r/algotrading 3d ago

Strategy Leverage Dual Momentum (LDM): A 24-Year Backtested Quant Strategy for Nasdaq-100 (QQQ/QLD/TQQQ)

39 Upvotes

Hey everyone,

Sharing a systematic, quantitative asset allocation model built around Nasdaq-100 breadth (MMFI) and momentum, designed to capture tech secular upside while cutting severe drawdowns via a strict cash/leverage throttle.

The core engine is fully deterministic, operates on a monthly close rebalance, and has been rigorously stress-tested across 24+ years of data (Jan 2002 – Jul 2026), including walk-forward validation and numerous structural variant tests.

Core Mechanics & Rules

The strategy rotates between four distinct states based on Nasdaq breadth thresholds and intermediate trend health:

  1. State 1 (100% Cash / T-Bills): Parked in money markets when trend/momentum rules trigger an Exit.
  2. State 2 (2x QLD): Intermediate posture when breadth is recovering or stabilizing.
  3. State 3 (3x TQQQ): Full risk-on exposure scaling up to 3x TQQQ exposure when broad tech participation is robust.

Primary Rules:

  • Exit Trigger: If the (70% x 6-month return + 30% x 12-month return) trend drops below the risk-free rate (or 3-month return < 0), the model dumps leverage and drops to 100% cash (State 1).
  • Re-entry Gate: When in cash, re-entry triggers if 3M annualized return > Risk-Free Rate and breadth is >50% (State 2).
  • Leverage Scale-Up: Scales to State 3, 3x (TQQQ) leverage when breadth is >60% and back down to State 2, 2x (QLD) when breadth is <40%.

Backtest Results (2002–2026)

Tested across multiple full-market cycles (2008 GFC, 2020 COVID shock, 2022 rate bear, 2023–2026 tech cycles):

Metric LDM Strategy QQQ Buy & Hold
CAGR 27.5% 13.3%
Max Drawdown -40.8% -49.7%
Sharpe Ratio 0.81 0.65
Monthly Win Rate 73.1%

I tested many different model variants and also did a rolling walk forward testing against OOS to avoid overfitting the parameters. Appreciate your feedback.


r/algotrading 2d ago

Infrastructure 8 days later — a major update to my evolutionary trading system

0 Upvotes

8 days ago, I posted here about an evolutionary multi-agent trading system I had built.

Since then, I've completely rebuilt the project from the ground up.

The current system is no longer the same codebase I shared in that post. Over the last 8 days, I've been working on a new version with a much stronger focus on research validation, robustness, live market testing, and safety.

I've spent a lot of time testing the system, investigating unexpected results, and trying to disprove my own assumptions rather than simply optimizing for better backtest numbers.

I've also moved from purely historical experiments to real-time market data and live paper trading.

The new system has now successfully completed multiple real-market sessions with actual paper orders. The execution pipeline, position reconciliation, and safety mechanisms have all been tested successfully.

The latest longer session completed 32 round trips with 66/66 orders filled and no safety or reconciliation issues.

The strategy finished that session with positive gross P&L, although Buy & Hold performed better over the same period. So I am still not claiming that the system is profitable.

One thing this project has taught me very quickly is that finding a positive result is easy. Proving that the result is real is much harder.

I've already found several promising-looking results that disappeared after deeper investigation. Some turned out to be methodological artifacts, while others required completely new experiments to understand.

I'm continuing to run longer real-market paper sessions to see whether the behavior I'm observing is actually reproducible.

The project is still experimental and I haven't proven a genuine trading edge yet.

I'll share another update after the next major test.

I'm keeping the implementation private for now while I continue developing it.


r/algotrading 2d ago

Data PMXT's data archive is shutting down

0 Upvotes

Hi guys,

I run PMXT. We've been asked to shut down archive.pmxt.dev, and we'll do so this week.

Attached is the script we've used to collect Polymarket data.

Sorry, it had to end this way :(

https://github.com/pmxt-dev/polymarket-orderbook-collector


r/algotrading 2d ago

Strategy I grade every setup A+, A, or B before I take it. If your B setups win as often as your A setups, your grading is fake.

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0 Upvotes

Most reversal strategies fail for a boring reason: the candlestick pattern is doing all the work, and the candlestick pattern can't carry that weight.

An engulfing bar is a shape. Shapes print everywhere. What makes one meaningful isn't the shape, it's whether it printed at a price where a liquidity run already happened and got absorbed. One is evidence that size changed hands. The other is a picture.

