Does anyone here use algos more as a decision-support tool rather than for full automation? I know the point of algorithmic trading is usually to automate execution, but I actually trust myself more with the execution side (at least for now). I’ve been using pine script to build something with the help of AI - is this common here or are there better tools?

What I’m working on is having an algo identify levels, conditions and potential setups, then I decide whether to take the trade manually after confirming the trade with some execution criteria (liquidity sweeps, CVD, acceptance/rejection).

My thinking is it removes a lot of the repetitive analysis and keeps things objective, but still lets me control the actual entries and exits. I’m completely new to this side of trading, so I’m curious if anyone else uses algos this way or if there’s something I’m overlooking.

I’m very open to feedback and guidance on this one.

https://preview.redd.it/1cn4jscquwah1.png?width=1241&format=png&auto=webp&s=803a18288d674c52cb44d81710d3204b95d60612

GoldRaider testing from 9/1/2025 to 7/2/2026 on M1 chart.

Hey everyone,

A bit ago, I posted about a strategy test for my custom expert advisor, Gold Raider (specifically running setfile #23). The backtest scored a 100% win rate over 96 trades on a €1,000 account, with a max equity drawdown of just 3.67%.

I’ve attached the full report image GR4_Set23_Demo.png for reference.

Naturally, a 100% win rate screams "hidden martingale or grid system that will eventually blow the account." However, I wanted to share a look at the architecture of the EA to show why it performs this way. This is a pure single-order scalper with strict safety nets.

📊 The Backtest Conditions (From GR4_Set23_Demo.png)

• Broker / Leverage: Fusion Markets Pty Ltd | 1:500
• Data Quality: 100% Real Ticks
• Timeframe: M1 (XAUUSD)
• Total Net Profit: 1,226.20 EUR (~122% gain)
• Profit Factor: 128.73

🛠️ The Safety Architecture (Why it hasn't lost)

Instead of relying on dangerous money management, Gold Raider uses a multi-layered gatekeeper system to filter out bad entries, combined with aggressive exit management.

1. Multi-Timeframe Confirmation Gates

The entry engine doesn't just look at the M1 chart. To take a trade, it passes through three distinct validation filters:

• Filter A (RSI Confirmation): Dynamically tracks M5 momentum zones to ensure we aren’t buying the absolute top or selling the absolute bottom of a macro swing.
• Filter B (Trend Strength Confirmation): An optional higher-timeframe Efficiency Ratio filter that blocks trades if the market is moving too violently in the opposite direction.
• Filter C (Range Validation): Uses a structural lookback algorithm to calculate key volatility boundaries. It ensures the EA only fires when price is within safe execution zones.

2. Strict Protection & Safety Nets

The EA has hardcoded protective logic to shield the account from catastrophic black swan events:

• No Grid / No Martingale: It executes fixed lot sizes or automated equity-proportionate lot sizing. One trade at a time per side.
• Hard Stop Loss & Emergency SL: Every single trade is launched with an initial stop loss protection mechanism.
• Aggressive Break-Even (BE) Logic: The moment a scalp moves into a set amount of profit points, the EA aggressively moves the SL to lock in the trade at Entry + 1 point. This is exactly why the backtest shows a 100% win rate—trades that don't hit the primary target are choked out at break-even before they can turn into losses.
• Built-in News Guard: It integrates directly with the internal MT5 calendar to completely halt new entries during high-impact macroeconomic events (like NFP or FOMC).

🚀 Next Steps: Live Testing

The equity curve in GR4_Set23_Demo.png shows a very smooth, linear ascent because the break-even and trailing mechanics are highly optimized for MT5's historical real ticks.

Of course, the real enemy of an M1 gold scalper is live execution: slippage, commissions, and spread widening. The code does include a strict max-spread filter to prevent trading during toxic market hours, but forward testing is the only way to know for sure. I am moving this to a live demo/forward test to see how the execution holds up.

Would love to get your thoughts on the filter mechanics, or what further stress-tests you'd put an M1 scalper through!

Building my bot and doing a lot of backtesting these days.

I had a breakout system that looked bulletproof on BTC: +66%, profit factor 3.2, 13% max drawdown, profitable in 6 of 9 walk-forward windows. So far, so great.

But, it only fired about 8 trades a year. At that frequency a single-asset walk-forward can't tell a real edge from getting lucky. The sample is just too small, no matter how you slice the windows.

So I froze the exact config, no re-tuning, and ran it on ETH and SOL.

• BTC: +66%
• ETH: -11%
• SOL: -60%, with a 0% win rate.

Also tried different parameters, but no parameter set rescued the other two. It was fit to BTC.

Might still be something "real" that only happens on BTC. But more likely just overfitting.

In contrast, my market-neutral funding carry pays +6.7 / +6.6 / +5.6% on BTC, ETH, SOL. Neatly aligned, what a real structural edge should looks like, boring and the same everywhere.

If your edge is low frequency and only tested on one asset, you don't know it's real yet. You know it fit one history. Might still make you money.

Do you cross-validate across instruments, or is single-asset walk-forward enough for you?

I've just started algo trading more than a years ago . For options buying .. my heart is broken.. I've tried 100s of strategies.. but I'm not able to find one profitable.. is there hope for me?

