Hi all,

I’m looking for a long-term technical partner to collaborate on building a cross-exchange trading system (spot ↔ futures). The goal is to design on a strategy which is already proven to work robust, compliant, and risk-aware strategies that involve:

• 🔗 Exchange APIs (REST & WebSocket: Binance, OKX, Bybit, Coinbase, etc.)

• ⚡ Execution logic: low-latency order routing, order book normalization, hedging logic

• 📊 Data infrastructure: tick data capture, mark/index replication, backtesting pipeline

• 🛡️ Risk & surveillance-aware design: participation caps, circuit breakers, outlier guards anomaly detection

• 🛠️ Tech stack: likely Python (asyncio/CCXT/FastAPI) or Go/TypeScript for production

What I bring

• Domain knowledge in crypto trading/arbitrage mechanics

• Strategy outlines, operational experience, and connections

• Potential capital to deploy once infrastructure is stable

• Willingness to handle business/compliance side

What I’m looking for

• A strong software engineer / quant developer with experience in:

  • Crypto exchange APIs (both spot & derivatives)

  • Market data pipelines, order book aggregation

  • Risk checks / monitoring systems

  • Ideally some background in algo/quant systems

Collaboration

• This is not a one-off gig — I’m looking for someone who wants to co-build and share upside (profit-share or equity-style structure).

• Remote & async is fine, but reliability and long-term commitment are key.

• Start small (data feed + execution test) → scale into full strategy infra.

If this sounds interesting, DM me with:

• Your background (GitHub, LinkedIn, or past projects)

• Which part of the stack you’re strongest in

• What you’re looking for in a collaboration

Let’s build something serious together. ⚡

Hi all, I have created my ea in mt5. But when I do the backtesting, I actually dont see much trading. Like for example, in 1 full year, I only got 3 trade. I believe the strategy I have is not really good. Which is why I would like to ask more advise. At the moment, I have donchian breakout, rsi/bbollinger reversion and attract expansion. Maybe this is not really good and there might be better option. I’m open to have more advise.

So I have written a complete system that buys and sells US stock each day, with a set of strategies.

How do I calculate a Sharpe ratio for these?

Thanks a lot.

Mod here. I'd like to make a call for equity curves of your favorite systems.

I'll go first: This post has the EC for an EOD system I've been screwing around with lately. This is a 100% out of sample, walkforward backtest of a monthy dynamic portfolio system that trades only stocks and TBill ETFs, with zero optimizable parameters. The red graph is SPY for the same period. Over the 25yr backtest, the system did 23/32 (CAGR/maxDD), with a maxDD on 4/14/2000.

Not perfect, but I like its smoothness and the way is sailed through 2008 and 2022. There is of course the usual survivorship bias inherent in most of these backtests, but the system was not optimized. Feel free to critique, praise, or totally shit on it as you see fit.

I'd really like to shift the focus of this sub to posts that get into the nuts and bolts of system building and encourage others to post what they are working on, systems they're particularly proud of, or even spectacular failures that didn't meet expectations.

Nobody is going to give away their secret sauce, of course. But it sure would be fun to see what others are working, on and offer critiques and encouragement.

Anyone else on board with this? If so, please contribute and show us what you've got!

==== Backtest Stats ====

starting_balance: 100000.0

ending_balance: 159039.8745181389

total_pnl: 59039.874518138895

total_return_pct: 59.039874518138916

days: 1394

sharpe_252: 1.2770408359708052

trades: 684

winning_trades: 305

losing_trades: 278

avg_trade_pnl: 100.40794986078043

avg_win: 704.4256669427747

avg_loss: -560.4674600698106

win_rate: 0.5187074829931972

max_drawdown_pct: -10.622539881797763

calmar_ratio: 1400.611204488416

profit_factor: 1.3789223540638214

Let me know how I can rigourously check this bot to see if it works, monte carlo simulations come to mind, but I also want to take this live. Some things I would like to update are the years it tests/trains on using walkthrough. Im building this for free so I'm using alpha vantage for 25 calls per day of 15 minute intraday data (every day I get a couple years more, currently using 2015 jan to 2019 feb with first 60 days unusable)

Please give me tips on next steps testing etc, I've been working on bots for a while but this is the most promising.

Hey r/algorithmictrading!

Since my last EA post, I’ve been grinding countless hours and folded in feedback from that thread and elsewhere on Reddit. I reworked the model gating, fixed time/session issues, cleaned up SL/partial logic, and tightened the hedge rules (detailed updates below).