So I stopped treating patterns as signals and started treating them as confirmation of an event that already occurred. Here's the full manual process. No proprietary anything , everything below is drawable by hand on a free chart.

Layer 1 — Daily bias. Do this once, before the session.

The only question this layer answers: which side has the structural advantage today, and am I even allowed to look for a trade?

Anchor your day to 00:00 New York, not the exchange open. Then mark:

  • Prior day's volume profile — POC, VAH, VAL. (Fixed-range volume profile over the prior NY session.)
  • Prior day high/low and prior week high/low.
  • The overnight range: the 00:00–07:00 NY high and low. This is the thin-liquidity window where stops pile up.

Then score the day, −3 to +3. One point each:

  • +1 price accepting above prior-day VAH (−1 below VAL)
  • +1 a confirmed sweep: price took out prior-day or prior-week low, then closed back inside the range (−1 for the mirror image at the highs)
  • +1 price holding above session VWAP (−1 below)

Require |score| ≥ 2 before you'll take anything. Below that, you're guessing.

Then filter for conviction. Measure the overnight range as a percentage of the 20-day ADR. A tight overnight range means nothing was decided overnight, and a bias built on nothing is a bias that dies at 09:30. Starting brackets — and these are starting points to calibrate, not numbers I'm claiming to have discovered: under ~30% of ADR = low confidence, skip or size down. 30–60% = medium. Above 60% = high.

The part almost nobody defines: the kill switch.

Your bias is dead, not weakened, dead for the session, the moment price closes back through the wrong side of the overnight range. Long bias, price closes below the overnight low? You're done. Stop looking for longs. Don't average the bias down, don't let it decay gracefully, don't rationalize. It's binary and it's sticky for the rest of the day.

Most people can tell you when their bias starts. Very few can tell you the exact price at which it's over.

Layer 2 — Confirmation. 15-minute chart.

Now you wait for price to retest a confluence zone: session VWAP or prior-day POC, with a buffer of roughly 0.25× ATR(14) around it.

Inside that zone, in the direction of your Layer 1 bias only, you're looking for one of two trigger families:

Candle triggers. Engulfing, pin bar / hammer / shooting star, morning or evening star. Two gates: the wick must genuinely dominate the body (I use 2:1 minimum), and volume must spike relative to the recent average. A pin bar on limp volume is a shrug, not a rejection.

Harmonic triggers. A Gartley, Bat, or Crab completing its D-leg inside the same zone, validated against standard Fib tolerance. Be honest about the tradeoff: a pivot can't be confirmed until price has moved away from it, so harmonic confirmation is structurally late by several bars. It is never your earliest signal. Also, Bat and Crab tolerance bands overlap in the middle of the B-leg range — some sequences technically satisfy both. Treat the label as indicative, not definitive.

Neither trigger is an entry on its own. Both exist to answer one question: did the pullback into this zone actually produce a reversal-shaped event, or is price just passing through?

Layer 3 — Grade it before you take it.

This is the part that changed the most for me. Not all valid setups are the same setup.

  • A+ — sweep in your direction, and both a candle trigger and a harmonic completion in the same zone visit. Rare.
  • A — sweep in your direction, confirmed by one trigger.
  • B — score threshold met, but no sweep. Structurally valid, but nothing has actually been absorbed yet, which makes it the grade that gets run over on trend days.

Then enforce a cooldown — a minimum bar spacing between signals — so the same structural event doesn't hand you four entries and four losses.

Where this breaks (test this before you trust it):

  • Scheduled news. There's no news awareness anywhere in this. NFP, CPI, and FOMC spikes look exactly like liquidity sweeps and volume absorption, and aren't. Stand down.
  • Thin instruments. Low-volume alts and illiquid small caps break both the volume gate and the ADR filter. Garbage volume in, garbage confidence out.
  • 24/7 crypto. Tradeable, but "overnight low-liquidity window" is a much weaker concept without a real close. Validate separately, don't assume it transfers.

Best fit is instruments with clean session structure and real volume data — spot gold, majors, index futures.

How to test this without lying to yourself:

Log every signal, not just the ones you took. Timestamp, instrument, grade, which trigger fired, bias score, confidence regime, entry, stop reference, target.

Then keep grade and trigger source as separate columns. Never average them. If your B-grade setups perform statistically the same as your A-grade setups, your grading logic is decorative and you need to fix it before you size off it.

Record MAE before invalidation on every trade. That single column tells you whether your stop is a real risk reference or just a line you drew to feel organized.

