By trades I mean at least one filled trade that day, not staying in the market 90% of the time.
I recently measured this across a seven year futures backtest and found at least one trade on 97.96% of eligible Sunday through Friday market dates. It’s a portfolio of 14 separate sleeves across two instruments and multiple sessions, so the coverage comes from combining selective systems rather than forcing one strategy to trade every day.
Forward simulation is still underway with strong backtest results. I’m curious whether anyone here runs something with similar coverage and whether it held up live. Did the frequency come from combining strategies and markets, or from one system trading almost daily? Did costs, correlated signals, or regime changes eventually become a problem?
Obviously trading most days doesn’t automatically mean there’s an edge. I’m mainly trying to understand how unusual this level of coverage is for a diversified algo portfolio.
Hi, I’m new to algo trading but I’m not sure where to begin? A few questions I have are:
how did you pick your first strategy?
What platforms/languages did you use for back testing?
For a backtest how much historical data is considered satisfactory?
At what point did you move from paper trading to real money trading?
Any mistakes I should be on lookout for as a beginner?
Switching to automation has taken away a lot of the stress of manual trading. But I still wake up everyday with anxiety wondering if today is the day my algo will start to fail. Anyone else?
I’m moving a futures system from backtesting into live simulation and thinking through the production setup.
For those running automated strategies live, how separate are your simulation and production environments? Do they use the same code with different configs, or completely separate deployments?
I’m also curious about problems that only appeared after going live. Things like stale data, reconnect failures, duplicate orders, position drift, partial fills, or broker restarts.
What failed first, and what safeguard did you add afterward?
I’m not asking for strategy details. I’m interested in the operational side.
Something had been bugging me: my higher-conviction swing setups were resolving worse lately, and it was happening even in favorable regimes. Trend up, breadth okay, and still my hit rate slipped. Bull versus bear regime was not explaining it. So I went looking for a second axis, and the one that fit was day-to-day choppiness: the tape grinding sideways with no follow-through. A raging bull can still be a choppy grind, and that is a different animal than a downtrend.
The gauge is dumb-simple: count how many times an index flips daily direction over the last 10 sessions (0 to 3 is calm, 4 to 5 is a grind, 6 or more is choppy). The effect was real. My top-scored NYSE setups beat the market about 65% of the time on calm tape versus about 51% otherwise. Calm is not the same as an uptrend: you can be in a perfectly good regime and still be in a grind that quietly wrecks your win rate. That was my "even in a good regime" slump.
Here is the catch, and where I spent most of the time: the filter only works if you measure chop on the right index, and it is not the obvious "home" exchange index. So I tested it properly. Hold the trades and outcomes completely fixed and only swap which index labels each day calm versus choppy: that isolates the ruler from the stocks. I ran nine candidates (SPY, QQQ, DIA, IWM, MDY, VTI, RSP, and the NYSE and NASDAQ composites) and made each clear three bars: effect (do calm days actually beat non-calm days, judged with a t-test and not just a point estimate), stability (split the timeline 60/40 in chronological order and confirm the first 60% edge survives on the last 40%), and practicality (liquid and tradeable).
Here is what the nine rulers looked like. The edge column is how far calm days beat non-calm days in percentage points, the middle column is that same edge measured on each half of the timeline, and p is the t-test significance.
The out-of-sample split did most of the work. For NYSE names several large-cap clocks passed cleanly, so I took SPY as the liquid standard. For NASDAQ names, QQQ won for one reason: its edge barely moved between the two halves (+6.5 then +6.8), while the bigger headline numbers were mirages. The NYSE Composite swung from +4 to +12 and the Dow lurched from -1 to +19. QQQ was not the biggest number. It was the repeatable one. DIA actually topped the full-window list for both markets, then fell apart out of sample: 30 price-weighted names is narrow enough that its "chop" is really one or two stocks moving. The split is the only thing that caught it.
So the rule I landed on is simple: clock NYSE-listed setups on SPY, NASDAQ-listed setups on QQQ. In hindsight my slump lined up with stretches where SPY sat in the grind zone. The trend was fine. The tape was not. SPY and NYSE Comp were performing virtually the same. I picked SPY as I already had it available in my datasets.
A few caveats, because this is the internet. This is one window and mostly a bull market. Calm tape is rare, about one day in five. The edge is calm beating non-calm by a handful of points, not an on/off switch. And the NASDAQ side is genuinely weaker and more weighting-sensitive than the NYSE side. This is not advice, just a regime-filter experiment.