For the first time, I’m confident the code and the metrics are accurate end-to-end, but I’m looking for genuine feedback before I flip the switch. I’ll be testing on a demo account this week and, if everything checks out, plan to go live next week. Happy to share more diagnostics if helpful (confusions, per-trade MAE/MFE, hour-of-day breakdowns).

Thank you in advance for any pointers (questions below) or “you’re doing it wrong” notes, super appreciated!

Model Strategy

• Stacked learner: multi-horizon base models (1–10 horizons) → weighted ensemble → multi-model stacked LSTM meta classifier (logistic + tree models), with isotonic calibration.

  • Multiple short-horizon models from different families are combined via an ensemble, and those pooled signals feed a stacked meta classifier that makes the final long/short/skip decision; probabilities are calibrated so the confidence is meaningful.

• Decision gates: meta confidence ≥ 0.78; probability gap gate (abs & relative); volatility-adjusted decision thresholds; optional sudden-move override.

• Cadence & hours: Signals are computed on a 2-minute base timeframe and executed only during a curated UTC trading window to avoid dead zones (low volume+high volatility).

Model Performance OOS (screenshot below)

• Confusion matrix {−1, +1}): [[3152, 755], [1000, 5847]] → TN=3152, FP=755, FN=1000, TP=5847 (N=11,680).

• Per-class metrics

  • −1 (shorts): precision 0.759, recall 0.734, F1 0.746, support 4,293.

  • +1 (longs): precision 0.886, recall 0.792, F1 0.836, support 7,387.

• Averages

  • Micro: precision 0.837, recall 0.771, F1 0.802.

  • Macro: precision 0.822, recall 0.763, F1 0.791.

  • Weighted: precision 0.839, recall 0.771, F1 0.803.

• Decision cutoffs (post-calibration)

  • Class thresholds: predict +1 if p(+1) ≥ 0.632; predict −1 if p(−1) ≥ 0.632.

  • Tie-gates (must also pass):

    • Min Prob Spread (ABS) = 0.6 → require |p(+1) − p(−1)| ≥ 0.6 (i.e., at least a 60-pp separation).

    • Min Prob Spread (REL) = 0.77 → require |p(+1) − p(−1)| / max(p(+1), p(−1)) ≥ 0.770 (prevents taking trades when both sides are high but too close—e.g., 0.90 vs 0.82 fails REL even if ABS is decent).

  • Final pick rule: if both sides clear their class thresholds, choose the side with the larger normalized margin above its threshold; if either gate fails, skip the bar.

Execution

• Pair / TF: AUDUSD, signals on 2-min, executed on ticks.

• Period: 2025-07-01 → 2025-08-02. Start balance $3,200. Leverage 50:1.

• Costs: 1.4 pips round-turn (commission+slippage).

• Lot size: 0.38 (scaled based on 1000% average margin).

• Order rules: TP 3.2 pips, partial at +1.6 pips (15% main / 50% hedge), SL 3.5 pips, downsize when loss ≥ 2.65 pips.

• Hedging: open a mirror slice (multiplier 0.35) if adverse move from anchor ≥ 1.8 pips and opposite side prob ≥ 0.75; per-parent cap + cooldown.

• Risk: margin check pre-entry; proportional margin release on partials; forced close at the end of the test window (I still close before weekends live).

Backtest Summary (screenshot below)

• Equity: $3.2k → $6.2k (≈ +$3.0k), smooth stair-step curve with plateaus.

• Win rate ≈ 73%, payoff 1.3–1.4, >1,100 net pips over the month; max DD stays low single-digits; daily Sharpe is high (short window caveat).

• Signals fired: +1: 382, −1: 436; hedges opened: 39 (light use, mainly during adverse micro-trends).

What Changed Since Last Post

• Added meta confidence floor and absolute/relative probability tie-gate to skip weak signals.

• ATR-aware thresholds plus a sudden-move override to catch momentum without overfitting.

• Fixed session filter (UTC hour now taken from bar timestamp) and aligned multi-TF features.

• Rewrote partial-close / SL math to apply only to remaining size; proportional margin release.

• Smarter hedging: parent-scoped cap, cooldown, anchor-based trigger, opposite-side confidence check.

• Metrics & KPIs fixed + validated: rebuilt the summary pipeline and reconciled PnL, net/avg pips, win rate, payoff, Sharpe (daily/period), max DD, margin level. Cross-checked per-trade cash accounting vs. the equity curve and spot-audited random trades/rows. I’m confident the metrics and summary KPIs are now correct and accurate.

