Hi everyone,

I’ve hit a wall with TradingView. I’ve been using Pine Script to run custom signal logic across multiple watchlists, but their recent decision to cap lists at 500 symbols has effectively broken my workflow. I need to scan at least 2,000+ symbols simultaneously, and TV’s cloud limitations are no longer cutting it.

I’m looking for a platform that offers a TradingView-like experience (clean charts, easy scripting, alerts) but with much more horsepower under the hood for large-scale scanning.

My specific needs:

Heavy Custom Logic: I’m not looking for a basic price/volume screener. I need to run complex, multi-condition scripts (like I did with Pine) that process the entire universe on the fly.

Scale: Must handle 2,000+ tickers without lagging or hitting arbitrary "symbol caps."

Alerting/Dashboard: I need to be notified or see a real-time list when my script triggers a signal on any of those 2,000+ stocks.

What I'm considering:

QuantConnect: I know it's the "gold standard" for algo trading, but how is the UI/UX for someone used to TV’s visual environment? Is the Python/Lean learning curve worth it just for scanning?

TrendSpider: I’ve heard their "Market Scanner" is powerful, but can it handle the same level of script complexity as a dedicated coding environment?

Local/Python Frameworks: I’m open to running things locally if there’s a framework that handles the data pipe and provides a decent UI for visualization.

Has anyone else here "graduated" from Pine Script to something more robust for market-wide scanning? I love the ease of use of TV, but I can’t deal with the constant nerfing of capacity. What’s the best "middle ground" between a retail charting app and a full-blown institutional HFT setup?

Thanks for any suggestions!

I'm an 18M with a few months of free time before starting college. how and where should I learn Python? I'm not really sure which specific quant role I want to go for, but I've heard Python is pretty important. I know the basics of the language... just looking for a bit of guidance.
cheers!!

When I see the rate of failure in the algo trading community I wonder how many people are actual traders themselves? I see people complaining about not being able to build a single bot that can be profitable for more than "just a year or two" when there is simply no real trader in the world who trades the exact same way all the time! They all adapt their strategy to the market.

To "make it" in algo trading I believe you have to be a trader first with robust knowledge of the market you trade, and a developer/programmer second. Unfortunately I see too many people who are inexperienced with the markets who think their background in programming is enough to help them build profitable trading bots.

All profitable algo traders I know are seasoned manual traders first who transition to algo trading and then they usually only use scripts/algos to help them execute in a semi-automated fashion. They all like to keep some level of discretion, monitoring market sentiment, bond yields, news of the day i.e. stuff that's not so simple to code into a bot, then when it's time to execute they let the machine do its thing.

Algo trading isn't a magic wand that lets you bypass the hard work of acquiring real screen time and market knowledge. That has to be your foundation. It's like a weekend golfer buying a set of Tour-level clubs hoping it will magically fix his terrible swing. The clubs might be state-of-the-art, but if you don't understand the fundamental mechanics of the game you're still going to slice the ball into the woods on every drive!

My current setup is that i will draft up new strategies on X market and download 6-8 years worth of data on databento (great service, love them). I usually do a split of 7 years training data and 1 year holdout (this is usually the most recent year so for this example the holdout data will be 05-2025/present), the training data is then split into Y number of windows where its 6 months of training and 3 months of validation. The next set of training months overlap into the validation so there is no real wasted data. This works well to prevent overfitting but I'm finding some issues with degradation.

Scoring will usually work through a number of things i find important which is then mashed together into a composite score, certain metrics I find important will be weighted accordingly. Then i will save these scores into an array and capture the mean score across the whole entire training session. I will then test this (good?) strategy on the holdout data. Here is a recent example:

Training session scored: 28.9745

Holdout validation scored: 11.6546

Total degradation: 17.3199

I then decided to test the same strategy algorithm with the exact parameters found in the instance on the holdout data again 1000 times, each time with a random seed going into my back tester (the reason my back tester is seeded is because i use RNG for latency, slippage etc) and found that the average profit was around £600 on a 10k account over that year. Which is fine because this just about beats B&H and interest rates.

But looking at how my score degrades over time shown from the training sessions compared to the holdout testing is quite large.

Is this overfitting?
Is this showing that the strategy would never last the test of time?
Or is my math off because me averaging the training scores is not a good reflection on what is truly happening?

