# 08: Dynamic Weights

## Source
- **Protocol**: Balancer
- **Files**: `GradualValueChange.sol`, `LBPool.sol` (V3), `quantamm_math.py`
- **Repos**: `balancer/balancer-v2-monorepo`, `balancer/balancer-v3-monorepo`, `balancer/balancer-maths`

## Rationale for MYCO

Static bonding curves have fixed geometry — the price surface never adapts. Dynamic weights allow the MYCO bonding surface to evolve over time:

1. **Token launch**: LBP-style weight schedule for initial distribution (start 95/5 MYCO/USDC, gradually shift to 50/50)
2. **Adaptive pricing**: Oracle-driven multipliers let the surface respond to market conditions
3. **Governance updates**: Smooth transitions between parameter sets (no discontinuities)
4. **Reserve rebalancing**: As target allocations change, weights interpolate smoothly

Two mechanisms:
- **GradualValueChange**: Linear interpolation on a schedule (deterministic)
- **QuantAMM multiplier**: Oracle sets a velocity, weight drifts linearly until next update (reactive)

## Formulas

**GradualValueChange:**
```
progress = (t - t_start) / (t_end - t_start)
value(t) = start_value + progress * (end_value - start_value)
```
Clamped: before start → start_value, after end → end_value.

**QuantAMM multiplier:**
```
weight(t) = base_weight + multiplier * (t - last_update_time)
```
Oracle updates `(base_weight, multiplier)` at each block/epoch.

## Parameters

| Parameter | Effect |
|-----------|--------|
| start/end values | What the weight changes between |
| start/end times | Duration of the transition |
| multiplier | Rate of change per time unit (oracle-driven) |
| min/max weight | Bounds to prevent extreme allocations |

## Implementation

See `src/primitives/dynamic_weights.py`