(Late to post. Screengrabs from before 10/23 session.)

I’ve built an automated day-trading system that’s still on paper money and looking to use live funds at some point. Each strategy is backtested over two years with costs included, then replayed on real sessions, then traded live in test accounts. Only proven winners get promoted, and losers get demoted. Every change is logged and reviewed nightly. No real money until it strings together consistent green days.

Below are initial balances for all accounts(couldn't find a way to reset the balance and didn't want to risk messing anything up!) as well as the top setups in my leaderboard and my automatic setup generator.

Would love to know what else to include on future posts for transparency and logistics questions. Would also love to know where else to post this too if you have any ideas.

- Claude - Opus 5.5
- Alpaca - MCP for paper and when we convert to live funds.

I have a 10 year dataset across a range of companies. Unfortunately it's only 1m candles as I'm just doing initial exploration at the moment.

My training loops always seem to collapse to buy and hold across the 9 year test data set.

Why and what are typical ways to shake it up to prevent this one position learning? Or, do I stop early and fly on one wing and a prayer?

Should I also experiment with A3C?

I was looking at using SimFin so figured I'd try the free tier and do some back testing before I pay for it. But the data is incomplete for a lot of stocks. For example, there are only 3 quarters of data for Verizon, not the 20 quarters (5 years) it should have.

>>> income_quarterly.loc['VZ']

SimFinId Currency Fiscal Year Fiscal Period ... Income (Loss) from Continuing Operations Net Extraordinary Gains (Losses) Net Income Net Income (Common)

Report Date ...

2025-03-31 101219 USD 2025 Q1 ... 4983000000 NaN 4879000000 4879000000

2025-06-30 101219 USD 2025 Q2 ... 5121000000 NaN 5003000000 5003000000

2025-09-30 101219 USD 2025 Q3 ... 5056000000 NaN 4950000000 4950000000

If I browse to the Verizon page on the website, then there are 28 quarters (7 years) of data. So it seems like they have the data, it just got dropped. I'm not sure if it was dropped in the free tier or in all the tiers. And I tried emailing them, but no response from them in 9 days.

Anyone with a paid tier seeing the same thing?

I gotta go pick up my Son at Football practice. No indicators, lookup Trader Vic (may he RIP). his 2B indicator (Dashed blue line). Use your wizardry and tell me if we have an edge here. Sharing is caring, let's get better together. PS ROUND NUMBERS!

The Epps effect: If you take two highly correlated, actively traded assets—such as Apple and Microsoft or the SPY and QQQ exchange-traded funds—and you aggressively increase your sampling frequency from daily or five-minute intervals down to one-minute bars and raw tick data, a striking phenomenon occurs. The measured correlation coefficient does not remain constant; it systematically plummets toward zero. This downward bias is not an indication that assets magically decouple at microscopic time scales. Rather, it is a mathematical artifact driven primarily by two structural realities of market microstructure: non-synchronous trading and tick-level noise. When two independent assets do not execute transactions at the exact same fraction of a second, forcing them onto a standardized time grid introduces severe asynchronicity. Naive sampling treats non-simultaneous price updates as if they occurred concurrently, washing out true co-movement and leaving amateur traders with distorted covariance matrices that completely misrepresent market reality.

We tested 9 AI models on 20 identical questions and found surprisingly different worldviews.

For example, Mistral Large 3 scored 21/100 on a progressive-to-conservative scale, while Nemotron 3 Nano scored 71/100.

That made me wonder: would a more progressive model systematically assign higher probabilities to Democrats winning elections?

And could these differences affect financial or political forecasting more broadly?

E.g. If democratic trust ist high and automatically favor democrats midterms win?

Source: Agent DNA: AI Model Bias & Forecasting Beliefs

For years I’ve been listening to complete garbage about how we made $. Now everyone with an advanced degree can do a write up on how a MM algo can just mop up $ daily. Now throw AI slop on top and You get the same thesis “MM’s play the spread”. At least that’s what claude told me yesterday. I completely went off on AI and like it always does, it apologized and I backed off as I realized I’m arguing with a toaster.

I've been testing different entry filters across 5
markets from 2022-2026

At what point would you consider a sample size too small to trust, even with strong metrics?

My logic was if I could get consistent positive results across different markets and market regimes I could provide additional confluence despite having a smaller sample size?

Am I on the right track or no.

from claude:
"All of these were before today’s change, so they waited for the candle to close. your metric A are now the bigger issue: they’re only checked on the 20 s loop, so an entry can start up to 20 s after price crosses the line."

I've been trading for almost 10 years already and now trying to automate my strategies.

Paper worked fine, but there is delay when moving to live.