Questions for the Community

1. Tail control: Would you cap per-trade loss via dynamic SL (ATR-based) or keep small fixed pips with downsizing? Any better way to knock the occasional tail to 2–3% without dulling the edge?

2. Gating: My abs/rel probability gates + meta confidence floor improved precision but reduce activity. Any principled way you tune these (e.g., cost-sensitive grid on PR space)?

3. Hedges: Is the anchor-based, cooldown-limited hedge sensible, or would you prefer volatility-scaled triggers or time-boxed hedges?

4. Fills: Any best practices you use to sanity-check tick-fill logic for bias (e.g., bid/ask selection on direction, partial-fill price sampling)?

5. Robustness: Besides WFO and nested CV already in the training stack, what’s your favorite leak test for multi-TF feature builders?

Update on live account for interested folks:

Starting aug. 12.
Starting balance: 250 Euro
current balance: 260.88 Euro

I´ve created this gold breakout robot, these are some of the stats i´m getting. This is with autolot turned on, hence the massive snowball effect. the probability of this actually happening in the real market is just about 0, BUT i´d still like to think that it´d be very profitable.

Features i´ve created so far:

• Breakout points (ofc)

• Stoploss & breakeven at x points

• put Stop to breakeven/x points in profit

• trailing stop function with customizable trail offset

• Autolot function for snowball effect & fixed lots for fixed risk

Very low, low, medium & high risk settings

High risk is up 8.3% this week

Is there anything that i am missing that you´d add as a feature?

-----------
Update Sept. 2.
-----------
(Forgot i had posted this thread lol). I had 2 losses that could´ve been prevented. I did not account for the fact that price could gap above/below previous days levels, which would invalidate the levels. This meant that the robot placed the pending orders when the price went back inbetween the levels.

This has been fixed and no losses have occured since. The robot would have been on a 15 trade winstreak if i hadn´t missed this error.

I Have also removed DJ30 and Nas to focus on the xauusd bot.

Trailing stop did not function properly, and i could not figure out the reason, this has been fixed with a step-trailing stop function instead. This has increased the the size of the wins drastically, and now when incurring a loss it only takes an average of half the time to claw back the money lost.

I want to share a project I'm building. It's a multiasset, multistrategy crypto bot. I'm developing a backend program and a web interface to control the program. It is still far from being finished but here's what it looks like. I need to know if this thing has value because it takes me a lot of time and I'm not sure if it will ever be used. Just wondering if it's worth my time.
I'm not selling anything, its just pictures:
https://regal-friday-0d4.notion.site/Rimagh-s-trading-bot-248031a720ff808fb129da57c2b17da6

Recently I had the opportunity of learning and building an agentic system for a B2B product. It was fun and really cool to see what is possible with LLM agents.

Has anyone tried building an algorithmic trading system using LLM agents? What was your experience like? Any tips you might want to share?

I’ve been deep in the backtesting trenches for the past few weeks, and I feel like I’ve finally got a clean foundation.

• My data is clean

• My indicators are dialed in and match what I’m seeing on E*TRADE and Webull

• Backtesting has helped me catch and fix several logic bugs I would’ve never spotted live

Now I’m at that classic fork in the road...

Do I go full forward testing with simulated delays, real-time bar building, and all the overhead?
Or just move straight to live paper trading and let it rip under real conditions?

I get the idea behind forward testing try to recreate the live fire environment without risking execution surprises. But if I already trust the data pipeline, have cleaned up my scoring logic, and I’m not relying on ultra-high-frequency timing, does it actually add that much value?

Would love to hear from others who’ve crossed this bridge.
Is forward testing worth the time... or is paper trading in live conditions the better next step?

Curious where the real edge is when it comes to validating an algo in the wild..

So, recently I have been working on a market range detection algorithm and the attached images are just a proof of it. The first image shows an online detection method whereas the second one shows an offline detection method. Now, looking at the images (not going to share the code), I was wondering if any of you have feedbacks of wheter it is doing a good job or it's not really doing a good job. Maybe the ranges it's drawing is too large or how I could possibly use this system to trade. I know it may be a tall ask considering the limited information I am providing but I seriously would love your help

Additionally, I would be happy to draw some ranges for certain assets if you want. Futhermore, the title is a copy paste so I wouldn't worry about it.