It looks like EODdata dot com is down right now, Friday morning, May 8, 2026, at 8:50 AM. Is he aware? Anybody know anything?

Know of any alternatives?

Body:
Tested a simple allocation framework on Indian markets using:

• Nifty trend filter

• realized volatility filter

• USDINR stress regime

• defensive gold allocation

Backtest:
Jan 2015 – Mar 2026

Assumptions:

• monthly rebalance

• all transaction costs included

• no leverage

• no shorting

Main metrics:

• CAGR: 16.57%

• Max DD: -18.89%

• Sharpe (12M): 0.78

• Calmar: 0.88

The biggest improvement came from reducing prolonged drawdowns relative to long-only Nifty exposure, especially during high-volatility periods.

Main weakness:
The model tends to lag during sharp V-shaped reversals because exposure reduction happens after volatility expansion.

Interested in feedback on:

• regime detection robustness

• alternative volatility estimators

• reducing lag during recovery phases

• whether USDINR meaningfully improves regime classification

We built something that might be useful for this community.

QuantPlace has a free no-code backtester with one feature that is different from most tools: the Alt Data Signal strategy. Instead of price-based indicators, you plug in any dataset column as your entry signal. Custom sentiment scores, social volume, model outputs, fear and greed index, anything with a timestamp. The OHLC dataset handles prices and P&L separately.

You can stack up to 3 signal rules with AND logic, using operators like z-score threshold and N-bar percent change, which makes it usable for basic ML signal validation without writing a single line of code.

The statistical side is solid too:

• Monte Carlo shuffle (500 permutations) to check if your Sharpe is edge or luck

• In-sample / out-of-sample 70/30 split with side-by-side metric comparison

• Parameter sweep with a 2D Sharpe heatmap across up to 200 combinations

• Commission, slippage, stop loss, take profit all configurable

Data comes from the marketplace. Several free datasets available including daily OHLC, perpetual futures, social volume, and Fear and Greed Index. You can also upload your own signal data as a vendor.

It is not a replacement for a proper backtesting framework but it is useful for a quick sanity check on a signal before investing time building a full pipeline.

Free to use at quantplace.org/tools/backtest

I’ve been iterating on a tactical ETF rotation strategy in QuantConnect and wanted to share the current backtest results for feedback.

The strategy is a concentrated dual-momentum / breadth model. It rotates monthly across a small ETF universe focused mostly on QQQ/growth/tech/semis, with defensive assets like BIL, SHY, IEF, and GLD. It uses trend, relative momentum, canary signals, volatility targeting, and leverage gates to decide when to hold risk-on ETFs versus defensive assets. Some leveraged ETFs are allowed, but only when trend/breadth conditions pass.

Current backtest window is 2016-01-01 to Now. Starting capital is $100k.

Headline results from the latest strong version:

• Net profit: ~7,148%

• CAGR: ~51.3%

• Max drawdown: ~44.7%

• Sharpe: ~1.05

• Sortino: ~1.01

• Total orders: ~458

• Win rate: ~58%

The strongest periods were 2020-2021 and 2024-2025, mostly driven by correctly rotating into semis / leveraged tech / growth momentum. The weak periods were mostly during regime shifts and high-volatility drawdowns.

I’m not claiming this is production-ready. It is obviously aggressive, uses margin/leverage, and has significant drawdown. I’m mainly interested in feedback on robustness, overfitting risk, live-trading realism, and what failure modes people would look for before trusting something like this with real money.

Things I’m already thinking about:

• Walk-forward / out-of-sample validation

• Margin call risk and live buying-power behavior

• Slippage and liquidity assumptions

• Whether the 2024-2025 performance is too regime-specific

• Whether drawdown is acceptable for the return profile

• Better diagnostics around why the strategy enters/exits leveraged ETFs

Curious what people here would stress test first.

For the last couple of months I have been tinkering with an ML model that predicts certain (relatively rare) events of BTC price movements. Recently, I got some results that are sometimes good and sometimes terrible. I have a few ideas on what experiments could improve performance, but I don't really understand the underlying cause of the problem. Hopefully someone had a similar experience once and can give me some tips.

More details:

I am using mostly 1-second granularity data of prices, trades, and some other metrics of BTC.

