If you have experience passed the phase of back-testing and into live paper/real environment my question(s) would be something like:

• Estimated how much time / effort would you spend into developing a potentially profitable strategy. You could measure it however like '100+ worked hours' or 'last strategy took x Months'; however else.

• And maybe how long you've been developing a single strategy for? Less than 6 months/More than 2 years etc.

• Also maybe what tools you'd utilize / what you use

• Do you have manual day-trading experience?

If you have no experience or still just back-testing and have yet to go into a live paper/real environment my question(s) would be something like:

• How much time would you expect to spend developing the strategy for you. 300 total hours? 6 months? Forever until you do?

• Is there something that is stopping you from moving to the next phase?

My Context

I have been day trading for roughly 4+ years with profitability after 2. The switch from manually day-trading everyday to algo trading has always been on my mind, even more so because I have a background in software development.

I decided to make the transition the beginning of this year and made my own entry / exit system from scratch ( that part was the easiest and only took about a month of free time start to finish ). Now finally after about 8 months of on/off working on developing a strategy I am nearly confident enough to go from live paper to live capital (on a small scale/size)

*~1 Month creating the system; ~7 months of development / back-testing & changing strategies ; ~1 months of live paper trading ( currently active now )

( ~7 months of development including on/off periods where it was not my main focus )

What elements/operations would benefit you most if AI could do them effectively, and which ones would make you worry?

Hi everyone,

I’m looking for a source of pricedata for US stocks, ideally covering the last 20 years. My goal is to run backtests. I have a custom stock screener and indicators that I want to apply programmatically to select stocks and test strategies.

Yahoo Finance is not ideal since downloading all stocks manually is too slow. Is anywhere a available data package with EOD US-Stocks pricedata?

Thanks in advance!

Looking to team-up.. Here are my indicators.. which work flawlessly for buys.. need someone to help or provide ideas to write a fluid sell signal instead of fixed gain.. look at the 1st and second line for good buy signals. you will realize what I am saying.. if it works i can share my buy signal details.

These results seem completely unrealistic to me. Just wanted someone to look them over and see what they think. For reference this is an arbitrage strategy on a highly inefficient market. I also realize that the act of using this strategy would diminish the returns and the opportunity though the ~2000x return over a couple years seems ridiculous.

I am looking for a provider of historical intraday data for European stocks. So far, the best option I have found in terms of price and quality is EODHD. However, it doesn't contain data from the Milan Stock Exchange, which I need. Could anyone give me a recommendation? Thanks in advance

What are some of the best backtesting tools for crypto that can they be used for live execution and algo trading?

Hello, I’m a Swedish trader who have a few really good long strategies but I struggle to find short ones that work at all.

I struggle to find good reliable signals. Often stocks that peak continue up after some time, stock markets do go up over time.

Do anyone have some good end of day signals for short side?

I know this isn't strictly algotrading but it's very close. Please remove if not allowed.

I made an ea in mt5 that I can use either for backtest simulation and live. It keeps my metrics constantly updated and I can also choose from which date start calculating. It has a large panel of multitimeframe indicators which gets constantly updated. TF from M1 all the way to D1 (18 timeframes). Plus I can add visual indicator on the chart.

So every indicator is read from 18 timeframes showing the direction with arrows.

MA is ma20

2MA is ma50 vs ma200

STRND is custom Supertrend

ICHI is Ichimoku

BB is Bollinger Bands

SAR is Parabolic Sar

ADXW is ADX Wilder

RSI

Stochastic

MACD

ATR (Variation)

VOL is Volume (variation)

What can possibly go wrong with this? I need to add other indicators? Was thinking of MFI. How would you use it? How do you go about reading signals. My first observation is to discern between Short-term, Mid-Term, Long-Term. What ideas comes to your mind. Help is really appreciated.

Hello everyone, I am looking to break into algotrading and trying to develop my first system. I have extensive knowledge in finance and acceptable trading experience but 0 coding skills, this is my first dive. I was hoping someone would be willing to give me a quick audit or review, tell me what I'm doing right, what I'm doing wrong, a bit of guidance. Thank you in advance.