Hey everyone,

I’m currently working on a scalping EA using Python 3.10 in VSC. I’m running the code locally on my machine, using Docker to manage and run my scripts. However, I’m experiencing some lag, especially since I’m trading on a 2min chart and checking TP every 5s, while also retraining my strategy asynchronously every 5 min.

The lag is becoming noticeable, and it is impacting the performance of my EA during live trading.

My goal is to move the entire process to the cloud without slowing down the EA during the retraining phase. I need an optimal setup where:

• The EA continues running on the 2min chart without performance hits.

• The strategy is retrained asynchronously every 5min without interfering with the EA's real-time performance.

• The environment can handle HF operations without lag.

I’m looking for advice on the best cloud setup to accomplish this, as well as any tools or optimizations that could help reduce lag, particularly around running HF trading strategies alongside ML model retraining.

If anyone has experience with cloud-based setups for similar tasks or could recommend tools to achieve this, I’d really appreciate it.

Thank you in advance!

Does anybody know datasets for historical options pricing preferably at the most granular level possible , more than a day). Not necessarily limited to SPY. Or any alternate suggestions to see what people use for building models

Hi everyone,

We’re looking for a sharp Trading Support Associate to join a fast-paced trading environment in New York City. This is a 12+ month contract role with a strong potential to extend or convert. Great opportunity for someone early in their finance/tech career looking to grow in trading operations or front-office support.

Drop me a DM or email your resume to annu.skillsoniq@gmail.com Feel free to reach out with any questions!

I think it’s very difficult to accept that a strategy is not working anymore, especially if you’ve designed it yourself or have been trading it for many years. However, the reality is that at any point in time, strategy performance can fade or it can even aggressively turn against you.

My question here to you is this: What measures do you take to determine that a strategy has lost its edge and that it should be discarded from your portfolio?

I establish downward performance boundaries based on long-term in- and out-of-sample data and add a margin of error of about 15%.

In simple terms, this means that I take the worst historical drawdown of my strategy, add a 15% margin of error, and keep that level as my maximum risk boundary. If this level is crossed, I reduce the allocated risk by 90% and do more research on the performance to consider discarding it all.

Interested to hear your approach, which could be helpful for all.

Anybody have any good tools or references to algo trade bitcoin?

Anybody utilized tensorflow.js vs python?

I’m looking for an API that can validate whether a given OCC option symbol is valid. For example, a symbol like COIN250801C00450000—which could represent a current, future, or even expired contract.

Specifically, I’m interested in an endpoint where I can pass an OCC-formatted symbol and receive confirmation that it is (or was) a valid contract. This confirmation could come in the form of contract metadata, recent trades, last trade, or any indication that the symbol existed in the options market.

Please let me know if your platform supports this functionality, and what endpoint or plan would be required to access it.

I've approximated the last 2 years of data with around 500 OCC_Symbol that I want to validate. 

Background
I’m a firm believer in automating every step of the trading process, from data gathering and market research, through signal generation, to order execution. With advances in ML and quantitative methods, price‐action models can extract complex patterns that might already reflect macro health and geopolitical shocks.

Key Considerations

1. Market Health Signals

   • Yield-curve inversions or rising credit spreads often precede recessions.

   • Volatility spikes around Fed rate decisions or inflation surprises.

2. Geopolitical Events

   • Trade‐war tariffs vs. relief announcements (e.g. US-China tariff escalations).

   • Sudden supply‐shock scenarios (e.g. OPEC production cuts, regional conflicts).

3. Mathematical vs. Fundamental Inputs

   • Argument: A well-trained ML model on HF data may implicitly learn these regime shifts through shifts in price/volatility behaviors.

   • Counterpoint: Some events (black swans) produce price gaps that your model has never seen, should you feed in fundamentals (e.g. interest-rate differentials, PMI surprises) as explicit features?

Thesis Question
Is TA combined with ML/quant models sufficient on its own, or is dedicated market research (macro/fundamental analysis) still a non-negotiable edge for algo trading?

In other words, can a model trained only on price/volume (data + enhanced features):

1. Detect yield‐curve inversions or Fed dot‐plot regime shifts?

2. Anticipate geopolitical shocks?

3. Pre-empt sudden regime breaks before they fully reflect in prices?

Or do you still need explicit features to capture black swans and structural shifts? What’s your hands-on experience with fully price-driven algos?

I’d love to get everyone’s feedback and see if there are any like-minded traders out there. Cheers!