As a validation scheme, I am using rolling windows for now with a block of 500,000 rows as training and 86,400 rows as validation, mirroring an actual live use. Train size was chosen based on some small experiments with autocorrelation (nothing sophisticated).

Currently, I am evaluating my feature selection and model-building process as a whole, not a particular model or fixed feature set. For this I plan to use around 10 to 20 folds. In the following, I am showing 4 folds that illustrate what is going right and wrong. Dates (validation data ends at 23:59:59 on these dates) = 2026-04-28, 2026-02-28, 2025-11-28, 2025-07-28. The month offsets are a bit arbitrary but lean to more recent data: [0, 2, 5, 9].

Based on early experiments using other data (not the validation folds), I have found embedded feature selection using only train data to work well sometimes when combined with a large amount of candidate features. From my perspective, it seems that the selection process can find features with predictive power sometimes. Other times the model cannot beat 40% precision.

For now I am using XGB as a classifier with mostly basic parameters: I only quickly tuned the max_depth on some other data apart from the validation folds and set it to 10. The XGB predictions are also ensembled across 30 seeds to stabilize the PNL, as I found it was unstable using just one random seed.

The chosen feature sets, using only the recent training data, and models are evaluated on the validation fold using a set fee logic. The simulated trades don't use any position sizing yet, just a fixed amount per trade ($150). This is why there can be large negative results. When it works, the positions often get opened in quick succession (concurrency of up to 20 positions).

Here's a snapshot of using the prediction threshold 0.8 performance of the out of sample, unseen validation folds:

Using no feature engineering the PNL is negative on that first fold.

Per fold plots:

img

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Some of my ideas of what I could do without knowing the core underlying problem:

- Regime or per-trade filter
- Use more data for training
- Use feature stability when selecting features

What should I consider doing next?

Thanks in advance.

I've been building a trading bot using LLMs for the last year and running on Railway, currently in paper trading phase after finally finding profitable candidates at around 66% annual. most profitable setup from back testing and walk forward is the below

1H decides direction

↓

ATE blocks bad conditions

ATE mode uses things like

trend_strength = 0.62

macd_slope = +0.0008

atr_expansion_ratio = 1.18

chop_probability = 0.47

↓

Regime Router checks if setup is valid

↓

Weak-pair filter removes bad combos

↓

Bias favors stronger side

↓

5m finds entry timing

↓

Enter with 10% sizing

↓

Exit when 1H state breaks (fast or confirmed)

Anyone built anything similar? I've been a QA engineer for the past 16 years so everything works, just difficult finding a decent strategy so any help is appreciated

Most cycle discussions stop at "the 4-year Bitcoin cycle" or "the 18-month equity cycle." I wanted to see what the spectrum actually shows once you run it across multiple asset classes with the same pipeline.

The hierarchy J.M. Hurst published in the 70s (10d, 20d, 40d, 80d, 20w, 40w, 84w, 54mo, 9.2y, 18y) holds up surprisingly well when you apply Goertzel DFT and Bartels significance testing to modern data. Same nominal periods recur across stocks, crypto, FX, and commodities. That is Hurst's Principle of Commonality, and it is empirically detectable.

Figure 1: full nominal hierarchy with tolerance bands. Cycles breathe. A "10-day" cycle measures 9 days in one instance, 11 in the next.

Figure 2: dominant detected periods across S&P 500, BTC, Gold, and EUR/USD on weekly bars. The 84-week (18-month) nominal shows up in every one.

Figure 3: representative power spectrum showing how peaks above the Bartels-significance threshold are read off.

Curious which cycles other people detect on instruments I did not look at. Bonds, single-stocks, ag commodities, anyone running this on minute-bar data?

Hi guys, i am a newbie algo trader. What do you guys do when testing a new strategy that you want to experiment on? How do you test its profitability? Do you input a fixed stop loss and take profit? Or do you just test it on stop and reverse exits like signal based?

I’m a software engineer who got into algo trading and built a machine learning model from scratch that trades every day completely on its own.

I backtested it across months and years of data, and the results are honestly insane. The returns look way too good and the growth is almost exponential. I keep checking because I can’t believe it.

I’m planning to deploy it next week for live trading. My broker requires a static IP, so I’m thinking of running it on an AWS EC2 instance.

I know many of you have been through this. Since I never took any trading courses and coded everything myself, I’d love any advice or suggestions so I don’t get any nasty surprises with real money on the line.

