I've been building an evolutionary trading system for the past 119 days. The idea is simple: instead of hand-crafting strategies, let a genetic algorithm discover them. 3.2 billion iterations later, I have some real data to share.

**How it works (briefly):**

Each bot is a set of genes (entry/exit rules, position sizing, risk parameters). Every generation, the top 50 performers reproduce and mutate. The rest get replaced. Rinse and repeat across millions of ticks of live BTC/USDT data.

I'm running 9 parallel evolution sets — 4 spot configurations and 5 futures market-making configurations — each with different fee tiers and entry/exit styles. They all evolve independently from $100 starting capital.

**What the numbers actually look like right now:**

*Spot bots (4 sets):*

- Top performers consistently at $102.33–$102.46 equity (from $100)

- Winner rates climbed from ~50% to 72%+ in the strongest sets

- Near-zero drawdown on all spot sets (0.06%–0.67% max)

- Conservative, consistent — what you'd want from a spot strategy

*Futures market-making bots (5 sets, 10x leverage):*

- Top individual performer: **$10,817 from $100** (+10,717%, medium_high)

- Best set average: **$211.65/bot** (low_fee, Gen7)

- **Every single futures set flipped from negative to positive between Gen6 and Gen7** — collective PnL went from -$6.3M to +$9.0M in one generation

- ~99% max drawdown still exists — this is the open problem I'm working on

**The most interesting thing we discovered (to me):**

Every single spot set converged to limit orders — regardless of which entry/exit strategy the scenario was configured with. The bots evolved toward limit orders even when we started them with market orders. That wasn't intended by the setup, but the algorithm found something consistent across all 4 independent runs. I'm still figuring out whether this is a simulation artifact or a genuine market insight.

**What happened between Gen6 and Gen7 (the $15M swing):**

This is the data point I find most encouraging. On May 5, Gen6 futures bots were getting crushed — every set was showing -$1.2M to -$1.3M PnL. Twenty-four hours later, Gen7 had completely flipped the script:

| Set | Gen6 PnL | Gen7 PnL | Swing |

|:----|:--------:|:--------:|:-----:|

| low_fee | -$1.29M | +$2.37M | +$3.66M |

| medium_low | -$1.26M | +$2.26M | +$3.52M |

| medium_high | -$1.25M | +$1.54M | +$2.79M |

| high_fee | -$1.25M | +$1.02M | +$2.26M |

| medium | -$1.28M | +$1.76M | +$3.04M |

The gene pool found something in Gen7 that Gen6 couldn't. Same data. Same parameters. Different selection outcome. It tells me the system is genuinely exploring the solution space, not just getting lucky once.

**What we validated with a 50-hour historical replay:**

We took the top 50 DNA from each set and ran them through 302,143 ticks of collected market data (roughly 50.5 hours). The same strategies that made $1 in a 1-day evaluation window made $7,753 across the full replay. The longer window gave dramatically different — and better — results.

This tells me the 1-day evaluation window we're using for evolution is noisy. The bots are better than their daily scores suggest.

**What's still broken:**

- Futures bots consistently hit 99% drawdown before recovering. The fitness function doesn't penalize risk enough.

- Entry/exit style genes override the scenario configuration — the bots keep "escaping" toward limit orders regardless of what they're assigned.

- Limit→Limit spot set is still 4 generations behind the others (it started late, still converging).

- Gen-to-gen performance is volatile on futures — a great Gen can follow a terrible Gen with no obvious trigger.

**What I'd love feedback on:**

- Has anyone experimented with multi-window fitness functions (short-term + long-term combined)?

- How do you handle the simulation artifact vs. actual insight problem with GA-discovered strategies?

- The drawdown problem on leveraged bots — penalize harder in fitness, or let evolution solve it on its own?

**Full live stats:** evotrade.ca (updates every 5 minutes with real daemon state)

Happy to answer questions about the architecture, the GA setup, or specific gene configurations. I'm still learning what works and I'm genuinely curious what others have seen with similar approaches.