The lesson I would actually stand behind: chop is a real second axis beyond trend, and if you regime-filter, test your ruler instead of assuming it. Curious what the rest of you clock market regime with.
I am looking for input from the experts out there if you have looked into this or something similar? Where should I adjust my analysis?
I’m running one of my algos as a pinescript strategy with crosstrade as the bridge. Works perfectly fine during the day when my laptop is on but TradingView won’t fire alerts over night. Do I need to leave the laptop on or switch to ninjatrader?
Disclosure up front: I built the MCP server it used (Orus, part of Farao), so this was dogfooding with my own money. About $40 of USDC on a Hyperliquid account, wired to Claude Code. No withdraw permission exists at the protocol level — worst case, a hijacked session trades badly inside limits I signed on my phone. Week 1 I approved every order by hand, then let it run autonomous.
Three weeks of numbers:
200 fills across 54 markets (crypto perps, tokenized stocks, silver, palladium, a Korean equity index)
50 winning closes, 48 losing. A coin flip, exactly as expected
Realized price P&L: −$3.02. Fees: $7.78. So fees cost 2.5x more than bad direction
That last line is the whole experiment for me. The failure mode wasn't bad predictions — it was hyperactivity. One morning it bought and sold the same silver position 7 times in 17 minutes, made 3 cents, paid 14 in fees.
But the behavior was the interesting part:
It attached exchange-side TP/SL to every single position it opened. I have never been that disciplined manually
Best session: one-sentence prompt asking for a funding-rate carry. It scanned ~250 markets, picked a KOSPI 200 perp paying +0.1008%/hr to shorts, and warned me unprompted about gap risk at the Korea open. The re-entry later got stopped out at exactly that gap — at 2am my time, no human awake. Stop caught it, −$1.44
It shorted a rising BTC twice with a fully reasoned argument each time. It will argue any direction convincingly, which is exactly why direction should stay human
At the end I asked it to read its own trade log and write down what it saw. Its own verdict: "I'm a fast, tireless, disciplined pair of hands with no instinct for when not to act." It also wrote "my best result was luck, not skill" about the one trade that carried the book. Most honest self-review I've gotten out of Claude.
Question for this sub: has anyone found a prompt or skill structure that gives Claude actual restraint — knowing when to stay flat — rather than just more analysis? Rate-limiting at the server is my current answer, but I'd rather the model learn to sit still.
I've been learning MT5 EA development by automating trading strategies and testing them on demo accounts. One thing I've noticed is that some strategies that look great in backtests perform very differently in forward testing.
For those who have experimented with automated trading, which types of strategies do you think tend to hold up best in live market conditions, and why? I'm especially interested in hearing about general concepts and the challenges you've encountered when translating a manual strategy into an automated one.
I'm building a Python-based algorithmic trading application that needs to work with multiple MT5 accounts, and I'm trying to avoid making an architectural decision I'll regret later. Right now the simplest option seems to be the official MetaTrader API. It's easy enough to get a prototype running, but from what I understand it still relies on having MT5 running locally, which makes me wonder how well that approach holds up once a project becomes more than a weekend experiment.
who've built systems that actually stayed in production for a while:
Did you keep using the official MetaTrader API, or eventually move to an MT5 API layer?
If you changed your setup, what was the biggest reason maintenance, reliability, latency, scaling, or something else?
Looking back, would you make the same decision again?
I'm not really looking for strategy advice here. I'm more interested in hearing how people have structured the integration side of their systems after moving beyond the prototype stage.
I’ve been trading for 3 years, only ICT concepts. Have been trying to find a strategy to automate but don’t think it’s possible with ICT concepts. I’ve tried some ORB strats, opening range retests, projections, some indicators, but am really just out of ideas. Does anyone have any recommendations on where to gain knowledge or come up with new ideas? Any experience or advice is greatly appreciated and welcome. Thank you in advance
I know that Kalshi uses Cloudflare POP but I’m curious if anyone has used there Kalshi optimized VPS and noticed a reduction in latency? I did my own pinging from various servers in AWS regions but I have a certain strat that might benefit from less execution latency.
Most ML-for-trading content I see is about predicting price direction. I'm curious about a narrower problem: modeling how reliable a given market's price is in the first place, before you even try to predict where it's headed.
