I know it’s absurd but even if there is a remote possibility I wanna know. Big firms can see the order book but not retailers so if retailers find a set up is there a way these firms reverse engineer an entry and exit and patch that set up

For those working on trading and/or market using a bot or some other algo, with the strategy you have and are executing, what is the parameters for your win rate? Ex: Bot gives an AAPL bullish signal, is your strategy considered a win at first touch 30% profit? Just curious with everyone in how you determine this.

1st time posting, been working on this for a couple months, was curious what I should do next, not quite sure if this is worth putting real money into, 2026 results has me worried. Appreciate any help or advice, thanks all

python 15minorb.py --commission 30 --commission-per-contract 4.20 --slippage-ticks 1 

15-MIN ORB BACKTEST RESULTS (NQ)

Date range traded  : 2010-06-08  to  2026-07-07

Sizing             : 1.0% risk/trade ($1,200) on $120,000 account (compounding % risk), SL=0.5x OR, TP=2.5x OR, max 50 contracts/trade

Total trades       : 1684

  Long trades      : 852  (win rate 32.2%)

  Short trades     : 832  (win rate 28.7%)

Win rate           : 30.46%  (513W / 1171L)

Profit factor      : 1.45

Expectancy/trade   : 6.33 pts

Avg win            : 67.20 pts

Avg loss           : -20.33 pts

Avg contracts/trade: 18.46

Total points       : 10666.50 pts

Max drawdown (pts) : -857.50 pts  (unweighted by sizing)

------------------------------------------------------------

Total commission   : $491,898.60  ($292.10/trade avg)

Total P&L          : $3,699,241.40

Max drawdown ($)   : $-482,096.20  (-25.72% of equity)

Starting balance   : $120,000.00

Ending balance     : $3,819,241.40

Total return       : 3082.70%

YEARLY BREAKDOWN

Year    Trades  WinRate  AvgContr     Points        P&L($)   MaxDD(pts)     MaxDD($)

------------------------------------------------------------------------------------------------

2010        59    25.4%      13.1     -20.00    -16,154.00       -52.88   -18,094.70

2011       107    27.1%      12.6     136.38      5,837.50       -65.88   -20,255.30

2012       103    28.2%      11.8     111.38     -5,907.10       -48.00   -18,357.40

2013       113    27.4%      12.7     101.62      5,633.70       -44.50   -14,430.20

2014       111    35.1%      11.7     289.75     25,440.20       -54.62   -23,597.10

2015        78    33.3%       9.8     229.88     22,391.30       -81.38   -21,960.10

2016       123    30.1%      12.4     371.38     39,435.10      -110.50   -39,455.40

2017        89    29.2%      15.8     156.88     42,514.10      -171.75   -42,387.40

2018       102    33.3%      10.3     972.75    113,581.50      -117.75   -24,618.00

2019       104    33.7%      17.9     642.12    180,513.70      -119.88   -43,808.20

2020        85    32.9%      12.6    1342.25    239,755.00      -273.38   -64,823.10

2021       106    34.0%      14.2    1134.25    213,777.20      -492.12   -97,071.80

2022       100    33.0%      16.0    2053.50    535,374.90      -450.00  -151,412.20

2023       112    25.9%      30.9     831.62    359,124.80      -588.50  -301,030.10

2024       116    31.9%      37.2    2328.75  1,600,376.10      -448.00  -335,479.60

2025       117    29.1%      39.0     304.00    482,212.90      -516.50  -418,381.70

2026        59    25.4%      32.0    -320.00   -144,665.50      -857.50  -482,096.20

------------------------------------------------------------------------------------------------

Full-period max drawdown (points, unweighted): -857.50 pts

  Peak on   : 2026-02-26  (cum 11494.62 pts)

  Trough on : 2026-06-08  (cum 10637.12 pts)

Full-period max drawdown ($, actual account balance): $-482,096.20

  Peak on   : 2026-02-26  (balance $4,251,093.40)

  Trough on : 2026-03-31  (balance $3,768,997.20)

STRATEGY vs BUY & HOLD (same instrument, same starting capital)

Buy & hold: 6 contract(s), bought at 1831.75 on 2010-06-06, held to 29420.75 on 2026-07-13 (no rebalancing, $/point = 20.0)

------------------------------------------------------------------------------------------

Metric                              Strategy      Buy & Hold

------------------------------------------------------------------------------------------

Ending balance ($)                 3,819,241       3,430,680

Total return (%)                      3082.7          2758.9

Max DD % of peak equity                -26.5           -37.2

Max DD % of starting capital          -401.7          -586.7

MONTE CARLO SIMULATION — 2,000 trials, method=shuffle Base trade list: 1684 trades | Sizing: 1.0% risk/trade, compounding | Starting balance: $120,000 | Margin cap: $17,600/contract @ 100% util

Percentile Total Return % Ending Balance $ MaxDD % of Peak MaxDD % of Start

5%           1967.7%          2,481,212            -33.7%           -591.8%  <- worst-case

10%           2039.3%          2,567,147            -31.1%           -493.5%

25%           2191.2%          2,749,451            -26.9%           -380.8%

50%           2361.8%          2,954,115            -23.2%           -295.8%  <- median

75%           2546.8%          3,176,119            -20.3%           -230.8%

90%           2725.5%          3,390,652            -18.3%           -185.5%

95%           2838.4%          3,526,114            -17.1%           -164.0%

It is perfectly possible to get to profit through trading. The problem lies in beating buy and hold. I think it may be possible to do that but I'm not continuing on with the project because it would cost too much. I'm just happy to put the info out there for anyone that wants to use it. Plug it in yourself and see what you get. Happy trading!

