Concepts
Accumulation Strategies
Canonical guide to DCA fundamentals and strategy comparisons across uniform and dynamic approaches.
This is the canonical concepts page for accumulation strategy design in Stacking Sats.
DCA Fundamentals
Dollar Cost Averaging (DCA) allocates a fixed amount on a recurring schedule. When price is lower, each contribution acquires more sats; when price is higher, it acquires fewer sats.
Example
| Week | BTC Price | Amount Allocated | Sats Acquired |
|---|---|---|---|
| 1 | $50,000 | $100 | 200,000 |
| 2 | $40,000 | $100 | 250,000 |
| 3 | $60,000 | $100 | 166,667 |
| 4 | $45,000 | $100 | 222,222 |
| Total | -- | $400 | 838,889 |
Strategy Comparison
| Strategy | Weighting | Complexity | Inputs Required |
|---|---|---|---|
| Lump Sum | All at once | Low | None |
| Uniform DCA | Equal each period | Low | Budget, frequency |
| Value Averaging | Target-based adjustment | Medium | Target growth rate |
| Dynamic DCA | Signal-weighted | High | Market/on-chain inputs |
Uniform vs Dynamic DCA
Uniform DCA uses equal periodic allocations. Dynamic DCA keeps the same total budget but changes how much is allocated per period based on model signals.
Stacking Sats uses a dynamic model that incorporates valuation and trend context to shift allocations while preserving a fixed-range budget. New users: sign in → /profile → plan → generate a personal API token → your AI agent pulls weights via the Agent API on the schedule you configure and you execute trades on your preferred platform outside Stacking Sats (no custody by Stacking Sats). See Quick Start.
See Signal Interpretation for daily operational interpretation and Glossary for canonical term definitions.
Limitations and Risk Context
No strategy guarantees better future outcomes. Backtests are historical evidence, not forecasts.
Dynamic strategies add model risk, data dependency, and operational complexity.