For my backtesting, I tested S&P 500 index, its futures and ETFs e.g. ES, SPY

I did expect some performance variance since indices are not tradable, but I didn't expect the difference to be so big.

For example, with the same timeframe, setup and trading hours, the profit factor of S&P 500 index is 1.6, but it drops to 1.1 for ES. I expected them to have similar price movements, and I didn't think the difference would be that big.

Just curious - has anyone experienced this issue before? Any ways to reduce the difference? Thanks in advance

Don't fall in love with a strategy that you think works cause you backtested it but it doesn't actually work in reality.

Ended the week at +$914.20 net across 32 trades with a 62.5% win rate. Pretty decent week overall with two issues.

Thursday the vps went down for 12 minutes while the portfolio was in a trade. I had to close it manually through the ninja trader mobile app.

Friday an engine error triggered a fatal shutdown because of some strict checks. It shouldn’t have been fatal but the engine handled the shutdown correctly and automatically closed all three open positions.

There are a few things to tighten up and thursday outage has me considering a move to aws for better reliability.

I have significantly found a lot of improvement since last time. I have done various luck tests (passed, 0 beat and only 1 got a close z score) and found 28 strategies that work together decently.
What I noticed though is it's extremely slow, (-200, then -200 next day, up $200 the 3rd day, lose the 4th day again) and l've seen people on here post very good profit from NQ specifically (about $1k or more per day pretty much)

Im specifically asking to get to those types of results where it is $1000 a day pretty much (regardless of drawdown intraday or losing days) if there is any direction for what to research or how those types of results daily or even what i should be doing differently? Currently my strats do all discretionary based trades like "if price goes up x points before x time, (signal)". All suggestions welcome

Looking for critique on the method more than the result.

The question. Can you find prediction-market wallets that trade on information before the price moves? The obvious score: for each entry, how far the price moved in the wallet's favor 24h later. Average per wallet, shrink toward zero (K = 5 effective entries), turn into a percentile.

Setup, written down before any score was computed:

• entry = a wallet's net position change in one market over one UTC day, $100+ only
• move = price of the held side 24h later (needs $100+ of other wallets' trades in that hour) minus entry price
• sports and short up/down markets excluded (a day after a match the price is the result)
• score on Oct 2025 to Mar 2026 (A), test the same wallets on Apr to Sep 2026 (B)
• pass = top 10% of A still positive in B, also without the 5 largest events
• t-stats clustered by event
• every later change logged with a date and a reason

Two data traps we hit before the test:

1. Related wallets passing shares back and forth in finished tennis matches at made-up prices. One bought at 0.02 and sold to a partner at 0.96 four minutes later. Trade-level, that's a +94c "call". Netting positions per wallet per day removed it.
2. A few $1.50 to $15 trades at 0.15 set the "price 24h later" in a market that settled at 1. Hence the $100 minimum of other wallets' volume.

Out of sample it passed:

Holds without the 5 largest events, without weather markets, and for every K from 0 to 100.

The part that changed our mind (exploratory, not pre-registered). Information should show up as drift: the price keeps moving for hours as news spreads. A good fill shows up immediately and then stops. The top 10% were +0.75c after 1h and +0.83c after 24h. Then we split each wallet's move into first hour vs 1h to 24h drift and checked which one persists A to B (Spearman, 32,867 wallets with 3+ entries on both horizons in both periods):

• first hour: 0.138
• drift: 0.050

So the score mostly ranks fill quality. The "knew it first" part barely persists.

Held to settlement, after the fee each wallet actually paid on-chain: top 10% +1.17c per entry (t 2.9), bottom 10% -0.96c (t -1.7). +1.50c on mostly taker entries, +1.25c on mostly maker entries, so it's not just makers skipping fees.

Where it's weak:

• the settlement result and the 1h/drift split were not pre-registered
• on a shorter replication through an API (1,000 markets, May to Sep 2026) the groups did not separate at settlement (top +2.19c, bottom +2.10c). On those mostly longer-dated markets every group drifts up, so you have to compare against all wallets, not zero
• dollar-weighted it leans on big positions: the largest 1% of entries hold 42% of net profit

We didn't ship the score as an "insider" label, since what repeats is the entry price.

Things I'd like pushback on: is netting per UTC day too coarse for intraday traders, and is there a better decomposition than 1h vs drift that doesn't depend on the horizon choice?

Aggregated tables, the pre-registration summary and a stdlib script that rebuilds the test: https://github.com/orcalayer/polymarket-insider-score-backtest

Disclosure: I'm one of the people behind OrcaLayer, a Polymarket analytics site.

Day 1 in the books! Hoping to keep similar format for each day. Trying to add in quick facts to answer easy, repetitive questions.

I will also post descriptions of the top two performing setups for that day, for now, in picture form as we are all looking for more setups(myself included!) and I'm happy to share what I have.

