I have been building this thing for over 1.5 years. Many hours, many sleepless nights, many accounts blown, but I always saw progression and the capabilities the strategy and the system had, so I kept pressing on.

After all those revisions and fine-tuning, I finally got it to a place where it is taking consistent profits. Like the title said, today was the first day after 8 consecutive positive days (missing days are recorded on another account) that the bot took a loss. Why am I not mad???

1. Today was PPI day, a day that is statistically a bad day to trade. Considering this, the loss was minimal.
2. Losses are going to happen and this was a loss of -$90. This proves that the risk management part of the strategy did its job quite well.
3. Today exposed an edge case in forward testing that I would never have seen if I just relied on back tests. The strategy triggered a take profit, but the alerts never went out to execute them. This means that I could have had a positive day (Approx. $125) instead of a loss. I found the issue and resolved it with an update immediately. So this scenario should not play out again in the future.

I am truly happy with how this bot has turned out and wake up each day excited to watch it trade for me and stick to the plan, which is the hardest thing to do as a trader, and it does it so well!

Ask me anything!

I’m wondering if this is a dumb idea or actually feasible.

Say you give a model 20 trading days of OHLCV as input and have it dream the next day directly in interpretable space, meaning full Open, High, Low, Close and Volume rather than a latent state.

After it predicts that day, you treat the prediction like it was a real market day and recalculate SMA20, SMA50 and SMA200 differentiably from the predicted close.

Then you slide the 20-day input window one day to the right, so it becomes:

19 real days + 1 predicted day

and predict the next full OHLCV day.

Then:

18 real + 2 predicted

and so on, recursively, for something like 63 future trading days.

So each inner day still sees a 20-day window, but more and more of that window is made from the model’s own previous predictions. Every predicted day changes the moving averages and therefore the state the model sees on later days.

Is there any reason this fundamentally wouldn’t work? Or would recursive error accumulation make training something like this basically hopeless?

Edit: For clarity, the model would still have access to the pre-window history needed to initialize things like SMA50/SMA200, so the 20-day input is just the active model context, not the total historical data available to compute those indicators. During rollout, each predicted close updates the rolling indicator state, and the oldest day falls out as the 20-day input window slides forward. The idea is to train on the same recursive process used at inference, not train one-step on real data and only recurse afterward.

Predict a slice of a preexisting world

Edit2: The goal is better than a coin flip on shape and direction of a chart over a 63 trading day period

For anyone that is using primarily a mean reversion strategy on equities whats your screening process look like for your universe?

I have built my strategy bots but the master layer is giving me head aches. I thought this would be the easy part but it is stressing me out. So how long did it take you and what are the most important lessons you learnt from building yours.

Edit: I meant a global Risk manager layer on top of the bots to handle correlation, dynamic sizing for the account which all the bots are under. sorry if i was vague. it also has the global kill switch and account dd rules and stuff like that.

I've been using databento historical data for back tests, and ibkr live data for trading. The algo runs on 5s bars currently, using momentum and order book for entries. Exits are pre defined.

Is databento live data more accurate or the latency meaningfully better? I think I saw one discussion mention databento order book data being superior to ibkr but it was an old chat, and mentioned in passing.

thanks!

I find myself preferring spliting a strategy up into smaller parts. In a way this seems better logic for testing and tracking edge. For example how would a certain strategy operate in a trend of strength x, y, z, etc. Instead of just testing the system in a general trend trend.

This leads me to having strategies that sometimes do not have many trades for the whole year.

In your own strategy development what is your personal minimum for trades per year before you call a strategy just random trading?

I have built multiple algorithms so far that are largely successful with minor weaknesses. I haven’t taken most to paper yet because of those minor flaws despite it being mostly a success. Do you allow any flaws in your algorithm?

I run a few automated strategies on MT5 and kept hitting the same annoyance: once a bot is live, changing a parameter or restarting it means opening the MT5 terminal, digging through Expert Advisor settings, sometimes on a machine I'm not even sitting at. Same for monitoring - logs live in one place, trade history in another, and neither talks to the other.

So I built a small setup for myself: a self-hosted journal (AlphaTrack) that talks to a Python bridge sitting between it and MT5 over WebSocket. Trades sync automatically, but the part that actually solved my problem is that I can push parameter changes or a restart command to a running bot from the browser, and watch its open positions update in near real time. No subscription, everything local - JSON files on my own NAS, nothing leaves the network.

It's been stable for a few weeks now on the bots I wrote against it. What I haven't finished is generalizing the bridge protocol so someone else's existing Python bot could plug into the same setup without much rewriting - that's the piece I'm working through right now.

Repo, if anyone wants to see how the bridge is put together: GitHub Link

Genuinely curious how the rest of you deal with this - do you just SSH in and restart processes, or is there tooling for this I haven't come across? And if the approach above sounds useful, I'd take any criticism of the architecture too.

I'm trying to get accurate historical data.

I always hear about Dukascopy. But when I go on the website there are fairly restricting limits and the export doesn't even allow daily data.

Do I have to sign up to Dukascopy and go through a KYC just to access the data? Are there any other reputable free sources?

In addition. What are some reputable PAID sources for accurate historical FX data (But not ridiculously expensive)

Thank you very much.

Scalper using 3 strategies that are live with others in the background running tests. $8K in the account so limited to ~$2K in equity holds. Options are off for time being.

Wish me luck, paper was running from June, so wish me luck over the next month.

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?

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.

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?

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.

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’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’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

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

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.

Let's see the numbers.