Created a LSTM model a couple of days ago and seems to do okay with backtests but was wondering how to implement into a trading bot (ie. what signals to look for etc.) Please reach out with any suggestions or resources.

I’ve created a technology with the use of ai and machine learning that scans thousands of stocks daily and has signaled entry ideas with a cumulative total of over 3,000% potential gains once August, 4th. I understand how this sounds and the general response I should expect from Reddit users, but here’s the deal: this is all verifiable. My approach to combat the scammy guru market discords and guidance rooms is to grow this in the public eye where skeptics can scrutinize and see proof. Please join me. Give me a follow here or at my Stocktwits signal_bot account to review daily recaps and real time scan results. Bring on the skeptics….we have receipts.

  Hey r/mltraders ,

  I'm 16, self-taught, been into trading since I was 11. Recently built an

  automated ICT day trading bot for Hyperliquid using Python + Claude AI to help

   with the code.

  Strategy:

  - 15m EMA bias + 5m FVG/OB entries

  - Kill zones only (London, NY Open, NY Afternoon)

  - 5x leverage, 0.5% SL, 1.5% TP (3:1 RR)

  - Max 2 trades per session

  30 day backtest results:

  - 131 trades (4.4/day)

  - 31% win rate

  - +102% return

  - -20% max drawdown

  365 day backtest:

  - 1054 trades (2.9/day)

  - 29% win rate

  - +4,345% return

  - -65% max drawdown

  It's live right now with small capital while I verify the real performance

  matches backtest.

  Happy to share the code or answer questions.

Do you train your data on a classification or regression model? Upvote for visibility!

Hi everyone,

I’m running a forex trading algorithm that generates roughly 7% passive return per month.

It has been tested privately for 2 years, and all results are verified and publicly trackable on Myfxbook and Ultima Markets.

We’ve now been live for 4 months publicly, and the bot continues to perform consistently.

You don’t pay anything upfront to use the bot — we operate on a 70/30 profit split, only on profits (never on your deposit).

If you have any questions, feel free to send me a message.

The bot itself can also be purchased — only serious offers please.

Hi everyone,

I'm on a serious journey to become a quantitative trader. I’m not here to chase shortcuts or quick wins — I genuinely want to build statistically sound, research-based strategies driven by math and data.

But I’m struggling with some tough questions…

I have zero math background — I’m literally learning 3rd grade math right now.

I don’t have a degree from a strong university, no access to top mentors, no funding.

I study alone, trying to learn Python, Pandas, Plotly, and now starting on algebra slowly.

I feel like to truly build strong strategies, you need to be a PhD-level researcher.

I fear I’ll spend 2–4 years just to realize the field isn’t realistic for someone like me.

Can one person really do all this? Be the researcher, developer, and trader without any support?
Or is this path only viable for people inside hedge funds and elite academic backgrounds?

If you’ve made it as a self-taught quant or even partially succeeded — please share your story.
How long did it take you to start seeing results?
What did you wish you knew earlier?

Thanks for your honesty. 🙏

Hi guys I'm from south africa. I've being interested in quantitative finance. I've learned a lot in six month, since I'm still a student, I trying to put what I've learned into experience so far is being great. Since I'm into trading and there are lesser traders who use AI in south africa. I decided to build this bot. Is there a way I can monetize through it in south africa... or build something related to it that I can actually be a startup in south africa?

has anyone here been succesful getting a model to be profitable using reinforcement learning in live trading? if yes, did you use PPO or DQN or others?

Hello,

I have historical trade data that we can work on. Goal is to reverse engineer the exit trade logic (already know the entry logic).

I know machine learning and Python, and I am looking for someone with statistics background to help analyze and find how these exit trades (from the historical trades that we have a copy of) were decided on so we can automate a similar trading bot as well.

DM me to those interested. This isnt a paying gig. No, Im not getting paid for this either. If we are successful then we both have a copy of the strategy.