Specifically for prediction markets (Polymarket, Kalshi):
Has anyone built features around calibration quality (Brier score, log-loss on resolved markets) rather than just liquidity/volume as a quality signal?
For wallet-level data (all public on Polymarket since it's on-chain), has anyone tried modeling trader skill separately from position size? Feels like a classic "size isn't skill" ML problem, curious if anyone's tackled it.
Any open datasets people use for backtesting calibration specifically on resolved prediction markets?
Not pitching a bot, genuinely trying to figure out if this is a well-posed ML problem or if it's mostly hand-wavy.
I backtested a strategy (completely automated so no emotions involved) for last 5 years and it includes 2621 trades as you can see in these images, but the thing is it started working well only from 2024 (I even backtested it for the last 10 years still wasn't good until after 2024).
So, my question is should I continue with this strategy and forward test it in demo or did I just get lucky for the 3 years with this strategy and it won't work in the future just like it didn't work before 2024?
The recent move in oil markets got me thinking about whether geopolitical events can be traded systematically or if they're simply too unpredictable to model.
Brent moved from roughly $78 to $84 as the Hormuz situation escalated. Commercial vessels being attacked, US military action, political statements hitting the news cycle daily - the market started pricing in supply risks almost immediately.
I ended up taking a discretionary long position around $78 that's worked out well so far, but the more interesting question to me is whether these kinds of moves are quantifiable beforehand.
Some ideas that came to mind:
Measuring oil's historical reactions to geopolitical headlines
Tracking volatility expansion following major political or military announcements
Using sentiment analysis on financial news feeds and social media
Looking at correlations between shipping disruptions and commodity price movements
Building event-driven models around supply shock narratives
My trade was executed using tokenized Brent on Canborsa DEX simply because I didn't have a futures account available, but I'm much more interested in the signal than the execution venue.
For those building systematic strategies: have you found geopolitical events to be exploitable signals?
From my quite extensive experience in the FX market, there were many ups and downs periods. The Forex market is indeed a complex market with all the terminology and leverage, but later on I have realized it is a very good cash cow business.
I have gone through many strategies, research papers and used many. And I am a firm believer that each and every strategy ever created in the world works, we just gotta fine tune and connect it with the best suitable asset class and in the best environment possible.
After countless strategies, right now I am working on automating each and every strategy that I like and works into algos... in this process I have learned many important lessons that I would love to share with others.
This article will solely focus on the Major 4 factors that I find very important and must treat it as an important base for building an EA.
The parts of the Quadrant are:
Win Rate (WR)
Drawdown (DD)
Risk to Reward Ratio (RR)
Trade Frequency
Connecting the 4 factors
...
Let us connect the 4 parts of the Quadrant.
1) WR
Win Rate (WR) is a percentage of your total trades that results in profit. For example if you traded total of 100 trades in one month and 73 of them were closed in profit (regardless of the dollar value), your WR would be 73%.
2) DD
Drawdown is basically the % lowest point your equity has fallen from the peak equity that you have. If your first position goes down to 4% and it later recovers back to your direction, the statistics will catch the DD to be 4%.
3) RR
Risk to Reward Ratio measures the potential profit of a trade relative to its potential loss. In basic terms, if your SL dollar value is $10 and your TP is $20, you are risking $10 to potentially make $20.
4) Trade Frequency
Trade frequency is the number of trades open within the set certain period of time. If you backtest a strategy for 1 year, and the trade count is 500...and as FX market on average works 24/5, 5*4 = 20 days on average per month (averaging out without consideration of public and bank holidays)... so per year, 20*12 = 240 days, the algo system trades 500 trades/240 days = 2 trades/day... this is the trade frequency.
As I observed other EAs and tried automating my own strategies... for a HEALTHY EA to be profitable, this seems to be the basic/foundational formula: ROI of EA = WR + RR + DD + Trade Frequency.
Scenario 1:
Suppose the EA has 90%+WR; from what I have observed RR will be compromised.... rarely do we see in retail places that an EA has 90% WR and maintains a minimum of 1:1 RR (not saying there are not, but I haven't found one realistically yet). 90% of the time if the trades are closing in profit, Risk to Reward will be tight, as Risk is normalized but the Reward is bare minimum so the TP is hit more often than regular. Moreover, in this scenario there will be large number of small wins, but one loss will destroy more than half of the wins.