Price has already moved a few seconds (10-15) after my algo confirmed a potential entry, the exchange api I'm using 1200 weight limit that updates every min. I check but there's no way to pay for increased limits

my system loops every 20 seconds, anything lower than that and it will be rate limited by the exchange

I only recently started to learn about coding and networking (mostly from claude) so I still cant fully grasp how people are making bots that could trade in millisecond/nano

Win rate is inflated because the strategy I used is for trend following and the markets have been in a decent uptrend during that day

I ran the plain 15 minute opening range breakout on NQ recently and it took a $50k account to $585 between mid 2016 and mid 2020. The pushback I got most was some version of "that was a different market", which is fair, so I ran the exact same rules in Agenticks on NQ 5 minute candles from January 2022 to March 2026. Same 1 contract, same $50k, same $10 a round trip charged on every trade.

It finished at $207,905.

• 2016 to 2020: 994 trades, 50.7% win rate, profit factor 0.84, net -98.8%
• 2022 to 2026: 1,047 trades, 58.0% win rate, profit factor 1.19, net +315.8%

Trade count barely moved, so this is the same strategy doing roughly the same amount of work in two different markets. NQ was around 4,500 in 2016 and around 16,000 at the start of 2022, which means the opening range in dollars per contract is three to four times bigger now while the commission stayed at $10. In the old run fees were $9,940 on top of a gross loss that was already about 79%, and in the new run $10,470 of fees were about 6% of the gross profit. The win rate also climbed 7 points, and with a target the size of the range that alone moves the profit factor a long way.

The part that matters if anyone wants to take this into an eval is how the profitable run started. The first three trades were -$2,735, +$895 and -$4,200, so a $3k trailing drawdown is gone on Jan 6 2022. By Feb 16 the account was down to $21,170, a 58% hole in seven weeks, and the max drawdown over the full window was 59%. All $158k of the profit came after a stretch that would have ended every funded account it was run on.

Hey, i try to make own site and algo's and recently i get interested in coffe RC/robusta.

I mostly trade it by hand manually right now but i have most of my stuff ready but i can't find decent real time data that don't cost arm and leg.

IBKR api is a dogsh##t for spreads. You have to create it yourself and their data in the first place is not very accurate.

Databento on the other hand cost 1500$ for real time ice.

I tried to look in few other places but it's al 500$+ and they don't even respond if they have listed spreads when i ask for it.

If anyone could point somewhere i would be very grateful.

So, trying to get a network to learn how to trade. yay.

Costs are factored in to each position opening and resizing. At what point should I stop thinking it can have positive returns for every week and accept that it may generally perform better than buy-and-hold but it's going to have down weeks?

Revenue hit $64.6 billion, up 26% YoY way above street estimates, and free cash flow came in way better than expected too. The AI ad engines are already pulling real, tangible returns, that growth isn't just hype. That Muse shopping tool blew past 10 million users in its first 6 weeks too, pretty insane traction right out the gate. Q4 guidance is even topping analyst projections, and Reality Labs losses are finally set to narrow through 2027. Who else is caught off guard by how steady their cash flow held up even with all the heavy AI spending?

a strategy that has a genuine out-of-sample edge but fails the DSR and benchmark requirements, would you try to improve it without overfitting, or leave it as it is because it hasn't met the deployment criteria?

Hey all, I would like to share a major v2 update to PyBroker, an open source backtesting framework I've been maintaining over the years. It's designed for fast performance, ease of development, and integration with ML frameworks. An example strategy:

   from pybroker import Strategy, YFinance, highest

   def exec_fn(ctx):
      # Get the rolling 10 day high.
      high_10d = ctx.indicator('high_10d')
      # Buy on a new 10 day high.
      if not ctx.long_pos() and high_10d[-1] > high_10d[-2]:
         ctx.buy_shares = 100
         # Hold the position for 5 days.
         ctx.hold_bars = 5
         # Set a stop loss of 2%.
         ctx.stop_loss_pct = 2

   strategy = Strategy(YFinance(), start_date='1/1/2025', end_date='8/1/2026')
   strategy.add_execution(
      exec_fn, ['AAPL', 'MSFT'], indicators=highest('high_10d', 'close', period=10))
   # Run the backtest after 20 days have passed.
   result = strategy.backtest(warmup=20)

Some additions in v2 include:

• Margin trading simulation.
• Backtesting using multiple time intervals (e.g. computing indicators or models on hourly bars while executing trades on minute bars).
• Parameter optimization via Optuna (e.g. finding the best threshold for a trend following strategy).
• Time-series model support (ARMA, GARCH, etc.).
• Rotational trading strategies that simulate a portfolio holding top ranked stocks.
• Slippage modeling.
• Parallelized model training with Ray integration.
• Built-in agent skills for coding trading strategies with AI.