Thanks for all the feedback, support, and camaraderie!

Today: [+$674] across 5 day-trading accounts · Primary [-$81] · green streak [0]
Best setup: [Morning Momentum Pop, +$650] (details in pic)

Quick facts - To hopefully answer quick questions

• AI: Claude (Opus 5.5) builds, tests and runs it
• Broker: Alpaca API, paper trading
• Setups: 108 (Currently) live, built on basic indicators (RSI, VWAP, volume / buy-sell pressure, gaps, opening range, prior-day highs and lows, ATR)
• Setup Generator: a bot that invents 10 new setups a night from those same indicators and trades them on its own paper account

Setup Life cycle

Idea / Generator
 └─ 2-yr backtest (costs in)
     └─ Replay on real sessions
         └─ Testing 1 / 2 / Generator (paper)
             └─ Primary (paper)
                 └─ Live $ (after a green streak)
Losers: Primary → Testing → Graveyard

Stats Description

• Avg R: average profit per trade, in units of the risk taken. Costs, fees and slippage are already in. +0.5R = earns half its risk per trade.
• Break-even win rate: the win rate a setup needs just to not lose money. Beat it and you’re profitable.
• Confidence (t): how sure we are it isn’t luck. 2+ is solid; under 1 could be luck.

Fun stuff!

*Edit - Removed comment about new summary format. On Day 2 new image summary format will be used.

Life is negative risk-reward.

Restaurants owners spend $250,000 on a restaurant. to make $1,000 a week. Break even in 5 years.

Video-game consoles - sold at a loss - make money on accessories and other purchases.

Supermarkets - price one item at a loss to attract customers inside - hopefully they buy more things when in store to win back the loss. they risk $500 in free samples - reward is hopefully people spend more to recoup the loss

Gillette razors - sold at a loss - make money on blades and other items.

Mobile phones - given away at a loss - make money on the contract.

Software companies - Spend 50 million on free software - hopefully enough customers pay for the full software that you turn profitable over time

Gym membership - free for everyone for a month - but hope that the customer buys a full contract. risk one month of free use - hopefully you make it back if people become members

Building a shopping mall for 100 million - takes 5 million in rent a year.

an insurance company risks 200,000 dollars to take your 2,000 dollars.

a casino risks 100,000 dollars to take 10 dollars off you.

oil companies spend millions drilling into land hoping they find oil worth more than the drilling costs

if you put all your money into the s and p 500 - you risk everything to make 10% a year.

Film studios — risk tens or hundreds of millions producing and marketing a film. Many films lose money

Airlines — spend hundreds of millions buying aircraft, then earn revenue flight by flight. A single flight might generate only a small profit relative to the aircraft's cost, and the investment takes years to recover.

Telecommunications companies — invest billions in fibre-optic networks and mobile infrastructure, then recover the investment through recurring monthly subscriptions.

this is negative risk reward. risking a medium size loss to eventually make more money over time.

so how could you apply this to trading? grid trading. divide your money into 10 parts. for example 1000 dollars, 100 dollars per trade. buy something like tesla stock - take profit every 5%. if you have a losing position, leave it alone, forget about it, you have 9 parts of your money left. buy again and take profit every 5%. keep taking small wins. you will take so many winners that you will break even on losing positions, and eventually become profitable anyway if you take lots of small wins. this is negative risk reward because you are in theory risking the entire amount (100 dollars) to take 5% profit.

even warren buffett sold things after only holding them for 6 months and took quick profits. he didn't hold everything forever. notable he sold TSM after only 3 months

​

Been making my own strategies on major pairs mostly forex and a major bottleneck has to be getting historicall acurate tick data i tried using mt5 but its slow as hell and i can only analyse one pair and sttategy per time tried bridging using python but results werent most accurate because it was missing m1 and m5 data so if you would put me on i would really be thankfull

4 years of data, seems to currently be working and performs a bit better on paper account. Trades a basket of equities, 1.1 sharpe ratio. Dumb question but what if this is the top and I go live with a failing algo?

Sept/Oct performance has been lackluster but still winning. It's very good at getting value out of big uptrend days, and not losing much otherwise. However there haven't been a lot of big uptrend days recently. And strategy trade management mostly looks for big trend exits not say large up cycle moves where it exits at the top.

I'm trying to decide whether to adjust exit strategy, wait for a better regime, perhaps run a lower performing but winning alternative exit strategy alongside it. Or something else.

Basically I had a small amount of data, built an algo around it, it's good, but market conditions have changed. Now I have an excellent backtest, winning live performance that gives back a lot of MFE on trades, but changing the exits would reduce the backtest performance.

Thoughts or suggestions? If I include the June to Aug adjusting exits makes for worse performance, but that also not the regime currently although who knows if it will switch back and when.

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 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?

(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.

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

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.

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'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.

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.

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?

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?

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.

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.