u/Accomplished-Rip9652

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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.

https://preview.redd.it/4y0nfw9x6nzg1.png?width=1184&format=png&auto=webp&s=325841b9bb88109e864895060f1f5fd567fb4ef5

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.