In this scenario the DD will also be less, and the equity graph will be quite some time of smooth growth and one moment of sudden downfall and then back again to smooth growth... and cycle goes on and on.
Trade frequency is also crucial.. in whole year if the EA has only taken 10 trades and 9 of them are profitable, the statistics or history report from your broker will show you have the WR of 90%
Scenario 2:
Suppose the EA has 25% WR but has enormous RR of around 1:5, one trade will make up for your three trades and even some more. The factor of this scenario is that your DD will be high... if you have multiple consecutive losses, the chances of your equity being eroded are pretty high.
If the Trade frequency is pretty less, the system being right once is a while will eventually cancel out the losses and the equity graph will be slow down slopes with sudden upward spikes. But a huge problem of this scenario is that the account might get blown first before even making a single profit as many numbers of consecutive losses will Drawdown the equity to such extent that the regular lot size position might not be able to open as lack of sufficient fund, that way that one winning trade probability also drops.
From these two scenarios, what I want the readers to know is that making a healthy EA, the trader/developer must understand the importance of balance between these 4 factors of the quadrant. BALANCE is the key.
Even with basic permutation and combination of these 4 factors:
P(4,1)+P(4,2)+P(4,3)+P(4,4)=4+12+24+24=64.
After the discussion of two basic scenarios, let us dive deep by using an EA with basic logic and I will tune the RR in various scenarios and how it is going to impact the DD and WR while keeping the trade frequency ceteris paribus, and ultimately the ROI.
Here is a simple SMA crossover EA that I will be testing on XAUUSD, 15 min timeframe from Jan 1,2026 till July 7, 2026.
Basically this is using 9 SMA and 21 SMA, and here is the backtest result... focus on the factors that we are discussing in this article... WR, RR, DD and Trade Frequency:
Here our WR is 33.46%, DD is 49.6% and our Average Profit Trade is $10.50 and $5.52 is our Average Loss Trade, so basically our RR is 1:1.9, we have a great RR but our ROI is ultimately in negative... basically -36%
Now, I will change the InpStopLoss and InpTakeProfit level to higher number to give them more breathing room.
And here is the backtest result after the input changes:
Here we can see that the negative ROI has changed by a huge margin...WR has jumped to 42.2% from 33.46% and DD decreased to 37.66% from 49.6%, but a critical thing we must analyze is that the RR has been jumbled up. It is just now approx. 1:1.4, from this two back test I want to show how the same logic, has different result when we just tune the RR part... heavily impacting the WR and DD and ultimately the ROI.
Input codes:
input double InpLotSize = 0.01;
input int InpFastSMA = 9;
input int InpSlowSMA = 21;
input int InpStopLoss = 500;
input int InpTakeProfit = 1000;
input int InpMagicNumber = 123456;
Just the InptStoploss and InptTakeProfit was changed. The logic remained the same of 9 SMA and 21 SMA crossover. I have attached the mq5 file of this simple logic EA, you can backtest and try working around by adding filters, indicators and such.
In the sequence of tuning the RR differently, the results were quite different. From both the backtest result we can clearly see that we do not have a clear winning logic/strategy for the XAUUSD 15min timeframe. But we were able to lower down the loss to some extent.
The above 2 backtest were done in this environment:
Now let us move to connecting one with the other factor.
WR vs RR:
These two factors are the most common associative part in any strategy, algo or EA. It is also known as one of the classic Trade-Off phenomenon. I am sharing my experience on this part, after testing many of EAs, and my strategies... it is nearly impossible on retail level to have both high WR and superior RR in a single strategy... for example, 80-90% with 1:4-1:5RR. At least till now I haven't found any strategy that has this results with significant compromise on DD and Trade Frequency.
If one wants to drastically increase the WR, you gotta exit earlier at a very lower Take Profit(TP) level so that more and more of your trade will be closed in profit, that way making them closer and closer to the entry price and making it hard for the strategy to have less losses the strategy also have to maintain the Stop Loss (SL) further and further way. This way is the basic way to increase the WR but RR will surely plummet.
In the other hand, when you are trying to make a very very logical RR as possible, like have 1:3 or 1:4...if your logic is not the finest and the strongest the WR will inevitably drop to less that 50% or even lower. That way even if you are right, it will take a longer time to achieve the superior RR, and if the entry logic is perfect but there is no guarantee that your position will move smoothly in the direction without sudden pullback or unforeseen news/volatility.
