I didn’t set out to beat the market. I built this to stop missing buys, panic-buying pumps, and drifting through chop. The weekend script became a multi-exchange DCA system with a fixed path and a “multiplier” path, full backtesting, and risk controls. The build taught me far more about execution, human behavior, and operations than any blog post ever did.
“Why not just use a fee-free recurring buy?”
If a single-exchange recurring purchase fits your life, that’s a great option. My motivation was different: split buys across exchanges, control precision/min-notional issues directly in code, and add logic that preserves capital near obvious heat while leaning in when price gets locally stressed. In other words, DCA with agency. The multiplier path doesn’t predict price; it redistributes when you spend your existing budget.
What multiplier DCA actually does (and the guardrails that matter)
Fixed DCA spends the same amount on schedule. The multiplier variant replaces that fixed number with a factor that ranges between zero and a capped maximum, depending on conditions. Early on, I let that logic fire too aggressively during fast drops. Backtests loved it; live trading didn’t—I’d burn through the week’s allocation and have nothing left for deeper dips. The cure was boring and essential: caps, cool-downs, and weekly budget limits. Once I added those, average entry prices improved and the system stopped front-loading into the first leg of a decline. These are the parts I’d implement first if I were starting over.
Backtests vs. live: the humbling gap
Backtests are seductive. Perfect fills, no latency, clean candles. Real markets add slippage, fee tiers, precision rules, partial fills, and rate-limit wrinkles. I treat backtests as directional: use them to check that ideas aren’t obviously broken, then paper trade, then go live behind strict caps and alerts before widening anything. In my own static tests over the last year, the multiplier accumulated materially more BTC than fixed DCA with slightly higher total spend—but live trading still finds edge cases, so I build for failure and explain every decision to the user.
Execution realities (speed, liquidity, and “front-running” fears)
This isn’t HFT and doesn’t have to be. My path looks like this: a watcher scans for ready bots, a message queue hands off work, and exchange adapters place orders with exchange-specific precision and min-notional validation. End-to-end, the critical path is fast enough for low/mid-frequency execution; the goal is robust fills, not microsecond glory. “Front-running” in the HFT sense isn’t really the concern for DCA frequency; what matters is modeling fees/precision properly and refusing to place orders that will be rejected after rounding. For liquidity, I’m pragmatic: use order types that actually fill, monitor slippage, and prefer reliability over cleverness.
The human side of “set and forget”
Everyone says they want automation and then checks daily—that includes me. I leaned into this. The bot shows remaining budget for the period, what just happened, what was skipped, and why. The moment I started surfacing those reasons (“cool-down active”, “min-notional after rounding too low”), support questions dropped and trust went up. It’s amazing how much anxiety disappears when a system narrates itself.
Tech stack (because devs always ask)
It’s Node.js with AWS for reliability and cost control: Lambda workers, SQS for fan-out, DynamoDB for fast state, and Aurora for relational bits. Nothing exotic, just legible building blocks that make idempotency and retries straightforward.
A caution I took to heart
One commenter said something that stuck with me: the confidence you get from “nailing bottoms” can push you to oversize risk. Most trading strategies look great until the day they don’t. My personal rule is boring: hodl first, automate the DCA, and keep the “smart” logic behind guardrails that assume I’m not the exception. That mindset has saved me from myself more than once.
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Appreciate any hard feedback, especially on execution edge cases and UX clarity. If you want to try the multiplier DCA flow and tell me where it hurts, early access: dca.bot