Spend Forecast

Project where Claude Code spend will end up by the close of the current week or month.

Input

The user provides: $ARGUMENTS

This is the forecast horizon, "week", "month", or a specific date. Default to month (calendar month-end) when nothing is given, and state the horizon you used.

Data Sources

Endpoint	Returns
GET /api/analytics	{ total_cost, tokens (effective totals, baselines pre-summed), daily_sessions (365d: [{ date, count }]), daily_events, overview, ... }, daily_sessions is the trend the forecast extrapolates
GET /api/pricing/cost	{ total_cost, breakdown: [{ model, input_tokens, output_tokens, cache_read_tokens, cache_write_tokens, cost, matched_rule }] }, authoritative spend-to-date and avg cost-per-session input
GET /api/sessions?limit=200	Session list with inline cost and started_at, group by day for a sharper daily-spend curve than the count-based approximation

Forecast method

Spend has no native per-day field, so build a daily-spend series and extrapolate:

1. Spend-to-date = total_cost from /api/pricing/cost.
2. Avg cost per session = total_cost / total_session_count.
3. Daily spend series: for the trailing window, daily_spend[d] ≈ daily_sessions[d].count × avg_cost_per_session. For a sharper curve, instead sum inline session cost grouped by DATE(started_at).
4. Moving average: avg_daily_spend = mean(daily_spend over the trailing 7 days). Also compute a 14-day average to gauge whether the trend is accelerating (▲) or cooling (▼).
5. Remaining days: days left until the end of the chosen horizon (week = through Sunday; month = through the last calendar day).
6. Projection: projected_total = spend_to_date_this_period + (avg_daily_spend × days_remaining).