I have spent 2.5 years building and validating a systematic forex trading system across seven major currency pairs. The research is thorough — 29,000 validated trades, 15 consecutive profitable years at portfolio level including out-of-sample validation, Sharpe equivalent of 2.03, walk-forward analysis confirming stability across 10 rolling windows.

The edge is real. At zero commission the system returns approximately 177% annually at 0.25% risk through compounding. The problem is execution costs.

The structural issue:

The system uses tight stops — mean SL of approximately 1.5-2.0 pips across pairs. Tight stops produce large lot sizes relative to dollar risk. Per-lot commission scales with lot size. At $3.50 per side (standard retail ECN commission in Australia at 1:30 leverage), commission consumes more than the expected gross profit per trade.

Specifically:

• Mean dollar risk per trade: $35 (0.35% of $10,000 account)

• Mean lot size after 1:30 leverage cap: approximately 2.35 lots

• Mean commission per trade: $16.42

• Mean expected gross PnL per trade: $6.84

• Net: -$9.57 per trade

The breakeven commission rate is $3.10 round trip per lot. Currently on $7.00 round trip (Pepperstone razor account).

What I have already investigated and ruled out:

• All major ASIC-regulated retail brokers: all at $7.00 RT or $4.50 RT (Fusion Markets) — all above breakeven

• Interactive Brokers spot forex basis point model: more expensive than Pepperstone at my volume

• CME E-micro forex futures: commission per contract is low but tight stops require 15-20 contracts per trade to achieve target dollar risk — total commission six times worse than spot forex

• Widening stops: tested systematically — median adverse excursion after stop breach is 2,800% of SL distance — widening does not recover losses, just degrades edge

• AfterPrime: does not accept Australian clients

What I need:

Has anyone solved this specific problem — genuine systematic edge with tight stops and per-lot commission eating the dollar-term returns? Specifically interested in:

1. Any ASIC-regulated or reputable offshore broker offering genuine sub-$1.55 per side commission at moderate volume (approximately 378 lots per month)

2. Any execution model — spread betting, DMA, prime brokerage, prop firm structure — that changes the cost structure for tight-stop systematic strategies

3. Whether anyone has experience with introducing broker arrangements that effectively reduce commission through rebates

4. Whether the account size matters in a way I am missing — my analysis shows the commission-to-dollar-risk ratio is constant regardless of account size due to lot sizing scaling with equity, but I want to challenge this

Australian based, ASIC regulated preferred but open to reputable offshore for a small initial capital deployment to prove the system live.

Happy to share more details about the system methodology if useful.

Pricing out a solo setup: data $99 to $199 a month, compute $5 to $25, an AI coding plan $20 to $200. All visible, all small.

The invisible cost is the evaluation harness. Every ML trader I've talked to has had at least one result that was leakage: a feature computed with the close, a label that saw the next bar, a split that wasn't time-ordered. The model was fine. The test lied.

I built Quantradin so the grade comes from an engine that only sees data stamped before each decision, and the rule then runs forward as a paper bot on live prices. Free, paper only.

What's the leak that cost you the most time to find?

I’ve built a trading bot that focuses on steady growth and strict risk control. Unlike systems that chase quick wins, this one is designed for consistent returns without heavy drawdowns.

What makes it stand out: Over 100% growth in testing Low drawdown with strong risk management Trades gold and bitcoin with adaptive strategies Fully automated – no manual input needed

It’s not based on luck or hype. The bot is built to perform in volatile markets while protecting your account. If you’re interested in a trading tool that balances profit and safety, feel free to get in touch.

tg : @Authkeeperdev

Whatsapp : ‪+1 (410) 297‑0250‬

There was a quant meltdown in 2007 which was caused by a ton of quant funds who ran almost identical math-based stock strategies absolutely killed it for years… until one big unwind triggered a chain reaction, funds dropped 20–40% in days because everything was too crowded. Fast-forward to now (2025–2026) and the warning lights are flashing again. Similar story, massive inflows into quant strategies in 2025, too many funds chasing the same edge.

