I know backtesting does not imply future profits, but I am very excited

I took 156 markets listed on both Kalshi and Polymarket with the same settlement rules, and priced a 1,000-contract buy three ways against the live order books, fees included.

Extra cost per $1,000, compared with routing across both:

• Kalshi only: +$24.51
• Polymarket only: +$6.80
• Always the cheaper venue: +$0.69

Where routing really pays is size on thin books. 10,000 YES on Inter Miami to win the MLS Cup cost $2,173.52 routed, vs $3,297.77 on Kalshi alone and $3,310.31 on Polymarket alone. On its own, each venue’s book runs out and the price climbs. Split across both, it stays low.

So I built a smart order router that does this automatically. It reads both order books, adds each venue’s fee to every price level, and fills your order from the cheapest price first, whichever venue it’s on.

The smart order router is available here: github.com/Synpath-ai/synpath

Disclaimer: Not financial advice. Educational purposes only.

I work alone and rarely get to share the mini achievements that each day's dev brings, I find this a real shame.

So I am curious what are you building today? Or what was the latest thing you built which you're proud of that no one got to hear about?

For me, today im working on improving my display/GUI for certain features to make live monitoring easier.

Do you think it's okay using Metatrader's api for fetching historic data? Has anyone noticed discrepancies? Isn't it supposed to be the actual numbers you'd get trading with that specific broker?

Mother Superior has been running live for roughly two weeks. It is a short-term XAUUSD scalper/intraday EA built from eight separate modules, with an integrated recovery layer. The first nine traded days are currently at +12.22%.

It has already traded through a few red-folder news events and behaved well so far. Still, two weeks is two weeks. The first genuinely ugly market regime will tell me far more

than a green opening run, so we’ll see what happens.

I’ve spent roughly three months building the system. Each module has its own entry logic, Magic Number and basket management. The shared risk layer lets me limit simultaneous positions and includes optional equity protection.

Only 2022–2024 was used for development and parameter selection. The final system was also challenged on the history outside that window:

• 2017–2021: earlier untouched survival/stress history

• 2022–2024: development and parameter selection

• 2025–2026: formal frozen-parameter OOS

That gives seven calendar years outside the development window. I keep the older survival/stress years separate from the formally declared OOS period, but the same final structure survived both sides of the development sample.

The main continuous 2021–2026 real-tick report used a fixed 0.01 first lot with no compounding. Starting from $1,000, realised balance finished at $3,903.12 across 1,511 closed trades. Profit factor was 2.93 and every tested calendar year was positive. The number I care about most is the observed MT5 maximum relative equity drawdown: 12.82%.

For the Monte Carlo check I resampled the complete deal history 10,000 times in 50-deal blocks. This keeps some local clustering instead of pretending every trade is independent. Median ending balance was $3,896; the 5th–95th percentile range was $3,504–$4,318. Median maximum realised-balance drawdown was 4.1%, with an 8.0% 95th percentile.

The Monte Carlo only rearranges realised trades. It cannot reconstruct every floating basket or invent a gold regime that never appeared in the source data. That is why I still treat the observed 12.82% MT5 equity drawdown as the more relevant risk figure.

How much live history would you personally require before giving meaningful weight to the untouched tests and bootstrap results?

Lots of people go ahead and point out 'as much as possible' 'as much as you can really' 'at least (some amount of years that is gigantic)'. But what does the answer turn to when you consider an algorithm that trades across much shorter distances and scalping algorithms that go in and out of trades after 5 minutes? Depending on frequency, too. I would assume its important to test out all possible markets, crashes, etc... If youre trading so quickly and at such a small length of time, market conditions that show results after a full day essentially wont impact you at all, or so i think? Im not too experienced so any advice is much appreciated.

(and no i dont mind downloading years of data i just want to know if at some point it becomes redundant)

Does anyone have clean, high-quality historical daily QQQ constituent weighting data covering at least the last 12 months that they'd be willing to share?

I haven't found a high-quality dataset at an affordable price. Getting NDX weightings directly from Nasdaq comes with a nosebleed price tag. Thanks!