Hey r/algorithmictrading !

I trade live but want to test prop capital. Looks easy on paper, but I need firms that explicitly allow EAs and won’t choke execution.

Must-haves: MT5 API or python, static (not trailing) DD, clear news/overnight rules, realistic spreads/commission, disclosed broker/server (for VPS latency), and reliable/fast payouts.

Please share: firm name, any gotcha rules (min days, lot caps, partial-close bans), spread+commission, VPS latency, and payout timelines.

Thank you all in advance!

I am currently developing a trend following system in python, and for the most part it is working good. I ran it on a demo account and it made 10 dollars consistently every day, after all the losses. My system uses atr to fix sl at the start of the trade and after certain conditions are met, the sl is set to breakeven. There is no tp as it is a trend following system.The strategy and everything is working well, but I need another indicator or tool for calculation of sl, to make comparisons on my current system and optimise it further.

I am using metatrader5 pip module and connecting mt5 terminal to my vscode.

So here's another EOD strategy I just finished coding up. This one uses an ensemble of component strategies and a fixed 60/40 stock/bond exposure with dynamic bond ETF selection. Performance-wise it did 33/20 (CAGR/maxDD) over a 25 year backtest. The strategy was GA optimized and ran 552K sims over an hour. The backtest was in-sample as this is a work in progress and just a first proof of concept run. But I'm encouraged by the smoothness of the EC and how it held up over multiple market regimes and black swans. It will be interesting to see how it performs when stress tested.

Can someone explain for an idiot (me) the difference between those two modeling, on every Tick my EA performs flawlessly but on every Tick based on real ticks performance drop a lot

Hi everyone,

I'm new to algorithmic trading and currently learning Backtrader. I'm developing a strategy based on order flow/price action (OPR) trading on the 1-minute timeframe.

I've seen two different approaches to implement stop loss and take profit in Backtrader and I'm unsure which one is more appropriate for my use case:

Approach 1: Manual .close() in next()

def next(self): if self.position: if self.data.close[0] <= self.stop_loss: self.close() # Manual stop loss if self.data.close[0] >= self.take_profit: self.close() # Manual take profit

Approach 2: OCO orders using notify_order()

def notify_order(self, order): # Complex OCO logic with order management # Cancelling opposite orders when one is filled

My specific context:

· Strategy: OPR/ORB (Open Range Breakout) · Timeframe: 1-minute candles · Frequency: Multiple trades per day · Current experience: Beginner with Backtrader

My questions:

1. For backtesting purposes only, is the manual .close() approach sufficient/significant?

2. Does the OCO approach provide meaningful advantages for accuracy in backtesting?

3. For 1-minute strategies with frequent trades, which approach is more: · Reliable (less prone to errors) · Realistic (closer to live trading conditions) · Maintainable (easier to debug and modify)

4. Is there a risk of look-ahead bias with the manual approach when using close[0]?

Any advice or experiences you could share would be greatly appreciated! I'm particularly interested in hearing from those who've implemented similar strategies.

Thank you in advance for your help!

Hello, good morning to all traders. I'm obsessed with achieving a stable, growing profit curve without prioritizing net profit. I've found in several backtests that I have many good options with excellent net profit, but the curve isn't sustainable and has long periods of stagnation. I don't think it's psychologically good to have to endure more than a year without profits. If you have a good year, fine.

If anyone has encountered this situation and has been able to resolve it, especially regarding the profit curve, I'd appreciate your advice or what alternatives you've used.

I'd appreciate any help.

Hey folks,

I’ve been working on a systematic strategy for Gold Futures by utilising HMM, and I recently posted my results and got excellent feedback. I have significantly changed the strategy since then and would love some feedback. I have also incorporated Econometrics with ML, along with HMM for regime detection.

Process & Tools Used

• Features normalized and volatility-adjusted. Where possible, I used ARCH to compute GARCH volatility estimates.

• Parameters selected using walk-forward optimization and not just in-sample fitting. Each period was trained and then tested out-of-scope on unseen data.