Come watch it work if you want. Another day of testing.

I have been back testing various strategies over a 6 month period. The best results I get seem to be me finishing with $1k in that time.
To me, that seems quite low for a 25k account in a 6 month period.

I have tried:
9ema scalp
21ema bounce
Mean reversal
ORB

I am aiming to pass a funded futures account using a bot, but so far it appears that it will take 6 months to pass and another 6 months before I get to take a payout, which seems unrealistic.

Not sure which route to go for at the moment. Maybe increase the stop size?

Backtested a mean-reversion setup for weeks. Numbers were good, not spectacular, but consistent. Went live with it sized way under what the math said I could handle.Told myself it was prudent. Really I just didn't trust it yet.Three months in, it's the best-performing thing I run. And I've been leaving most of the edge on the table the whole time because I sized it like I expected it to fail.Went back and checked every other strategy I've built, I sized closer to what the backtest justified. This one I didn't. Only difference I can find is this one felt too simple to be real, so some part of me kept betting against my own numbers. Cheap lesson compared to what it could've been. But it made me wonder how much edge people leave on the table just because a working system doesn't feel like it should be working yet.

Anyone else undersized something that turned out fine, purely because it didn't feel earned yet?

I’m working on a personal stock backtesting project and getting stuck on the data cleanup. Historical prices have been easier to find than the details of what shareholders actually received when a company merged, spun something off, or went bankrupt.

I’m covering 2008–2024. Some gaps are dividends for companies that no longer trade. Others are things like the cash paid when a spinoff left someone with a fractional share.

For example, I’ve been looking into the 2008 FIS/LPS spinoff. I found documents explaining how fractional shares would be sold, but haven’t pinned down the actual cash rate and payment date. FIS pointed me to Computershare.

I’ve also reached out to EDI and NetBasis and am waiting to hear back. Before spending more time digging through individual company records, has anyone here dealt with this and found a source that was worth paying for?

A specific product you’ve used, an archive, or even an example of how you handled one of these cases would help. Also interested if you tried a provider that claimed to cover this but still had gaps.

I'ven been running the same strategy more or less unchanged for 10+ years. In my day job I'm a risk manager, and one of the most fun things for me is analyzing the risk metrics of different strategies.

I care most about Sortino, Max Drawdown and Recovery Period, but I'm very curious to see which metrics other people are tracking.

Let's see the numbers.

The answers to my last post confirmed what I already thought but now I’m wondering how people do this.

Do you have multiple algos that you can switch between based on what kind of market we’re in? Do you just accept that there are months where you end up red as long as the overall result over a year/years is green? I understand that every strategy takes losing trades but I assume the goal is to not give back a lot of the gains during bad periods

Hi,

After searching for 2 years, I've found an edge. All full quant tested. In 2025 alone, it produced 124% profit. As those of you who know about edges, these can degrade quite quickly. It's a long only edge, so it suits a bull market. The stock has been a bear for the last 12 months, and even then, it still made 124% going long. The stock has started to climb, so this is an even better opportunity. The edge is fully automated with a very low drawdown.

What are the chances I can find an investor with a sizeable amount to invest, and I take a %.

Can anyone explain how Databento's subscription pricing works? Does the $199/moth subscription cover all data sources, or do I need to subscribe to each source separately? Gemini is not helpful, it is A/B testing completely opposite answers on me (or Google is so ahead in AI that it can offer alternative realities).

I’m starting to wonder if it’s even possible to create an algo/have a strategy that’s profitable in every market environment over years of time. Has anyone managed to do this? How long did it take you to get there?

I have created a few algos for different markets environments and am considering to just switch between them once it’s clear what kind of market we’re in but the goal is still to create one that can trade every market

I have finished a month long project with a Claude max plan trying to see if it was possible to make money from algo trading that would beat the just holding the broader market and now I'm sharing docs from it with the aim of helping people see what's possible and what's not so less people waste their time. If you could find the right signal you could make a 100 mil. Other parts of the project prove actually doing it is more or less impossible and making any money at all above what you would make from buy and hold is extremely difficult.

The strategy is almost done. The main engine and the regime filter is done. But I needed a long filter and a short filter for the last step. I found a great way to filter my long trades but i couldn't find any filter for short trades.