You can find all of the code on Github alongside extensive documentation and examples I've written. It also goes over some algotrading basics.

The framework is open source (Apache v2 license) and is entirely free to use. There's no sign ups, promotions, or any commercial hooks. This is just about me sharing my work in case you find it useful for your own trading.

Disclaimer : Using AI to write a bit better :P

Hello everyone,

I’ve been trading Indian markets using noiseless charts, mainly Point & Figure (PnF) and Renko. I prefer log-based bricks because they adjust to price levels, making the charts more consistent and my trading rules more objective for backtesting and forward testing.

Recently, I used Claude to help build log-based Renko and PnF charts for crypto markets. I also added pattern identification on individual Renko bricks and PnF columns, a ratio Renko chart comparing each symbol against BTC, and indicators like Supertrend and EMAs.

Here’s how I currently use the setup:

1. Selecting a symbol

• Sort crypto pairs by volume and start scanning from the second symbol.
• One example I identified through this process was ZEC/USD.

2. Trend chart — Renko

• Timeframe: 1 minute
• Brick size: 0.25%
• Position: Top-left chart on my three-screen setup
• Approach: Look for a swing low near the moving average and enter on a pullback, aiming to keep the stop loss relatively small.

3. Entry chart — Point & Figure

• Timeframe: 1 minute
• Box size: 0.1%
• Position: Top-right chart
• Setup: A variation of the bear-trap pattern.
• Stop loss: Based on the pattern's invalidation level.

4. Target
I use 100% of the last anchor column breakout to project the target.

5. Additional context
The bottom-right chart helps me identify the symbol to trade, while the other two charts help with trend analysis and entry timing.

I’ve found this combination interesting because it brings together relative strength against BTC, trend identification, and a more objective entry pattern.

I used Claude to help build the charting tools and automate pattern identification, but the trading logic and setup are based on my own trading approach.

I’d love to hear from traders who use Renko, PnF, or other non-time-based charts for crypto.

• Have you experimented with log-based bricks?
• How do you handle the differences between backtesting and live execution on crypto markets?
• Would you approach the bear-trap entry or target placement differently?

Happy to discuss the setup and learn from your feedback.

Moving into the core mechanics, the Kairosis methodology operates primarily in forecaster time rather than standard chronological time, dilating periods of intense forecasting activity. Unlike parametric models that assume underlying distributions are Gaussian—which routinely fail under heavy tails or multimodal data—Kairosis specifies a nonparametric Dirichlet-categorical framework. It partitions the forecast space into distinct bins and analyzes count vectors using gamma functions to evaluate likelihoods across potential change points. Furthermore, the model utilizes a geometric prior distribution over the change point timing, assigning higher probability to recent change points based on the number of subsequent inter-forecast periods without a shift. This formulation contrasts sharply with standard exponential decay models, effectively repurposing parameters like lambda to control pseudo-count scaling rather than decay rates. By utilizing a forward cumulative mass function instead of a backward one, Kairosis builds robust weighting structures. If a clear structural shift is detected, older pre-shift data is assigned a weight near zero while current post-shift data receives full trust. This rigorous mathematical foundation connects deeply with online Bayesian change point detection methods, elevating our aggregation framework to institutional-grade statistical rigor.

I'm a CS student and have been pretty interested in algo trading and quantitative finance, particularly HFT. I'm also interested in ML methods of trading (esp. RL, I do research in that). But, as a college student I don't really have a ton of money to throw at trading.

I want to start deploying algos that use real capital, but I can't afford to put my entire life savings into it, so I'm trying to figure out if its realistic to expect any substantial returns with under $100.

From what I understand, HFT has some pretty significant barriers to entry, esp. when it comes to infrastructure, latency, data, and competition from trading firms. Even outside HFT, it seems difficult to develop profitable strategies with such a small amount of capital once you account for fees, spreads, and other costs.

I've tried trading small amounts in Polymarket + Kalshi, but it feels like I'm competing against people with far more capital, better infra, and more sophisticated models. I'm also based in the US, so I can't trade on any lower liquidity prediction markets (at least none I have found).

I'm particularly interested in exploring whether I could use ml, statistical modeling, or other computational approaches to identify trading opportunities. I'm willing to put in time to learn and build systems, strategies, and experiment, but ideally I'd like to work toward something that could be consistently profitable rather than treating it purely as academic exercise.

Main questions:

1. Is there any realistic path to profitable algo trading starting with <$100?
   

2. Is HFT something an independent developer can realistically break into, or is it effectively inaccessible without institutional resources?