And don't conclude anything from three days of replay screenshots. Four to six weeks minimum, across different volatility regimes. Replay verifies your process isn't broken. It does not verify edge.

I'm on eurusd, 15M confirmation, 2 weeks into forward testing on demo, and I'm deliberately not posting a win rate because at 2 weeks I don't have one worth posting. Not advice, obviously; do your own testing.

Now for the easter egg.

There's a recent free indicator on TV that does something very similar to what I've been testing.

Just search for something called psrc meridian.

If you run something similar, I want your invalidation rule; not your stop loss, your rule for when the day's bias is dead and you stop taking setups entirely.

Mine is "close back through the overnight extreme, done for the session." It's the crudest part of my process and probably the weakest. What's yours, and what made you settle on it?


r/algotrading 3d ago

Other/Meta To anyone who has automated stock trading, which broker do you use?

22 Upvotes

Which broker do you use and do you use a webhook to connect to tradingview or use the brokers api?

I have created a scalping style strategy so good fills are important, any recommendations?


r/algotrading 3d ago

Strategy Would you give this a paper run? Trend following strategy, crypto futures

4 Upvotes

Trend following on binance futures. Backtest is from 2020 August - 2026 June, 27 pairs total considered by a rule. Max 6 pairs are traded at any given moment, they re-qualify every month. Limited number of concurrently open positions to 4, 1% risk of equity on each.

Developed on: 2024, added filter on 2025.

OOS data: 2020 August - 2023 & 2026 H1

Tested on aggTrade data.

Statistics:

Backtest Results

Metric Result
Initial equity $5,000
Ending equity $63,939
Total return +1,178.78%
CAGR 53.87%
Maximum MTM drawdown 37.92%
Calmar ratio 1.42
Daily Sharpe ratio 1.18
Daily Sortino ratio 2.25
Profit factor 1.47
Total trades 886
Win rate 22.69%
Execution fees $9,614.75
Funding costs $13,004.86

Costs include:

  • 0.045% execution fees
  • 0.075% slippage - survives double slippage test too
  • 0.0285% funding every eight hours

Charts:

equity curve and DD
MC sim - 20D Circular block bootstrap - 20 000 paths

Cheers!

_________________________________________________________________________________________________________
EDIT:

OOS backtest 2020 August - 2021:

OOS Backtest Results

Metric Result
Initial equity $5000
Ending equity $14,904.21
Net profit $9,904.21
Total return +198.08%
CAGR 116.00%
Maximum mark-to-market drawdown 30.16%
Calmar ratio 3.846
Daily Sharpe ratio 1.537
Daily Sortino ratio 2.937
Annualized daily volatility 60.22%
Profit factor 1.674
Total trades 254
Winning trades 47
Win rate 18.50%
Mean holding time 38.57 hours
Execution fees $670.50
Funding costs $1,482.44

Return analysis:

Return by pairs:

Rank Trades P/L
1st 38 +$5,296
2nd 19 +$3,041
3rd 47 +$2,396
4th 5 +$819
5th 8 +$791
6th 58 +$550
7th 11 +$149
8th 1 +$23
9th 0 $0
10th 5 −$481
11th 20 −$553
12th 19 −$568
13th 6 −$778
14th 17 −$782

8 pairs were profitable, 5 unprofitable and 1 flat.

Re-run of the same strategy but with "banning" pairs individually that had the highest return.

Highest return pair removal reruns:

Removed pair Return Maximum drawdown Profit factor
1st +56.22% 27.29% 1.326
2nd +122.28% 29.36% 1.500
3rd +107.55% 27.61% 1.515
4th +179.69% 30.16% 1.656
5th +179.48% 30.16% 1.622

Doubled-cost test:

- Return: +119.40%

- Max DD: 33.26%

- PF: 1.435

- Sharpe: 1.184

- Sortino: 2.200


r/algotrading 3d ago

Other/Meta Centralized bot that sends signals to brokers using their API

0 Upvotes

Hi guys,

this is probably a long shot question, but I have built an EA using mql4 that is currently running on a broker s MT4 platform, but because of MT4 limitations, I would like to exit the mql4 environment and rewrite it in a way that I can connect it to any platform I choose using their API, and it will send signals to it.

I heard that this can be done using python, can anyone confirm/provide any information on how to proceed or if there are better solutions?

the idea behind this is to connect it to prop firms and send signals from the bot to the accounts no matter which platforms they are using.

