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Air Date: April 24, 2026 We’re back with Episode 4 of The Bot Pod! This week, Mike and Fede tackle one of the most requested features in Hummingbot: backtesting. They explain why backtesting should be used as a parameter research tool rather than a prediction engine, demo the new backtesting capabilities in Hummingbot 2.14, and show how PMM Mister’s position hold feature prevents selling at a loss. The episode also includes a candid discussion about this week’s DeFi hacks (Drift, KelpDAO) and why the foundation moved all assets to a wallet earning 0% yield. Sometimes the best trade is no trade.

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Episode Highlights

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Backtesting Philosophy

17:00 — Fede doesn’t hold back on this one:
“You will never be able to have a backtesting that in real life works exactly as the backtest.”
Why? A few reasons. First, there’s the queue problem—you never know where your order sits relative to everyone else’s. Second, there’s path dependency: if just one fill happens differently in real trading versus your backtest, everything after that diverges completely. And exchanges don’t tell you who placed orders when, so you’re always guessing. So what’s backtesting actually good for? Parameter research. You want to understand how changing a setting affects your bot’s behavior—not predict exactly how much money you’ll make.

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PMM Mister Deep Dive

24:00 — Mike and Fede walk through the PMM Mister strategy parameters, and there are a lot of them. The key insight is how they all work together. Portfolio allocation controls how much you place around the mid price—set it to 2% and you’re putting 20inordersona20 in orders on a 1,000 portfolio. The min base percentage determines when you start selling—if it’s 30%, your bot will only buy until it accumulates that much inventory. Max active executors by level caps how many times you can replace a filled order, which effectively limits your exposure. And profit protection ensures you only sell when your position is in the green. Mike works through the math live to make sure he understands:
“So if I’m placing 1% buy orders and max executors is 20, the most I’ll ever have on the book is 20%?”

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Running the Backtest

35:00 — The actual demo is quick—half a day of backtesting runs in about 34 seconds. But there’s a crucial detail: you need one-second candles for market making strategies. Fede explains that he often gets 30+ fills in a single minute, so one-minute candles would completely miss that activity.

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2% vs 10% Portfolio Allocation

54:00 — To show why backtesting matters, Fede changes just one parameter: portfolio allocation from 2% to 10%. The difference is dramatic. With 10% allocation, position builds way faster—the bot hits $300 in inventory almost immediately instead of gradually. But then something interesting happens: trading stops completely. The bot hit its max position while underwater, and because profit protection is on, it won’t sell at a loss. So it just… waits.
“If the market never recovers from this point,” Fede says, “this bot will be stopped forever.”
That’s exactly what backtesting is for. You can see how a single parameter change completely alters your trading continuity before you risk real money on it.

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Trading Bots vs Trading Agents

76:00 — A viewer asks about AI latency, which leads to an important clarification. In Condor, trading bots and trading agents are different things. Bots are deterministic—pure code, no LLM involved. They run on one-second ticks or faster, and backtesting works perfectly for them. Agents, on the other hand, use LLMs to make decisions. They’re slower by design, running every minute or so.
“When you’re doing HFT, you probably don’t want the LLM involved in every single tick. That’ll slow things down.”

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Resources