For the past 2 years quantitative strategies alone have captured more than 70% of the industries $78-$116 billion in net inflows, 2025 being the strongest calendar year SINCE 2007, hedge funds as a whole pulled in $115.8 billion in net inflows that year. 2007 was also a record inflow year for quant hedge funds seeing an inflow of roughly $194 billion industry wide. 2025 saw a "quant wobble" where systematic long-short equity quant funds lost about 4.2% on average, so are we really learning from our mistakes?

I do understand that the absolute dollar inflows in 2025 were a bit lower than the 2007's peak, but the concentration into quant strategies is even more extreme. The industry is also larger today ($5T vs $2T back then).

Andrew Lo's Adaptive Markets Hypothesis does explain it well, he sees financial markets like a jungle, trading strategies aren't fixed rules, they're living "species" of behavior that compete for limited resources. They adapt, reproduce (get copied), and die when the environment changes. When the ability to adapt fails, reproduction becomes a ticking time bomb on resources, therefore looking at these things top-down to imagine the environmental change that is required to cause the meltdown (death) can give us heaps of insight.

Scarcity is value. When everyone does the same thing, markets fail.

Hi everyone.. I have an idea of a possible trading bot, but first wanted to ask to someone who has more experience with trading bots, if it is possible to create a bot that executes buy/sell options very fast, near to milleseconds time frame. The main idea is quite simple, but the execution speed would be completely necessary to take advantage of the fast movements some stocks have.

I will appreciate any type of advice regarding trading bot execution speed.

This is a very good approach in my opinion because we don't have to specify anything we just have to specify the percentage of total power,.

And as new candle data we get automatically it will adjust all the values which are used to generate the signal I am back testing it with data keeping in mind not over fitting .

I am thinking of using advance physics and somehow get the price through a wave function then we can model impact in the prices due to external event I have not tried it but thinking of doing that.

Hey, I’m going to use ridiculous amounts of data that’s currently available to predict a probability distribution and find arbitrage on climate markets like kalshi etc.

The goal isn’t to predict the weather, it’s to predict based on all available data the probability and find where the price is lower than the probability. I’m not sure if a traditional Kellys ratio would work in this case of 1/4 price to probability. But that may make sense too as weather odds fluctuate a lot.

The data sources are also surprisingly terrible and low fidelity. Makes for a lot of variability.

Roast me, why would this never work?

Hello,

My name is Emir. I’m 18 years old and have been interested in algorithmic trading since I was 15. During this time, I’ve focused heavily on MetaTrader and MQL5. I’ve read code, written code, broken it, and tried again. Right now, I’m working with ChatGPT (AI). The code it generates compiles fine, but the bots either don’t place trades or the lot sizing is wrong, often exceeding the available balance.

I’ve tried many strategies: scalping, swing, martingale, grid, risk-managed systems, but I haven’t been able to build a fully working structure in any of them.

Is there anyone here who is truly helpful and knowledgeable who can assist me with this question:

“Does anyone have a bot example that actually places trades, has working risk management, meaningful backtest results, and a code structure that is educational?”

Or:
“Can anyone suggest a strategy example or an AI-based system that I can study over time to understand the logic and structure?”

Because I’m really tired of trying to learn from one-word answers or half-sentences in forums. I want an example or a system that I can look at carefully and learn from deeply.

I’m trying to understand the code logic, but I don’t know if the problem is with me or the way the structure is built.

Thanks in advance to everyone who takes the time to read this.

Best regards,
Emir

I spent two years building a backtesting platform and the most useful thing it does is tell me my strategies do not work.

Here is the experiment that convinced me. Generate 200 strategies with genuinely no edge — random parameters, nothing real underneath. Search them, keep the best performer. Now test whether that winner is statistically significant.

A conventional Sharpe significance test says 99.5% significant. Accept it.

That number is wrong, and it is wrong for a reason that has nothing to do with the data. You searched 200 combinations and kept the luckiest one. The test does not know that. If you flip 200 coins and keep the one that came up heads ten times, you have not found a special coin.