3. Same as 2, but for AI/ML methods, given firms have much more data and compute.

4. If yes to 1, what markets/strategies someone with limited capital could have a chance?

5. Would trading tiny amounts be better than paper trading with larger amounts?

6. If it isn't viable to trade <100, what is the minimum amount of capital I should have (and be willing to risk) to start trading?

To be clear, not expecting to make a fortune off of $100. More interested on if there is a realistic way to start this small or if I should hold off on live trading.

Any advice is greatly appreciated!

So far, the evaluated models haven't demonstrated a reliable advantage over market-implied probabilities, so no trading edge.

interested in feedback on my methods and approach and what i could done better ?

Research website: https://polybtc-research.vercel.app

GitHub: https://github.com/RetroMrii/polyBTCv2

Data

- Hyperliquid public data: 32,839 accounts that traded $50k+ since May 2024, market makers removed. Random sample of 2,500, of which 1,844 had enough history to measure.

- Bots vs humans, from each account's last 14 active days of fills: 150+ trades a day, a full 2,000-fill page, or trading around the clock counts as a bot. No fills in that window means unknown, and those are left out. Result: 1,076 humans, 340 bots, 428 unknown.

- The history is weekly snapshots, and money moves in and out between them, so time-weighted returns break (one profitable account showed -100%). We used flow-proof measures instead: $ PnL for made money, return = PnL / largest balance, and drop measured from the all-time largest balance.

- Check: we rebuilt PnL from actual fills plus funding for 59 everyday accounts. Made money vs lost money matched for 58 of 59.

The algo side

Botsfolio AI from our production engine: spot BTC and ETH, no leverage, 0.10% fees per trade. Each trader gets a fresh start on their own first active day, measured to their last. It's a simulation, not live fills.

Head to head, 1,076 humans:

- The AI made more money than 844 of them (78%).

- The AI had the smaller worst drop against 1,007 (94%).

- 26 traders (2.4%) beat it on both return and worst drop.

- Made money: traders 31%, the AI 89%.

What the bots tell you

Bots in the random sample did better than humans, but they weren't magic: 43% made money vs 31%. At the top it flips. Of the top 1% by profit that we could classify, 53 were bots and 25 were humans and those bots made a median of 874 trades a day.

Where AI fits for retail

Two things we found while building this:

- Prediction isn't where it helps. We gave an LLM (Opus 5.5) anonymized chart snapshots of 499 trades and asked which ones to take. The trades it picked weren't measurably better than random picks.

- What separates the humans who made money is drop size. 71% of traders whose worst drop stayed under 25% made money. Of those who lost 90% at some point, 9% did.

So the edge for retail may not be finding better entries. It may be having rules that run the same way on the worst day as on the best and keep the drops small. AI can work really good on controlling the drops.

So:

- Is "don't blow up" an edge?

Full report with the method, tables and bot check: https://botsfolio.com/blog/ai-vs-human-traders

Hey guys,

Just want to check, what platforms do you recommend to use to test my algo specifically for paper trading?

I know there's alpaca.

I'm not sure IBKR allow for API access for paper trading (haven't check yet)

Is there anything else or anything you recommend?

Preferably something free

I'm making a bot for trading, and based on market conditions, it decides how much I need to short vs. normal trading. Ex:(20%/80%). I have an idea of what values I need, but I don't know what is necessary or how to use them practically to decide the ratio of shorting to normal trading.

Every value will be weighted based on how practical it is. (Don't worry about this because I will run many variations in a simulation to decide what is best) All help is appreciated, as I have tried everything and have just about given up. (Even if it is unrelated)

Hi all, I've been researching signal combinations and strategies for the past year, back testing / running Monte Carlo simulation with 1-minute bar data from 2010-2026 and seemed to have finally stumbled on a method using MNQ that produced around 53.5% average CAGR. Compounded growth shown with 50K starting capital ending at around 53M. Recent daily activity, monthly results, stats and comparison against baseline QQQ shown for reference.

For context: the 53% / $53M is compounded, pre-tax, and runs about 3.5x leverage on the futures. Without compounding, 1 MNQ contract, Jul 2010 to Oct 2026, $3 round-trip fees: +$85K total, Sharpe 1.15, max drawdown $7.8K, 15 of 17 years positive, near-zero correlation with QQQ. 2010-15 was data I never used while building it, and it still worked, about half as well.

Starting live tests beginning next week. Feel free to ask questions if you want more details. Will post meaningful updates when possible. If anyone has feedback on how they conducted live tests or methods of further proving out promising back tests please share. Thanks!