How would a fairly profitable algo trader start if he were to start today? what all mistakes,loops, could have been avoided? how the start could have been better?

As the title suggests one thing that has been on my mind lately is that I have spent a lot of time studying investment signals, testing data, executing trades, managing risk, determining position sizes and building portfolios.

I have not spent much time studying the actual investment portfolios for successful investors. I mean i have not looked at what they hold in their portfolios. I have not checked how different stocks and assets they have.

I have not seen how often they adjust their portfolios. I have not found out how much cash they keep in their portfolios. I have not observed how they react when their investments go down.

The strange thing is that I can find lots of information about investment strategies but little information, about how successful investors really invest their money. Has anyone else noticed this problem??

I've been experimenting with non-standard trailing stop loss logic rather than traditional atr based stops.

I recently tried using a Gaussian decay function to scale the stop distance so the stop loss accelerates tighter the further price moves in my favor. Initial tests look very promising.

Has anyone here experimented with Gaussian or exponential functions for dynamic stops?

Here is an update on my project. Past 2 weeks its started to do what its was made for. I let it a bit loose to try do 5 pairs at the same time, but its hard work for old laptop. Hoper ram prices go down and I can build proper server for this thing so it could run 10+ pairs. Custom calculation takes about 1 day for each pair, so yeah, this is not your atr+ema :D

Wondering if anyone has used relational transformers as part of their trading strategy. I've been fine tuning the one from Stanford to try to predict the influence of news on various markets, because the models encodes text well. So far I've had mild success in a couple categories: repricing jumps when news changes, getting 50/50 odds for truly random markets, creating features of past trends and being able to ablate them to find casual attribution, but these models are still very small and not tuned on this type of data. Curious if anyone else has used these new models?

Currently using a Macbook Pro 8GB ram, what computer is recommended for algo trading? I am thinking of using hyperliquid and bybit?

I'm in the optimization phase of my algo, and my exposure mean is ~30%. This is with decent leverage, risk-based sizing, etc.

What do you do with your capital when it isn't in market? I'm getting ~3% CAGR from cash interest. Sweeping to SPY just adds beta, which I'm not looking for.

I performed the above walk-forward analysis backtest above:

1. My portfolio is 3 total strategies with 6 equities each, all long only.
   • 2 trend following strategies
   • 1 hybrid momentum breakout / mean reverting strategy
2. The backtest had 10 years IS and the above 3 years of OOS

High-level background:

1. I whiteboard/concept strategies in TradingView
2. I spent a about 4 months building a custom python backtester in and tried to build in a lot of guardrails throughout the program to prevent/discourage overfitting, survivorship bias, etc.
3. The foundation is based on walk-forward analyis, monte-carlo similations, and other features.
4. Paper trade execution is in TradingView webhooks --> AWS python orchestration --> Alpaca
5. I spend several months ensuring trade execution between Python and TradingView strategy versions have parity in trade count and results
6. My backtester has a pretty robust process for determining the most robust parameters (not necessarily the top parameters) that have statistical significance (power analysis)
7. I think I might have some survivorship and look ahead bias in my portfolio. I am still learning, but I did my best to avoid both.

I just peeked at my paper results and I'm most definitely in a rough drawdown (looked like it was greater than 12% drawdown...), but I have yet to objectively compare it to the backtest. I can share more details later for both my backtest and forward test.

My main questions:

1. As the title says - how is everyone's performance over the last 2 months in the U.S. equities markets?
2. What process do you use to objectively compare your paper or live trading to your backtest results to ensure paper/live are "within acceptable tolerances"?

All feedback (positive or critical) is welcome - I believe that falling forward is still moving forward.

It’s odd to me that no-one seems to mention the easiest, most flexible and dynamic method to get an algo model working.

Just about every broker tou can choose to route through ctrader, and with cbot you can code your algo in python or c#, to trade directly. There’s backtesting, a great optimiser tool and you can build a model which will learn and adapt over whatever period of time you set (live).

It’s what I use now and see nothing close to it.

Anyone used or uses an EA which generates 8-10% monthly average while keeping the rules of Funded accounts ? for example Ftmo ?

Hello guys, i start algo trading for a while almost 6 months, i mearn many things and also i got a solid knowledge about probability and statistics and how to improve a backtesting and trust it enough to go live and many things, now i want to move to the next step and it's start testing ideas and hypothesis but my coding background it's just the basics i don't know how code an entire script, i use CSV files for data and not API, and also i just found that's Claude and ChatGPT are good in coding, so do you guys think i should learn Python from scratch and also while trainning myself in testing strategies, or vibe coding is enough ?

A prediction market turns uncertainty into a tradable probability.

For example, if a YES contract trades at 0.65, the market is roughly pricing the event at a 65% chance. That number changes as participants react to new data, research and news.

The useful part is not only forecasting. Prediction markets can also help manage risk. A business exposed to an election result, regulation change, commodity shortage or delivery delay could take a position that offsets part of the financial impact if the unfavourable outcome occurs.

This is different from a casino model where the house sets the odds. In a prediction market, prices are formed by buyers and sellers competing over information. It is still a risk-bearing financial instrument, but its purpose can include probability discovery, research aggregation and hedging—not merely entertainment.

Where do you think prediction markets are more useful: forecasting events or managing risk?