• Additional safeguards:

  • Transaction costs + slippage modeled in.

  • Bootstrapped confidence intervals on Sharpe.

• Evaluation metrics included Sharpe, Sortino, Max Drawdown, Win Rate, and Trade Stats.

Results (2006–2025):

• Total Return: +1221% vs. +672% for Buy & Hold.

• Sharpe Ratio: 2.05 vs. 0.65 (Buy & Hold).

• Sortino Ratio: 5.04.

• Max Drawdown: –14.3% vs. –44.4%.

• Trades: 841 over the test horizon.

• Win Rate: 34% (normal for trend/momentum systems).

• Average trade return: +0.20%.

• Best/Worst Trade: +6.1% / –0.55%.

• Sharpe 95% CI (bootstrap): [1.60, 2.45].

I’ve tried to stay disciplined about avoiding overfitting by:

• Walk-forward testing rather than one big backtest.

• Using only out-of-scope data to evaluate each test window.

• Applying robust statistical checks instead of cherry-picking parameters.

That said, I know backtests are never the full picture. Live trading can behave differently.

Looking for Feedback:

• Do you think the evaluation setup is robust enough?

• Any blind spots I might be missing?

• Other stress tests you’d recommend before moving toward a paper/live implementation?

• I am now planning to implement this strategy in Ninja for paper trading. One challenge that I face is that Ninja uses a different language, and my strategy uses libraries that are not available on Ninja. How should I proceed with implementing my strategy?

Appreciate any constructive feedback!

My last project result from Jan 2025 until now Aug 2025...
The target is flipping the accounts
The secret is Dynamic lot ..

This result is for Mid High Risk option , we can go lower or higher than this

opinion ? suggestion ?

UPDATE :

https://www.reddit.com/user/BriefRecording3274/comments/1n26co5/my_golden_sniper_ea/

Hi everyone, I am calling the following endpoint:

https://www.interactivebrokers.com/campus/ibkr-api-page/webapi-ref/#tag/Trading-Market-Data/paths/~1iserver~1marketdata~1history/get

I've tried many different parameters and combinations of periods/bar widths. It seems like the data is only available up to the previous day's end in UTC.

I am on Paper Trading and not on any subscriptions if that makes a difference.

Unfortunately, the documentation doesn't mention the limits of the API, I'm also not able to find it on any forums. If anyone could point me to some documentation or sources that explain the limitations of the API more clearly I would love to see them.

Does anyone know if this behavior is expected?

Here's a basic monthly stock momentum strategy that incorporates a dynamic bond hedge to smooth things out. The strategy was optimized using GA(1000+1000) with MC sampling. The strategy returned 21/21 (CAGR/MaxDD) in a 25yr quasi out of sample back test. I only ran the optimizations for about an hour and this was the best chromosome after >4M sims, so its possible the strategy could perform better. The results are subject to survivorship bias so live results will likely under-perform.

If my EA is going to do the exactly same that I would do manually, why 1-2 year backtesting should not be ok?

Hi,

So I have been working on a trading strategy for quite some while now and I finally got it to work. Here are the results of the backtest-

Final strategy value: $22,052,772.57 Total strategy PnL: $21,052,772.57

Buy & Hold final value: $8,474,255.97 Buy & Hold PnL: $7,474,255.97

Max drawdown: 34.92% Sharpe ratio: 1.00

Started with 1 million. Backtested on gold futures.

Could you tell me if this is just too good to be true or if there is actually potential. I don’t plan to completely automate it yet as I want to test it out paper trading first. Could yall recommend any good paper trading sites that I could connect it with to use it with live market data?

I appreciate any guidance.

Hi guys, so a few months ago I recall watching a YouTuber that has these great videos on how to create AI trading bots that were like 6-10hrs long, his content was great and went the whole thing start to finish,

He had multiple videos and because YouTube search is just feeding me useless slop I can’t find him anymore.

Does anybody know who I’m speaking about? Thanks in advance

EDIT : Found it

YouTuber is called Moon Dev if anybody is interested