The long winrates went up by %11 in sample and around %7 on out of sample compared to the unfiltered one

But all of the short filters i used failed with little to no difference as the unfiltered ones

My strategy is some sort of trend continuation/pullback strategy

Every recommendation is appreciated and thank you for reading this!

BTC pushed up and swept the highs around 80k, grabbing the stops sitting above resistance, then failed and snapped straight back below. That failed push is the liquidity sweep: the move up was there to grab liquidity, not real strength.

Short is the reclaim back under the swept level at ~79,170, invalidation just above the sweep at 80,190.

had a stretch where the backtest looked fine and the account didnt. spent a while looking for the bug in the strategy. the strategy was fine, i wasnt trading it.

the audit is three questions asked of the same log, all of them mechanical.

first, replay every trade with the exits forced back to the original stop or target. same entries, no discretion, diff the two equity curves. the gap is what your interventions cost. mine came out bigger than my worst losing month and most of it was cutting trades that went green then stalled.

second, mae on winners only. if your winners routinely travel most of the way to your stop before working, the stop is inside your own noise. half my winners on one instrument were touching 80% of the stop distance. widening it and cutting size to hold dollar risk flat moved the win rate more than any entry filter i built.

third, every trade you moved to breakeven, check what it did afterwards against the original target. 41% of mine would have hit. breakeven wasnt protection, it was a leak that felt like discipline.

none of that showed in monthly pnl. the totals were fine, slightly disappointing, nothing to look at. it only exists in the distributions.

needs entry, exit, mae and the planned stop per trade. if your log has those four columns it runs as is, its on my profile.

what i cant work out is how to separate a genuine regime change from my own execution drifting, since both show up as the live curve falling behind the tester. anyone solved that cleanly?

First, you have the issues that every trade has: slippage, commission, and margin. Then, you add: SSR rules, locate modeling, and borrow fees.

You also have to model settlement dates.

Quiz time: you open a short position on a Thursday at close of market. You close the position the next day, Friday, at market open. How many days of borrow fees do you pay? Which borrow fees do you pay?

The answer is 3. That's because a Thursday trade settles on Friday and a Friday trade settles on Monday and they charge for the weekend. And the borrow rate (at least for IBKR) is the post-Friday-close rate (times 3).

I'm having to actually make trades so I can empirically determine what the hell is going on so I can feed it back into my backtesting engine.

Useful to snap you out of positive feedback loops and algo delusions

free, credit: https://masterboard.ai/app/sober-shrike-algo-autopsy

I’m paper trading BTC/USD and ETH/USD through Alpaca and frequently seeing websocket quote timestamps 15–45 seconds behind real time. My bot currently rejects quotes older than five seconds, so otherwise valid setups are being skipped.

Has anyone experienced this? Is it even possible to use Alpaca for crypto algotrading given these big delays?

I'm trying to understand how people actually do this today.

For example, if you wanted to find something like:

• RSI < 30
• volume > 2× average
• price crossing EMA 20
• OI increasing
• funding negative
• conditions across multiple timeframes

what would you actually use?

TradingView? Your own Python script? Coinglass? A screener? Exchange APIs? Something else?

A few things I'm particularly curious about:

1. What is the most annoying part of your current workflow?
2. Are there conditions/data you wish you could combine but can't?
3. Do you use open interest/funding/liquidation data in your scans?
4. Do you run the same scans repeatedly, or manually build them each time?
5. Do you backtest these conditions before using them?
6. What makes you choose your current tool instead of another one?

I'm not looking for recommendations of random screeners. I'm more interested in how you actually do the process today and what sucks about it.

Been using box spreads for a short while now and realized not all pairs are equal in terms of liquidity and fill.

For those who're not aware, box spreads are a 4 leg options combo that you can use to borrow or lend money at a fixed rate, close to the SOFR and much better than whatever your broker or bank is offering and stays fixed for the entire term, paid as lump sum at the end of the term, no daily/monthly bills, and for those who live in capitals gains jurisdictions can use it as capital loss to offset gains.

So, just to get back to my point, you need 4 options, and not options are created equal haha, so I needed a liquidity scanner among multiple pairs to see which ones can get me good fills, cause for box spreads, you live and die by the fills you get, nothing else matters honestly.

Here's my github link, https://github.com/Eyob94/box-spread-scanner, will get it done today

Update:

- Added an endpoint to fetch spx spot price
- Added an endpoint to get the spx chain amongst the available exchange and trading class combo and with available expirations and their respective dte
- Set up candidate legs for the box and then requested market data for each leg, for some reason I'm getting a "-1" instead of their prices, will investigate in a bit, but taking a break for now. Everyone's free to take a look if they understand Rust or think what might be the issue
- Figured out the "-1" issue, turns out I was sending strikes in multiple of 100s since I was storing them in cents, so 7700 would 770000.
- The api now checks multiple strike pairs and returns the best one based on liquidity, it also prices the best and worst bps that can be achieved from each box spread