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DCA Strategy Wizard

Self-Custodial DCA: Backtest & Automate

Created on 22nd June 2025

D

DCA Strategy Wizard

Self-Custodial DCA: Backtest & Automate

The problem DCA Strategy Wizard solves

“Investing in crypto shouldn’t feel like assembling code—it should feel like flipping a switch.”
— Paraphrased from Elon Musk

Across the crypto landscape, everyday investors struggle with:

Fragmented workflows
Juggling spreadsheets, cron jobs and multiple exchanges turns a simple DCA plan into a full-time coding project.

Blind spots in decision-making
Without reliable backtests, users can’t see how their strategy would have fared through real market swings—so they either go all-in blindly or stay on the sidelines.

Risky self-custody
Built-in DCA on centralized platforms sacrifices control; rolling your own buys means wrestling with private keys, gas prices, and network failures.

Fear of the unknown
Gas-fee spikes, runaway spend limits, and missing a trade window can erase hard-earned gains overnight.

DCA Strategy Wizard turns this chaos into confidence by:

  • Automating every step in a single, intuitive wizard—no spreadsheets or servers required.

  • Simulating with real historical data and real-time performance monitoring so you know exactly how your plan would have worked in the past and watch it perform live, as it executes.

  • Enforcing safety guards (gas caps, daily spend limits, “Are you sure?” confirmations) to protect both newbies and power users.

  • Empowering with AI insights that translate raw results into clear, actionable recommendations.

  • With DCA Strategy Wizard, anyone can set up a self-custodial, AI-guided DCA strategy in minutes—and finally invest on autopilot, without sacrificing security or control.

Challenges I ran into

✅ Addressed in the hackathon

Problem definition & value-prop alignment: We kicked off by validating that true pain was “self-custodial, no-code DCA,” not generic yield farming.

Tool & tech-stack selection: Under intense time pressure, we evaluated React vs. Streamlit and Ethers.js vs. Web3.py—landing on Streamlit + Web3.py + AgentKit to maximize delivery speed without sacrificing reliability.

Feature scoping: Prioritized backtesting, mock live execution, and AI insights for an MVP that delivers immediate user value—cutting secondary wishlist items to meet our deadline.

Basic UX trust-building: Built “Are you sure?” confirmations, immediate explorer links for transactions, and contextual tooltips so users feel safe every step of the way.

Tracks Applied (1)

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