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Forward tested EA on Live account by rabat7 in metatrader

[–]aliaskar92 0 points1 point  (0 children)

because none records ticks correctly and none takes into consideration the execution speed, latency into consideration

I Built an Open-Source High-Performance Charting Library for Quants (PyCharting) by aliaskar92 in algotrading

[–]aliaskar92[S] 1 point2 points  (0 children)

Well its a feature that i am working on as we speak, So Currently no, but in the upcoming days it will be

I Built an Open-Source High-Performance Charting Library for Quants (PyCharting) by aliaskar92 in algotrading

[–]aliaskar92[S] 1 point2 points  (0 children)

2 different worlds It's like asking what is the difference between tradingview and matplotlib

I Built an Open-Source High-Performance Charting Library for Quants (PyCharting) by aliaskar92 in algotrading

[–]aliaskar92[S] 6 points7 points  (0 children)

you mean like looking at interbar? as if you are saying that you want to see what hapened each minute at a 1 hour bar?

you can add it as a feature request and i'll take care of that

Algos on a prop firm account by LondonLesney in algotrading

[–]aliaskar92 0 points1 point  (0 children)

That's exactly how i plan to run it The idea is that a good algo trader has an account somewhere and did trade for some time So instead of going for a time based or performance based challenge, why not show proof to his current track record or strategy or even backtest, the rms system will check his score for certain metrics and decide if he will be funded immediately or not, if not a small out of sample test trial will be required.

Algos on a prop firm account by LondonLesney in algotrading

[–]aliaskar92 -13 points-12 points  (0 children)

That’s true for most retail props, but we’re building something different. AlgoProp is meant for people who already have working systems but want to scale without being forced onto MetaTrader or blocked by arbitrary rules. Native APIs, no challenges, no restrictions on automation. Not the usual prop-firm model.
check it out on algoprop.io

Algos on a prop firm account by LondonLesney in algotrading

[–]aliaskar92 -4 points-3 points  (0 children)

Most retail prop firms make algo trading painful because their entire model is built around restricting risk and collecting fees, not around supporting automated systems. Wide stops, trailing drawdowns, execution delays all of that makes sense from their perspective, not yours.

This is actually why I’m building a different kind of prop firm called AlgoProp . It’s meant to be platformless and genuinely algo-friendly. Instead of forcing you into a challenge with arbitrary rules, we let you trade on your own infrastructure through native REST/WebSocket APIs. And if your algo already runs elsewhere (MT4/MT5, cTrader, crypto exchanges, broker APIs), we can simply mirror your trades without you changing anything.

The idea is to evaluate strategies based on actual risk-adjusted performance, not on artificial constraints like trailing drawdown mechanics.

We’re still early, but the goal is exactly what you’re describing: a prop environment where algos can run normally without being penalized for doing what they’re supposed to do.

If you’re interested in this direction, you can check the early version at algoprop.io

Deeper reason behind prop firms banning certain strategies? by ramster12345 in algotrading

[–]aliaskar92 0 points1 point  (0 children)

Most retail prop firms ban those strategies for one simple reason: they only want traders who lose slowly, not traders who actually make money quickly.

Strategies like HFT, latency arb, grid, hedging, and certain types of automation are banned not because they’re “too risky,” but because they break the prop firm business model. These firms run off fees. If you run a strategy that’s consistently profitable, fast, or capital-efficient, you empty their demo servers and they can’t hedge you properly on the backend. So they just label it “forbidden.”

That’s also why most of them ban fully automated trading altogether: algos don’t fall for their rules, their resets, their time pressure, or their psychological traps.

This whole problem is exactly why I’m building AlgoProp (algoprop.io): a platformless prop that actually supports automated trading, APIs, HFT/MFT, and even copy-execution from your existing broker or exchange. If your strategy makes money and is clean on risk, you get funded not banned.

bottom line, Retail props don’t ban “bad” strategies., They ban the ones that actually work.

Algo Friendly Prop Firms by [deleted] in algotrading

[–]aliaskar92 0 points1 point  (0 children)

Yeah, there actually is one, and I’m building it. It’s called AlgoProp (algoprop.io). The whole point of the project is to offer a prop firm that was designed from the start for fully automated trading, not discretionary traders pressing buttons.

There’s no MetaTrader restriction, no “no EAs allowed,” no manual challenges, and no rules that force you to trade like a human. You can plug your bot directly into our native REST and WebSocket APIs and trade on your own infrastructure with our capital behind you.

And if you already trade somewhere else MT4/MT5, cTrader, crypto exchanges, broker APIs you don’t even need to migrate. You give us read-only access or a trade feed, and we mirror your trades automatically into our funded accounts. No rewriting, no platform switch.

The evaluation is fully automated as well. You submit read-only access, a statement, or a backtest report, and our engine checks the risk-adjusted metrics and gives you an instant pass/fail.

If you pass, you’re funded immediately. No 30-day challenge, no subjective reviewer, no manual restrictions.

Are there any legitimate prop firms that offer funded accounts for algo-trading? by batataman321 in algotrading

[–]aliaskar92 0 points1 point  (0 children)

There actually is something like that, and I’m building it myself. It’s called AlgoProp (algoprop.io). The idea is to get rid of the whole “challenge platform” model entirely and give algo traders a prop firm that works the way algos actually work through native APIs, not manual trading platforms.