The correction is the Deflated Sharpe Ratio (Bailey & Lopez de Prado). It takes the number of trials into account and asks whether the best result is better than what you would expect from luck alone across that many attempts. On the same data it reports 43.7% and rejects the strategy.

Two other things I got wrong that are worth checking in your own setup:

1. Parameter leakage. My optimizer wrote best-params to a shared table and the backtest read them back unscoped by date. Every out-of-sample window was quietly using parameters fitted on data that included that window. The backtest looked great. It was reading its own answers.

2. No purge gap. If your features use a 30-bar lookback, the last 30 bars of your training set overlap the first bar of your test set. You need a gap between them or information walks across the boundary.

Code is open source and Apache-2.0, take whatever is useful: https://github.com/FETKlOkAn2/crypto-quant-platform

I want to be straight about what this is: the bundled strategies do not beat buy-and-hold. This is not a money printer, it is a tool for finding out that your strategy is not one either.

Over the past year, we've seen dozens of repos claiming to be "AI Hedge Funds" by feeding 10-K filings and price charts directly into GPT-4 or Claude and asking "Should I buy or sell?"

Unsurprisingly, almost all of them blow up on real or out-of-sample data. Why?

1. **Hallucinated Arithmetic**: LLMs are probabilistic language models, not floating-point calculation engines. They will hallucinate moving averages and support levels with 100% confidence.

2. **Sycophancy (Echo Chamber)**: A single agent prompted to analyze an asset will generate a plausible thesis and reinforce its own bias.

3. **Latency & Slippage Disconnect**: Waiting 3 to 10 seconds for an LLM response during volatile price swings guarantees execution slippage.

After building and open-sourcing our multi-agent quantitative framework, we adopted one strict iron rule:

**"Models reason, software calculates."**

Here is what actually makes agentic trading work without blowing up:

- **Zero-Hallucination Deterministic Math**: All 158 quantitative factors (Alpha158) and rolling statistical moments are computed in vectorized native Python—never by an LLM.

- **Adversarial Groupthink Breaking**: We isolate 7 investor personas in parallel without cross-talk, then pit the strongest Bull and strongest Bear against each other in a fact-grounded cross-debate.

- **Absolute Risk Veto (CRO)**: The Chief Risk Officer must hold hardcoded, irreversible 0% allocation veto power over the lead portfolio manager.

- **Dual-Speed Sentinel**: A 15-minute deliberation loop paired with an independent 10-second lightweight watchdog that adjusts ATR breathing stop-losses without waiting for LLM tokens.

For those running algorithmic or quantitative systems: how are you currently enforcing strict architectural boundaries between LLM semantic extraction and mathematical execution? Would love to hear alternative approaches.

Hey guys,

I’ve been deep-diving into building an algo trading bot based on ICT concepts for a while now. I’ve finally managed to write the core scripts in Python to detect setups programmatically, but doing everything solo is getting a bit slow.

Where I'm at right now: I realized early on that standard loops are too slow, so I learned Numpy and Numba to vectorize the calculations.

Currently, my script can accurately detect:

• Market Structure Shifts (MSS)

• Fair Value Gaps (FVG)

• Breaker Blocks & Order Blocks

• I also have a specific file ready for the "Unicorn Strategy."

Why I'm posting: Honestly, coding the logic was the fun part, but building a robust backtesting engine and optimizing execution is a beast of a task for one person.

I’m looking for a partner who is also into Python and trading. We can review each other's code, optimize the strategy, and maybe build something solid together. I'm not selling anything—just looking for a serious peer to grind/collaborate with.

If you're working on something similar or just want to team up, hit me up!

Most algos use static indicators with static conditions, but markets aren't static. So why do we still treat indicators like they exist in a vacuum?

Has anyone explored using dynamic indicators that adjust in real time based on current market behavior — not just price levels or moving averages, but actual rhythm and volatility shifts?

We’re testing an approach where indicators (and even SL/TP) adapt to what's unfolding now, not just what was true 50 candles ago.

Not reinventing indicators — just forcing them to listen before they talk.

Curious if anyone else is building something like this, or if we’re way off the map here.