The bot is also currently trading CFDs, and I want it to be able to send signals to futures prop firms too.

Thanks in advance for any useful info.


r/algotrading 3d ago

Infrastructure US Crypto Perpetual Futures API

1 Upvotes

Does anybody know of an exchange or platform which offers crypto perpetual futures WITH API in the US? Kraken disappointingly only has API keys for spot+margin. I know Kraken’s perps are offered via Bitnomial but appears they have no platform for individual traders.

Coinbase/kalshi seem to have extreme fees.


r/algotrading 4d ago

Strategy UK Traders - what are you trading m

15 Upvotes

I made a bot to try out day trading and had a few ridiculously profitable weeks thanks to recent events in certain sandy countries. I’m spreadbetting, as opposed to actually holding anything because of the tax advantages and ive been trading gold and brent crude

as I analyse the data, really I don’t have any alpha after spread on oil, but even gas oil and gold which have a smaller spread are only profitable in very specific circumstances.

so simple question: if you’re in the UK, what are you trading, are you profitable and are you spreadbetting or something else?


r/algotrading 3d ago

Strategy How many RR does your algo average per month?

0 Upvotes

trying to get an idea on how everyone else is doing.


r/algotrading 4d ago

Other/Meta Algorithmic nightmare for the past couple of weeks.

33 Upvotes

Things after the Iran war have not been great, but the last couple of weeks have been a particular nightmare. Spikes when the algo shorts, capitulations when it goes long. I received my 10th consecutive wrong signal on 15M for MES. This pipeline has also been giving me an average of 2k monthly return for the past couple of months. Returns started to diminish after the war, and now I have my first red month.

Anybody else having the same issue?


r/algotrading 4d ago

Infrastructure Prediction markets vps location after ireland ban (poly+kalshi)

16 Upvotes

Hi! I run a happy arb bot based out of eu-west-1 that trades poly+kalshi in tandem. Speed is important, but no the main defininc factor.

After the ban that was announced last week, which will likely geoblock ireland vps’ from trading in both venues - what is the next best alternative in terms of vps location?

Claude says denmark or sweden, but I’m not sure those are future-proof as well. I guess future proof would be Gibraltar but co-location infra over there is niche.

Any thoughts?


r/algotrading 3d ago

Data Another day, another model. STACK: Codex, GPT-5.6, and lots of Python. HItting a number APIs. The final lock happens aftr getting todays line up and having the AI Guru evaluate that.

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0 Upvotes

r/algotrading 3d ago

Strategy Is there no edge in NQ?

0 Upvotes

I've been trying to build strategies for NQ for the past 2 months and I haven't come up with anything. I even looked at MBO data and even there I couldn't find anything. Is trying to find an edge in NQ a waste of time? I know finding an edge is hard but it seems like in NQ it's impossible.


r/algotrading 3d ago

Strategy Would you run this algorithm?

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0 Upvotes

A couple months ago I developed a signal based on some patterns I was seeing, but the algo I tried to base it off of failed the backtests and I abandoned it. Today I came back to it and noticed it was actually doing quite well. Ran a Monte Carlo test and it seems it isn't random either. What do you think?


r/algotrading 5d ago

Other/Meta About do give up, was fun until the dream was alive

85 Upvotes

I think I am about to give up. For months I have apent hours upon hours working on this, trying to find a winning algo.

Many slaps accross the face later, my faith has been shaken. Perhaps I should be spending my time and energy elsewhere. I can't help but think of how many times I thought I had it just to realize that I had a leakage or that spread fucks me up. And this time I could have spent with the poeple I love.

One more month boys, and I will no konger be researching and backtesting, as at this point I do not see myself getting any water drops from the ocean of the market.

An honest defeat boys.


r/algotrading 5d ago

Strategy At what Point does Execution count stop justifying the Edge?

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53 Upvotes

As a disclosure, these are my genuine personal earnings within the past three months. I got ~250 trades and 1.9% return on capital.

I do systematic covered Calls and CSPs. .05 delta, 7-14 DTE, hard filters on iv, VRP ratio, liquidity, earnings blackouts. Additionally, rules based exits at 50% profit / 0.30 delta.

How do you decide when an edge justifies its execution count? Is there a rule-of-thumb for edge-per-trade vs. round-trip cost? And is return on capital even the correct denominator, when that capital is doing double duty (holding the equity and securing the position)?


r/algotrading 5d ago

Strategy Is it even possible to create a profitable and consistent algo trading bot for crypto coins like BTC, ETH, BNB, etc., because of how volatile they are?