I don't know where to get the data for back testing with LTP.. so I just back test with pts and paper trade and trade and a year has passed testing . Nothing is consistent or profitable.. is there actually a strategy that works.. and how much profit% can I expect if I'm successful.. am I expecting too much?

No official data out there. Not to include institutional traders or organisations.

What is your estimate that retail traders out there deploying algorithm? 1%? 10%

Curious how much the data might have jumped since the beginning of FREE LLM to code strategies.

I listened about breakout, men reversion and sational strategies that tend to have a 50-70% of win ratio.

But lately i am been reading some autors that talk about low win ratios but with a lot of net profit with a ver very good chart of net profit in a lot of years, the example is on the turtles book, that are called as trend following strategies (5-30% of win ratio)

So i will like to know if someone here has found edge of this kind of strategies and how has been your performance or some tip to find those, i can share also my experiencie so is not the one side alfa divulgation.

In my own thinks i believe that this are like 'narnia' strategies in the correct markets of shure.

Right now I am thinking of starting a project regarding MCMC or Bayesian networks, for I am not sure what I should look into. My goal is to create a very rigorous model that will provide risk analysis as accurately as possible. Im willing to spend around 10k for compute power, is this possible?

Also, I am an undergrad pursuing a math of comp and statistics data science major, but I would not say I am a master at probability, and I do not have much experience in ML. I am willing to learn, but do you think its feasible for me?

I just have the strategy created. And I'm serious about building it. Im a java developer. Not very proficient, but I can get the job done.

What are the common pitfalls i will face, and what the basics I should understand, while making the system.

It's a simple swing trade management system. So I won't need a high level of optimizations. If the system is good. I might incorporate more strategy.

Right know I'm just making the llm create the documents on each segments.

Signal generation, entry placement, trade management and book keeping.

What issues could I face when I code the system?

Wanted to share a project I've been working on: a fully rules-based momentum strategy for US tech equities. No discretionary stock picking - everything is ranked and selected by a defined funnel (momentum filters, 52-week high proximity, MA stack), rebalanced monthly.

Backtest results since 2016:

• CAGR: 42.1% (trailing 10Y)
• 1Y return: +145.4%
• 3Y return: +403.8%
• Max drawdown: -40.8%

Happy to go into the funnel logic or backtest construction if anyone's curious - especially interested in feedback on drawdown management since -40.8% is still rough.

my best algorithm has a 1.52 sharpe ratio and it's a daytrader.

daily-rebalanced FAST residual momentum. Beta-residualize each S&P-100 mega-cap vs SPY over 40d.

I tested several hundred different algorithms, 99% barely beat the SPY. I have an LLM researching more that get sent to a quant engine that are backtested to see if they pass the SPY gate. They are all duds.

What's your best performance?

you can now bring your API key and test our stock database for free and make any models that you want. You can also test your own models.

Tried getting a trading bot running this week and almost gave up like 3 times — half the battle was just the setup, WSL, servers, my card kept getting declined by VPS providers lol. Strategy was the easy part honestly. Anyone else bounce off how technical it is just to run one? Or did you push through? Genuinely curious where people landed.

Hey all,

I've been seeing some things on line saying its possible to connect claude and trading view via MCP. Is this true? Is it actually beneficial? I've recently had my second child and am finding it hard to continue trading whilist being a father, I've heard you can set it up to scan for your setups and get it to notify you when they appear. Can any one confirm this? Also alot of the stuff I am seeing is mac based, is it capable on windows. If anyone could point me in the right direction it would be appreciated!

Thank you in advance!

What tool do you use to backtest your option trading strategies?
Streak doesn't let you do this, I believe it did at some point in time, but not anymore.

Have you deployed a very long multi-legged strategy to be able to generate consistent returns from option trading?

I am trying to create a tool for my personal use, but would want to check if there are any good tools out there.

Hello enthusiasts ,

I'm running an automated live system of about ~70–75% win rate, lots of small wins, positive in aggregate, the entries themselves aren't the problem, Im pretty satisfied with the build over all , besides the low end.

So... the losers can run deep before they resolve, and a small number of them never come back and do the most damage. The edge depends on sitting through drawdown, but the occasional non-recovery is brutal.

What I've already tested (so "just use a stop" won't help):

• Hard stop at −20% ROI (on a 10x position): net negative. It cut a huge number of trades that would have recovered — bled more than it saved.
• Drawdown distribution: even among trades that breach −40% ROI, about 53% still return to a small loss or a profit. So any flat "cut at −X%" throws away more eventual winners than catastrophes it prevents.

So a fixed price-based stop is a trap that ive discovered over multiple sesions. The deep drawdowns are mostly noise the rest killers, and by the rest I've narrowed it down to about 4-5%.

The big question how can i mittigate the deep loses? The methodology used:

• Time-based exit (cut if not recovered within N bars) instead of a price stop
• Vol-expansion / regime filter — only bail when the regime that breaks the recovery assumption shows up
• Portfolio-level heat cap instead of per-trade stops
• Size for the drawdown + fractional Kelly, just accept the DD
• A cheap tail hedge / trend overlay

To sum up, I've tried lots of options for the bot itself, and the current conclusion is just not run it 24/7 but in specific times of the day and for a short while.

Any and all suggestions would be greatly appreciated.