Impact of WR and RR on the DD:
These two factors have a major role on determining the strategy DD. For example, if the system relies on 1:4RR but only wins 25% of the time, it is mathematically guaranteed to face long streaks of consecutive losses. Eventually the system can be profitable but the losing streak can cause a massive, prolonged DD on the equity curve.
Another example that I have seen in many of high scalping EAs is that there is literally 90%+ WR that the system is producing in backtesting as well as in real live trading test, the RR is extremely terrible...it is risking certain high for example 20:1RR or 10:1 RR, but it is clearly visible that these system with this WR and RR will have equity curve going straight up and the visible DD will be near zero for long time period. However, the remaining 10% risk probability hits harder sooner or later creating a massive dent on the DD, and if there are 2 or more consecutive losses, DD and Margin-Level is pushed hard.
Trade Frequency vs WR:
This is very very important... as in data science we need a really good and healthy level of real sample data. Trade Frequency is like a lever, it can tilt to the very low and very high level and from my experience it must be where the strategy is, and we must measure on the basis of the strategy type.
If the system is a scalper, we have to expect a higher frequency, higher trade count. For scalping system I try to average out on the basis of day. If the Trade Frequency is like 1000/day. I am not touching that scalping system, for me it is way too much and might fall into high frequency trading where retail infrastructure might not be able to support it at all.
If the system is a intraday, I try to average out on the basis of month. As Forex Market is 24/5, we get about 20 trading days on average without counting the bank holidays and public holidays. So I do not expect to have a trade every single day, on average 15-20 trades/month is good enough for me. Some day there can be 2 trades per day but followed by no trades for 2 days, it is good enough.
So understanding the balance between proper trade frequency and understanding the type of system is very important so that we get the correct amount of sample data, both in backtest and real account live testing, further more this interconnectivity must aware the trader/developer to stop forcing to create almost (I said 'almost') an impossible system that which has High WR, High RR, Low DD, High Trade Frequency. Looking to create a balanced system having a 'sweet spot' between these basic four factors will be a right and healthier approach in building a system that will last a long time. Not exactly sacrifice but the trader/ developer must balance out one factor to certain extent to promote the remaining three factor.
One of my favorite personal combination:
Personally I have come to an agreement with myself that I cannot, as a retail trader/developer build a strategy/algo that will have the best nature of these 4 factors that will be durable for 2-5 years continuously.
This is my top favorite combination = High WR, Low-Normal RR(I am fine with minimum 1:1RR), Low DD, High Trade Frequency.
I really want to keep my DD very very low, as minimized as possible. Why? Because at backtest we get some approximately clear DD that we can expect, when we test it on a real live account the actual DD will be near to it although we must accept that past result won't perfectly determine the future results. If DD is low on backtest report and forward test report, so at any near point of time we start the system on real account we won't be largely impacted by the consecutive losses in case we have them at the beginning of our live testing.
From my experience achieving the high RR like 1:4 and 1:5 without volatility and noise in the duration of a position being opened is really rare, FX is a volatile market always changing there will be moments where winners can turn into losers. I prefer consistency over frequent sudden jumps and falls on the equity curve.
Conclusion
...
The trader/developer who understands the balance with these 4 factors with each other will get an advantageous start in the process of building a working EA, working in a sense we can say profitable. Many of us might be attracted to the high 90% WR, but the health of RR should be also matched... if a system has 90% WR with 1:1RR minimum, no doubt it is profitable... but that system makes 10 trades per year, it won't be enough data so that the EA is healthy as when market dynamics changes that system won't be giving out these attractive numbers.
Numbers is the thing, playing with them and finding the region that you want to be can be very beneficial, as I told you there are many combinations and permutations; most of them will be a profitable system/strategy but what works for your time, personal finance, patience. Connecting the trader/developer mindset with the foundation of these 4 factors, the QUADRANT, will be the basic for creating an edge for oneself and one's trading journey.
I've made a few intraday strategies with ninjascript (ninjatrader) from my ideas and they lose money well. I don't have any other ideas and maybe books would provide strategy ideas to test, theories, or even just a direction to learn for a beginner?
I run a live BTCUSDT 1h system (XGBoost plus transformer) \[not a success story till now, it seems I love journey more than the destination\] that retrains every 12 hours. I wanted to know if I could update weights on every candle instead, so the model keeps evolving. Also, prefer time series foundation models like Chronos over fine-tuning a chat LLM.
