DCA Strategy Wizard
Self-Custodial DCA: Backtest & Automate
Created on 22nd June 2025
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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:
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Automating every step in a single, intuitive wizard—no spreadsheets or servers required.
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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.
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Enforcing safety guards (gas caps, daily spend limits, “Are you sure?” confirmations) to protect both newbies and power users.
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Empowering with AI insights that translate raw results into clear, actionable recommendations.
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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.
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