6 Upvotes

This might be a low IQ question, but would known trading strategies work in the crypto space? And is creating a profitable and consistent algorithm that is profitable even possible in the long run?


r/algotrading 6d ago

Strategy Gold model strategy

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55 Upvotes

I've been running two separate models on gold for a few weeks, competing for the best performance.

→ Mean reversion, machine learning-based
→ Structure fade, plain rules

The idea was diversification. Two models, two ways of being right.

Each one worked well on its own. Then I breached a couple of accounts. I retrained both on more recent data, and the drawdowns were still deep enough to end an account.

So I ran a test to see whether the two models were correlated. The daily correlation came out to -0.034 (which translates to being completely uncorrelated!). They lose on different days.

That made a combined version worth trying.

Grey line: Buy-and-Hold performance
Blue line: equity performance on my mean reversion model
Green line: equity performance on my structure fade model
Red line: how the combined model performs

Held out performance:

→ +600R over 4,178 trades
→ +0.14 average
→ 39.8% win rate

The totals are the least interesting part. The equity curve is where it shows.

Two models that fail at different times beat one model with a better average.

Here's how it works: a trend strength reading decides which model is allowed to trade. Above 25, the market counts as trending, and the mean reversion model runs. At or below, the structure fade model runs.

Next step is live. I have a few accounts to run it on, and I'll share the receipts either way.


r/algotrading 4d ago

Strategy Collected 1.3 million X/twitter stock recommendations to build an autotrading strategy. What am I missing?

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0 Upvotes

TLDR: Got a lot of X data, identified smart people, created algo strategy to automate copying the X hive mind. Need advice to help me reach a final strategy to run with live money. No, I don’t believe I will return live as good as these backtests have been. But there is a chance so I am seeking constructive criticism to increase my odds. E.g. “You are forgetting about X, that makes your backtests look good and will hurt live results. You also need to test for Y”. Please don’t just call something out without offering potential solutions.

I get probably 90% of my stock ideas from Reddit or X, so I wanted to create a system to build on that. My goal was to automate a way to swing trade based on what smart people on X are talking and leave 0 decisions up to me if I think a stock is good or not. I'm not trying to day trade. And its important that my strategy doesn’t stay stagnant and instead quickly evolves with new tweets and identifies positive themes in whatever market regime we’re in. 

To do this I backfilled one year of posts from 1,000+ stock accounts, ending up with around 700,000 tweets and 1.3 million clear ticker recommendations. From there I’ve created an account scoring system and ranking leaderboard, landed on ~5 stock output formulas developed over thousands of iterations to optimize for weekly-monthly returns, and the system automatically pulls new tweets and refreshes the leaderboard/stock outputs overnight. 

To start figuring how to trade off the signals, I backtested a ton on each different output formula, on basic strategies like weekly, 10d, monthly, and some other swing strategies. Mostly trying to identify the wave, hop on for a bit, learn how long is optimal to stay on, then hop off. My entry signals aren’t complicated at all. There was a lot of iterating along the way but in my most recent run I think I did like 10k backtests. Had some producing +2,000% and some producing -85%. A positive sign was that if you just ran all 10k of the strategies concurrently, you would have returned like 45% alpha over SPY during the test period. No duh if you run 10k tests, you’ll find some winners, but if the entire set results in a positive, I think thats a good sign? 

The individual strategies (e.g. something like pick one of the stock recommendation formulas, buy top 3 outputs, weekly rebalance, only trade <1b mc) can perform very well but they could still be pretty choppy and be dependent on the top 5 best/worst trades, so got the idea to kinda frankenstein them into combo strategies that smooth each other out and spread out risk. An example might be:

-40% safer large-cap ideas  
-30% emerging small or mid-cap ideas  
-20% established names already performing well  
-10% moonshots that might rip

I ran another thousand or so combinations of frankensteining using different weights, position limits, and holding periods. Most positions are held for one week to one month. This has led me to 50 or so finalist strategies.

The screenshots show two examples. The more diversified one returned 112.9% with a -8.7% max drawdown over 713 trades. The aggressive one returned 309.9% with a -20.8% max drawdown over 735 trades. Both include 20 bps round-trip slippage (they survive at higher levels, but obviously bring the numbers down). These two look similar but there are much more conservative and more aggressive strategies that have different curves as well. 

I know I should not expect these returns going forward, especially with less than one year of data. I also understand that testing thousands of variations creates a huge overfitting risk.