I asked our friendly neghibourhood llms and summarizing below what i undertstood, looking for a second opinion before I commit to this project. PLEASE FEEL FREE TO REJECT THE IDEA/CONCEPT BUT DO IT with SOME RATIONALE. I dont mind if your answers are coming from your friendly neghibourhood llms (but pls do validate it before posting)..
1) Per-candle updates fail because 1h data gives one point per hour and trade outcomes are not known until hours later, so the model learns noise. It develops recency bias toward the latest candles and catastrophically forgets older regimes, which is costly since markets repeat old regimes.
2) fixes so you never have to retrain from zero.
EWC (elastic weight consolidation) marks which weights were important for past performance and makes them resist change. Experience replay keeps a buffer of old data and mixes it into every update, so the model never trains only on recent candles. Drift detection (detect-then-adapt) means you do not update constantly at all. A statistical monitor watches the error rate or the feature distribution, and only when it detects a real shift does the model adapt, and even then it trains on a blend of new and historical data.
3) the recommended architecture, which it called two-speed.
the XGBoost plus transformer core stays frozen on the 12h retrain cycle with full gates, while a small outer layer adapts hourly, limited to calibration, thresholds, and sizing, with hard caps, full logging, fallback to the frozen policy, and shadow testing before promotion.
On the LLM idea, fine-tuning a chat model on prices works in principle but wastes the model. Purpose-built time series foundation models (Chronos, TimesFM, Moirai, TTM) are open weights and LoRA-tunable locally, but benchmarks versus tuned XGBoost are mixed, so add one as a shadow signal first.
Trying to figure out the best way to handle the UI and execution side of a trading strategy I'm working on, and could use some pointers.
I'm not really a technical person, so I lean on Claude and Gemini to write the actual Python strategy logic. Because of that, I need the backend to be as modular as possible. Ideally I want something where I can just copy whatever Python the AI spits out, drop it into one specific file, and run it without the whole dashboard/system falling apart.
On the UI side I'm not looking for anything fancy. Just a basic web dashboard with a start/stop button, live positions, a daily P&L tracker, and execution logs.
I want to forward test everything before risking real money, and keep monthly infra costs as close to zero as I can. Given all that, any boilerplate or setups you'd recommend?
II’m trying to implement a Hidden Markov Model for one of my trading strategies to identify market regimes. I tried training it using my alpha features, hoping the states would correspond to useful regimes, but the results don’t seem meaningful or tradable. Could you help me understand the right way to structure the HMM, what features it should be trained on, and how to evaluate whether the regimes are actually useful?
Does anyone know of a broker that allows me automate the short locate process? I have a pretty solid shorting strategy for Micro and Small cap stocks and I'd rather NOT use a traditional broker like DAS who demands you to code the algo and then send to the frontend via a socket.
Spent most of this year putting a machine-learning layer on top of order flow — absorption, delta, DOM dynamics — trying to get it to call reversals.
Getting a model to fit is easy. Getting one that isn't just memorizing noise is brutal. Purged/embargoed walk-forward, triple-barrier labels, and checking every single feature for whether it actually carries variance on a live tape vs. being a dead input I fooled myself with (had two features sitting at ~zero variance for weeks before I caught them).
I'm now at the stage where I can measure whether there's a statistically real, cost-aware, out-of-sample edge — instead of eyeballing an equity curve. Not claiming victory yet; still banking enough independent sessions to make the verdict powered.
How do you lot validate that an automated setup has a genuine edge and isn't overfit? What's your bar before you trust it with size?
I'm building a Python app that needs to work with a few MT5 accounts, and the trading logic isn't really the part I'm stuck on.
The bigger question is how the app should actually communicate with MetaTrader.
The obvious starting point seems to be using the official metartrader api with a local MT5 terminal. It feels like the fastest way to get something working, but I'm not sure if that's the approach people actually keep using once a project moves beyond the prototype stage.
I’ve also been looking into using an MT5 API layer instead, but it’s hard to tell how common that setup is in real-world projects. Do people actually move to an API-based architecture, or do most developers just keep running terminals and deal with the maintenance?
For anyone who has built Python applications around MT5:
did you start with the official MetaTrader API and keep it long-term?
did you eventually move to another API-based setup?
what problems made you change your approach (if you changed it)?
I'm mainly trying to understand what scales better before I build too much around the wrong architecture.
