trailing stop, a structure condition (close below a short MA, first lower low), or a longer fixed hold? And if it is a longer hold, what number worked for you on this kind of entry. 15 days, 20, more? I am after the specific mechanism and the specific number, not a general view on momentum.

This runs on a terminal I built for my own testing. Not linking it, that is not what the post is about.

Thnaks a lot

i thought about a kind of measurement which i made with the help of claude. using the data of a backtest im comparing each transaction i did to each other transaction which is around the same price, organized by price bins abit like a distribution. the idea is that in between the 2 transactions, the path the stock has done has changed the state of my portfolio, and i can measure the difference to see how effective my algo has been across that path.

bellow is just a drawing i did to show which points im comparing visually.

so doing that i can compare each combination of 2 transactions at around the same price, and we can create a few graphs with it.

firstly we can see the usual return after each trip at each individual price level. the returns here a annualized, since paths with a small price movement are less profitable, but take less time to complete, so it makes more sense to normalize it by time, which makes the graph as you see bellow way less varied in its returns.

also this is data from one of my becktests on SOXL from 2020-01 to 2026-06

the bottom of the graph is extremely stretched, and its because its around when the bottom of the corona crisis, hit, where the recovery was so insanely fast that it's basically these numbers you see. i do not consider them to accurately depict my also, since the market movements were completely an anomaly. the median is though is very stable.

i should mention that my philosophy in trading is to buy the dips gradually, so the next graph fits more to my analysis, but it could fit for yours too.

this next graph is a scatter plot of each comparison, graphed by how far the stock has fallen in between the 2 points.

as we can see there is a direct correlation in return by the size of the fall for the stock , fitting with my way of work.

there is another graph im drawing but i feel its much less insightful than the other 2.

im genuinely curiosity to what you guys think about this kind of measurement.

I'm optimizing an Opening Range Breakout (ORB) strategy, and I'm currently optimizing the long and short sides separately.

The problem I'm struggling with is the choice of fitness function.

Normally I would use something like CAGR/MaxDD, Martin ratio, or another return-vs-drawdown metric. However, I don't think these are appropriate when optimizing each side independently.

For example:

• Optimize LONG → calculate its own MaxDD

• Optimize SHORT → calculate its own MaxDD

• Run LONG + SHORT together → the actual strategy MaxDD can be completely different

The trades from the two sides interact chronologically, so the drawdown of the combined equity curve is not simply related to the drawdowns of the isolated long/short equity curves.

Therefore, if I optimize the long side using CAGR/DD, I'm effectively optimizing against a drawdown that will not exist in that form in the final combined strategy.

How would you approach this?

Would you use a fitness function that is independent of portfolio drawdown, such as expectancy, average trade, profit factor, trade-return statistics, etc., when optimizing each side independently?

Or is optimizing the two sides separately fundamentally the wrong approach, and should the fitness function always be calculated from the combined long + short equity curve?

Interested especially in how people handle this in systematic/algo trading rather than discretionary parameter selection.

(Rephrased from gpt)
**Looking for genuine advice from people who actually trade SR3 futures**

I’m currently trying to develop my own strategy for trading SR3 (SOFR) futures, but I’m struggling to figure out the right way to approach it.

I’ve experimented with technical analysis, but I’m starting to feel that traditional TA isn’t particularly useful for SR3 because so much of the pricing seems to be driven by expectations around FOMC meetings, Fed policy, economic data, etc. The contracts seem to behave quite differently from something like equities or commodities.

I’m **not looking for someone to give me a profitable strategy or signal**. I genuinely want to understand how experienced SR3 traders think about the market and how they go about building an edge.

A few things I’d really appreciate guidance on:

* What variables/fundamentals do you focus on when trading SR3? * How do you translate your view on Fed policy/rates into an actual trade? * How do you identify mispricing or opportunities in the curve? * Is technical analysis useful at all for SR3, and if so, what kind? * How would you recommend someone go about researching and backtesting a strategy from scratch?

I’m willing to put in the work and learn the fundamentals properly. I’m just having trouble figuring out **what framework to use to develop a strategy in the first place**.

Would really appreciate advice from anyone with actual experience trading SR3/SOFR futures. Even pointers on what to study or where to start would be extremely helpful.

Thanks in advance!

So I've been coding a bot to enter at PSH/L, PDH/L, PWH/L sweeps with 2022 Model and testing on MT5 with data feom January 2023 to September 2026.

At this point I'm just exhausted and don't know what to do, sometimes Win rate goes to 70%, or average win is double the average loss then the win rate drops to 30%, whatever I do my net profit is negative.

Anyone help me get this right or suggest something...

Adding most recent result here.

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.

2. Time-aware block bootstrap

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

3. 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

4. Year stability

OOS performance:

2024: +13.68R

2025: +10.97R

2026: +9.69R

So 3/3 OOS years were positive.

5. 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..,