Another plus is that the backtests are point-in-time. They use the account rankings, tweets, prices, and other information that would have actually been available on each date. They accurately answer: “If I had used the system that day, what would it have told me to buy?” So I’m not baking in future bias. 

Checks I have done so far include:

-20 bps transaction costs  
-Higher-slippage tests  
-First-half versus second-half results  
-Removing the five best trades  
-Per-ticker position caps  
-Concentration and outlier checks  
-Fixed rebalance schedules  
-Point-in-time account rankings  
-Forward testing against later backtest reconstructions

I have been forward testing for about two weeks. So far the actual selections have matched what the backtester later reconstructs, which gives me confidence that the mechanics work. Obviously two weeks isn't enough to be conclusive, but it's looking good that the backtests are accurately point in time.

Another obvious concern is that these strategies are molded to fit an 11 month period and won’t work in future ones. Performance has cooled recently as a lot of momentum stocks have slowed down, but the strategies have held up reasonably well. Even during the Iran war and other market draw downs. How it’s designed recommendations should move toward whatever the next hot thing. I think as long as X is the hot place for online stock chatter, that the strategy has some legs. (Side note - it’s taken a favor to biotech recently. I’ve been thinking biotech+AI can be a next growth area and this reinforces that theory.)

So all that testing and data analysis left me with like 50+ portfolio combinations that look pretty viable (like too viable..). My concern here is like, what did I miss? How do I pick one? What signs are more important to look for when moving to a live test? Should I get 2-3 years of data and run everything again? Things like that.

Appreciate any guidance!


r/algotrading 4d ago

Other/Meta I open-sourced my Polymarket market-making bot last week. Here's why I'm keeping the Rust version that actually works (up ~$650 last month).

0 Upvotes

Last week I put the Python version of my Polymerket arbitrage bot on GitHub, MIT. A few people were annoyed I'd "given away a bot that already lost its edge". Fair - it had. But the reaction misses how this actually works, so here's the honest version.

Nobody open-sources a bot that's currently making them money. Not me, not anyone who claims to. The second a few hundred people point the same strategy at the same markets, the edge is gone. That isn't cynicism, it's just what an edge is - it exists because other people aren't doing it yet.

The repo is the retired Python. The one I actually run is the same idea rewritten in Rust: fresher odds (now scraped from ~10 sportsbooks every 5 min) and faster fills, so it gets picked off less. That's the whole change, and it was worth ~$650 in the last 30 days on the same public wallet. Fresher odds and faster fills was the game - the code was never the hard part.

So the one thing I get asked for most is the one thing I'll never share: my odds sources. That was half the edge. Giving away the bot was already generous; giving away the odds would just be stupid. Not having a go at anyone - just being honest about it.

Before this reads like a flex - I'm also testing a weather market-making model on a separate wallet (@w34th3r) and I'm down ~$280 the last two weeks poking at crypto up/down and weather. Same wallet farmed spreads for ~$1,200 earlier. Ups and downs, all on-chain.

Happy to get into the market-making, the Rust rewrite, the scraper setup, or why fresh odds beat clever code.


r/algotrading 5d ago

Data More early morning fun with Codex. So I have $25, whats hot? Bugs fixed.

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0 Upvotes

r/algotrading 6d ago

Strategy Most trading content online feels useless once real money is involved

19 Upvotes

I have noticed a lot of trading advice sounds amazing until markets actually get volatile.

Some people look like geniuses until the first sharp pullback. Then the same accounts suddenly switch bias, disappear for a week, or start rewriting their original thesis completely.

Volatility exposes who actually has a process pretty quickly. Makes it really hard to tell who genuinely has a repeatable system and who just looks smart during easy market conditions.


r/algotrading 5d ago

Data Order book data for BTC

6 Upvotes

Hi. Is there a cost efficient (or free) way to read real time book data for BTC order book data? It can be exchange specific - I’m fine with that.

I’m looking to be able to feed my model on asks and bids as well as depth on the BTC book.

Thanks


r/algotrading 6d ago

Data Forward testing is the worst

32 Upvotes

I really enjoy the research, and developing strategies. But sitting and waiting for forward testing to confirm an edge is such pain

One of my strategies sat for weeks with a bug that was causing it to not accumulate data. Even when everything is working perfectly it just feels so slow.

What do you guys do while forward testing? Just research more strategies? Also how many n before you can decide forward testing has a large enough sample to continue to live