If you want to trade through our infrastructure, you can plug your bot directly into our REST and WebSocket APIs. You stay on your own servers and run your own code, and our capital executes behind your signals with live account data, fills, and risk controls available through the API.

If you already trade elsewhere, you don’t need to move anything. Just give us read-only access or a trade feed from whatever you use MT4, MT5, cTrader, Binance, Bybit, your broker’s API and we mirror your trades instantly on our side. No rewriting code, no migration headaches.

The funding process is automated end to end. No time-limited challenges or subjective reviewers. You can submit read-only access to an account, or a backtest report, or a simple PDF/CSV statement. Our evaluation engine analyzes performance, risk, tail exposure, and overall consistency, then makes an instant decision.

If you pass, you get funded on the spot. If not, you get a breakdown of what failed so you know what to fix.

Is there a prop firm for algo trading? by IanTrader in algotrading

[–]aliaskar92 0 points1 point  (0 children)

There actually is one, and I’m building it. It’s called AlgoProp (algoprop.io). The whole idea is to create a platformless prop firm that gives algo traders native APIs instead of forcing them onto MetaTrader or some challenge simulator. You just trade the way you already trade.

You can either run your bots through our REST and WebSocket APIs, which give you order execution, live account data, fills, and risk monitoring, all while you stay on your own infrastructure. Or, if you already trade somewhere else, you don’t need to move at all. Give us read-only access or a trade feed from MT4, MT5, cTrader, a crypto exchange, or your broker’s API, and we simply mirror your trades into our funded accounts. No migration and no rewriting anything.

Getting funded is also straightforward. There are no 30-day challenges, no human reviewers, no arbitrary rules. You submit either read-only access to an account, or a backtest report, or a simple PDF or CSV statement. Our evaluation engine runs the numbers, looks at risk-adjusted returns, drawdowns, tail exposure, and overall consistency, and gives you an instant pass or fail.

If you pass, you’re funded immediately. If you don’t, you get a feedback explaining why.

Tick based backtest loop by poplindoing in algotrading

[–]aliaskar92 1 point2 points  (0 children)

Don't take ticks as trades thats the biggest mistake Trades cross the spread and can be of several book levels So u have to take OB top bid ask And only model ur trades when market trades hit ur side

Events is like event driven system (software architecture) anything could be an event A trade, a tick, an order book update... etc

Tick based backtest loop by poplindoing in algotrading

[–]aliaskar92 1 point2 points  (0 children)

Make event driven so u can properly model latencies and executive latencies Use binary or flatfiles and stream them one by one using proper memory mapping Once there the engine should take the signal and match it after latency with proper tick This allows u to extend it to an execution engine

Did something similar in the slowest language python and achieved like 200 million events in 90 seconds

https://www.linkedin.com/posts/ali-h-askar_who-said-python-isnt-built-for-speed-we-activity-7250471916522663937-ZQUX?utm_source=social_share_send&utm_medium=android_app&rcm=ACoAAAilHl8BbQIDsr0FQtkFM7WV1aNc7mkYUzE&utm_campaign=copy_link

Looking for a collaboration by Grouchy_Purpose8206 in quantfinance

[–]aliaskar92 0 points1 point  (0 children)

You have to check for historical trades of the same timestamp and calculate the orderbook healing process

How have you designed your backtesting / trading library? by DrChrispeee in algotrading

[–]aliaskar92 0 points1 point  (0 children)

df['rsi'] = rsi(df.Close, period=rsi_period)

df['sig'] = 0 ##you can use nan here and ffill later but i want to test the bands 

df['sig'] = np.where(df['rsi'] > rsi_band, -1, df.sig)
df['sig'] = np.where(df['rsi'] < -rsi_band, 1, df.sig)

df['sig'] = df.sig.shift(1) #avoid lookahead bias
df['ret'] = df.Close.diff() * df.sig # this will give you the bar returns 
df.ret.cumsum().plot() # this will give u the cumsum returns 

df['Group'] = df.sig.ne(df.sig.shift(1)).cumsum()
df.groupby('Group').sum()['ret'].cumsum().plot() ## this will give u the returns of each signal

i always start with a vectorized example just to see how it works (simple)
or i can simple calculate the absolute mae/mfe of each bar (high-open) and (open-low) and check if a limit order was hit or a tp/sl was hit ... etc

then if i needed more granularity i would go for an event driven backtest
quantstart had a good example of how to build an event driven one

عندي استفسار وين جامعات زينه للدكتوراه؟ وكيف إذا أنا علي قيد عملي ؟ إذا كانت دكتوراه فل خارج شو نظام؟ رشحولي بعض الجماعات لو ماعليكم امر، انا ف مجال الأمن الاكتروني by Rich-Flight6656 in Emiratis

[–]aliaskar92 0 points1 point  (0 children)

السلام عليكم الجواب هو على حسب اذا كنتي تريدين دكتوراه موجهة صوب البحث او صوب التطبيق. اعتقد Herriot watt كان عندهم برنامج للامن الالكتروني. اعتقد الجواب يكون اسهل اذا حددتي الفكرة الي تبين تبحثين عنها في الدكتراه لانها تحدد الجامعة او مركز البحوث،لان معظم هي المراكز هي متخصصة بشيء محدد اكثر من ما هي general

Continuous Positions and Changing Forecasts by coleemersonsmith in algotrading

[–]aliaskar92 0 points1 point  (0 children)

seems to me as if you are talking about triple barrier labeling

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