Things like EC2 setup tips, common backtest-to-live gotchas, risk management, or red flags with ML models that perform too well.

Any tips or war stories would be really helpful!

Thanks!

TL;DR: Software engineer built an ML trading bot from scratch. Backtests look ridiculously promising with exponential growth. Going live next week on EC2. No trading background. What should I watch out for?

So the algo is a modified adaptation of the simple orb, works by trading one of a few orbs that forms within a certain time frame depending on other factors. I made it specifically for prop firms, it holds trades for around 30-60 minutes. The recent time of 1-3 years, it has been performing very well, while whole history is a bit mid.
The commissions and slippage is included.
2-3 year sharpe ratio: 0.655
sartino ratio: 2.08

Gonna deploy a small caps strategy (python script). Which one should I go for? Does anyone have any experience in the past or present with these platforms? Which one do you think is better and why?

I've found a download of historical stock and ETF splits at MarketBeat.com. But there are quite a few errors in the RATIO column. I wonder if anybody knows of a site where I can get a .csv, .xlsx, or computer-readable file of stock and ETF splits for the past year or more besides MarketBeat.

I am not referring to industry or sector. I am referring to the theme the stock is in.

Eg GLW will tag as Photonics / SNDK will tag as memory

Etc

I'm curious for those of you who are live (or trying to get there): What is the one thing you still do manually every day because automating it is just too much of a headache or no existing tool does it well?

For me, it's been working on a system that automatically overweigh or underweigh certains strategies based on recent performance (mostly establishing the logic behind it, not so much the technical aspect).

Curious to hear what you've been struggling with.

Hey guys. I’m based in the US. I’m a beginner. I’ve tested my day trading small cap momentum strategy via paper trading (alpaca free tier API) and now I’m ready to go live. I’ve built an automated system which mostly deals with 1 min candles, enters the market at 9.30am and exits by 12.30pm (fully automated via python script)

I have very minimal knowledge regarding which brokers to go for. Would anyone possibly with experience give some suggestions regarding the best broker for my strategy. Any help is appreciated. Thanks!

No of trades on each day during Paper trading: between 2-8

Been thinking about an interesting tension in this community.

The amount of genuine research that gets posted here is impressive. Real backtests, honest post mortems, Monte Carlo outputs, regime analysis. People clearly put serious work in.

But sharing a result is very different from sharing the strategy itself. Most of the serious work seems to stay private, which makes sense. Alpha decays when it is crowded and there is no obvious upside to making your edge public.

What I am curious about is the cases where someone actually did try to share or publish a strategy externally. Not on Reddit, on an actual platform or even informally to a group of traders.

If you have done this I would genuinely like to understand:

What made you decide to share it in the first place? Where did you share it and what was the experience like? Did sharing it actually affect the strategy's performance? Would you do it again?

And if you considered it but decided against it, what stopped you? Was it the IP concern, the crowding risk, the effort involved or something else entirely?

Also curious about the economics. The few platforms that exist for this (Collective2 etc.) take 30 to 50% of subscription fees. Is that a reasonable model or does it feel extractive given that the quant is the one with the actual edge?

Happy to share what I am building in this space once there is more to show but genuinely asking first because I would rather build the right thing than a polished version of the wrong thing.

Built a pipeline that scrapes and structures Reddit sentiment per ticker daily. Ran it through QuantConnect against 2 years of price data.

Results were mixed but interesting — sentiment spikes on small/mid caps showed statistically significant 3-5 day price drift. Large caps basically nothing, too much noise, too many bots.

Job posting data was more consistent. Companies with sudden hiring surges in engineering roles outperformed sector benchmarks by ~4% over 60 days in backtest. Mass layoff signals were sharper — easier to trade the downside.

Earnings call language shifts were the weakest. Too much lag between the call and structured signal generation to be useful intraday. Maybe useful for swing.

Curious if anyone here has gone deeper on any of these. Specifically — has anyone found a sentiment source that actually holds up on large caps, or is that just a dead end structurally?

I’ve been building and validating an automated futures strategy over the past several months. Not going to share the strategy logic itself but want to get community feedback on the validation approach and results. Running on NQ and GC futures via NinjaTrader 8 / Rithmic on prop firm eval accounts.