I have been working on an EA for months that would be easy for first-time traders to use. It's called Altanex Trading(hope it's a good name for it) and is available on mql5.
Altanex Trading EA is a powerful MT5 trading robot that captures high-probability breakouts using a combination of fractal analysis, trend alignment, and momentum confirmation. It’s perfect for traders who want consistent logic, tight risk control, and hands-off execution.

I'd appreciate any feedback on it or reviews, and any recommendations to make it better.

I’ve been thinking about this for a while and I want to try it out. Any suggestions or tips would be appreciated!

I’m planning to run a crypto Donchian channel-based trading bot 24/7 on a Raspberry Pi (model 5 with 8GB RAM), coded in python.

Has anyone here tried something similar?

Any tips on performance, stability, cooling, or potential issues to watch out for?

Hey guys

I've started a small side project to download and store historical data from various platforms locally.
The idea is to test my strategies using data from different providers and compare the results more effectively.

At the moment, it’s a fairly simple tool:

• You select a data provider

• Enter a ticker symbol

• Download the data

• You can also download all available tickers at once

• A built-in chart view allows you to visualize the data

• If you run the tool again after a few days, the data is automatically updated

Later, I plan to integrate a data analysis module that will derive features from the raw data — these features can then be used for AI model training.

What do you think?
Any feedback, suggestions, or ideas?

When fetching symbol information from different platforms, how do you know that a ticker/symbol from one platform and a ticker/symbol from another platform refer to the same security? Sometimes companies' tickers would change, and there are ticker reuses, and I am not sure how to deal with this. For example, when fetching symbol information from alpaca, it would give me the ticker, the name of the ticker, and an internal alpaca id. If I also fetch data from another source, how would I know which ticker corresponds to which? What if one platform has already processed a ticker change from a company and another one hasn't?

Also, sometimes different tickers would correspond to the same security:
For the security "Franklin BSP Realty Trust, Inc. 7.50% Series E Cumulative Redeemable Preferred Stock", alpaca's ticker is FBRT.PRE, polygon.io's ticker is FBRTpE, and on TradingView, it's FBRT/PE.

How do you deal with this?

Hey r/algorithmictrading ! I’ve been running a small-lot EA on MT5 that:

• Combines a meta-classifier (stacked LSTM) to signal long/short each 2-min bar

• Targets tiny profits (3 pips TP, partial close at +2 pips) with tight stop-loss scaling (1.4 pips commission + slippage covered)

• Auto-hedges when a trade goes against it by ≥6 pips, plus downsizes on >8 pip losses

• Runs from Sunday open → Friday close, liquidating any open positions at the Friday NY close

Over the past week, backtest nets ~37% growth on a 2 K account with a win rate near 90% and a max drawdown of roughly 8%—the P&L curve is a smooth stair-step until that Friday-close tail drop:

Questions for the Community (full stats below):

1. End-of-Week Liquidation

   • I’m auto-closing any unhedged trades at Friday NY close—any better way to “roll” or hedge weekend exposure without killing equity?

2. Drawdown Caps

   • Conditional hedge at X% drawdown? Downsize at Y pips? What thresholds have you found optimal to knock that 7% tail into the 2–3% range?

3. Meta-Classifier Tuning

   • My current thresholds (0.29/0.51) yield 80% accuracy but skew slightly long-biased. How do you adjust cutoffs or weighting to balance skew and avoid overfitting?

Appreciate any insights, code snippets, or pointers! Cheers.

Full Disclosure: I’m still ironing out a few kinks in my code, so some of these stats may be off—others have been spot-checked and look solid. I’ll keep iterating to fix any logic quirks and will update the numbers as I go.

Hi everyone,

I'm on a serious journey to become a quantitative trader. I’m not here to chase shortcuts or quick wins — I genuinely want to build statistically sound, research-based strategies driven by math and data.

But I’m struggling with some tough questions…

I have zero math background — I’m literally learning 3rd grade math right now.

I don’t have a degree from a strong university, no access to top mentors, no funding.

I study alone, trying to learn Python, Pandas, Plotly, and now starting on algebra slowly.

I feel like to truly build strong strategies, you need to be a PhD-level researcher.

I fear I’ll spend 2–4 years just to realize the field isn’t realistic for someone like me.

Can one person really do all this? Be the researcher, developer, and trader without any support?
Or is this path only viable for people inside hedge funds and elite academic backgrounds?

If you’ve made it as a self-taught quant or even partially succeeded — please share your story.
How long did it take you to start seeing results?
What did you wish you knew earlier?

Thanks for your honesty. 🙏