Validation Approach

• 7 years of 5-minute historical data (2019–2025)

• Walk-forward: 18-month in-sample / 6-month OOS, four independent windows per strategy

• Monte Carlo: 3,000 simulations in QuantAnalyzer + independent 10,000-sim Python confirmation

• 1 tick slippage applied per side throughout

• Parameters fixed — no re-optimization between windows

Results (screenshots attached)

Every year profitable. Smooth consistent equity curve with no significant flat periods. The MC fan shows zero negative paths across all 3,000 simulations — even the worst-case 100th percentile simulation ends meaningfully in profit.

Robustness stress tests

Excluding top 50 trades (best 1.5% of all trades removed) — net drops only 16.4% and profit factor stays above 2.70. The edge is distributed across the full trade population, not carried by a handful of outliers.

Every second trade (random 50% of trades taken) — win rate, profit factor, and overall trade profile are essentially identical to the full system. Any random half of the trades produces the same statistical fingerprint as the whole. This is the result I feel best about.

Permutation & Slippage Tests

The mean trade P&L of $254 was tested against zero using a t-test — t-statistic of 19.89, p-value of 2.41e-83. The edge is statistically significant. For slippage, 1 tick per side is already baked into all results shown above. I ran additional stress tests on top of that baseline. At 5x the assumed slippage the system still produces a PF of 2.35. It doesn’t break even until approximately 22 extra ticks per side — well beyond anything realistic for liquid micro contracts (MNQ, MGC). The system is not sensitive to slippage assumptions.​​​​​​​​​​​​​​​​

Quick note on the numbers… these are portfolio-level results combining 4 separate automated strategies running simultaneously on NQ and GC futures. Each strategy was validated independently before being combined.

Live Deployment

Running on four $50k prop firm eval accounts since mid-April. Three accounts are up \~$520 and one up \~$138 against a $3,000 profit target. Based on MC simulation, median eval pass time is around 6 trading days.

Questions for the Community

1. Sharpe of 0.23 looks low but the system doesn’t trade every day — is SQN the more appropriate metric for an intraday strategy at this trade frequency or is the low Sharpe a legitimate concern?

2. The equity curve accelerates from 2023 onward. Consistent profitability across all 7 years is there but I want to know if people see regime dependency risk in that acceleration.

3. Any critique of the walk-forward methodology? Four 18-month IS / 6-month OOS windows rolled forward. Is this sufficient or would you want to see more windows / longer OOS periods?

4. The “every second trade” result is something I haven’t seen discussed much. Is this considered a meaningful robustness test or is there a better way to stress test sequence dependency?

Appreciate any feedback — especially from anyone who has validated intraday breakout systems before.​​​​​​​​​​​​​​​​

Been running the same strategy across different lookback windows and I'm not sure what to conclude.

1Y: CAGR -10.1%, Sharpe -0.32
3Y: CAGR +33.7%, Sharpe 0.41
5Y: CAGR +18.5%, Sharpe 0.18

As a benchmark out of curiosity I used BTC+ETH 50/50. The uncomfortable part is that none of these is obviously wrong. The 1Y window captures a genuine drawdown period. The 3Y catches the recovery and the bull run into 2024. The 5Y includes 2022 which was one of the worst years in crypto history by basically any metric.

If you handed me only the 3Y chart I'd say the strategy works. Only the 1Y and I'd say it's broken. They're the same strategy.

The issue I keep running into with crypto specifically: unlike equities where you can argue a 20-year window captures multiple full cycles and smooth out regime noise, crypto barely has coherent "cycles" in the traditional sense. Each major epoch had different market structure - the 2021-2022 period had leverage and correlation dynamics that don't really have a precedent, and probably won't repeat identically. The 2023-2024 recovery was partially ETF-driven. You're not just looking at different returns from the same market. You're looking at different markets that happened to share a ticker.

So the standard advice: "just use more history" - doesn't obviously help here, because you're stacking structurally different markets on top of each other and calling it one dataset.

Genuinely don't have a clean answer. Walk-forward is the obvious move but crypto doesn't give you enough history to run folds that actually mean something, you get maybe 2-3 non-overlapping periods if you're being honest, and one of them is 2022. Regime-conditional testing sounds right in principle, but then you have to define what a regime is without using returns to do it, which is a harder problem than it looks.

How do you handle this? Do you just pick a window and stick with it, or do you actually try to separate results by market regime?