# Acquisition Trends > Use this skill when the user wants to analyze acquisition or performance trends over multiple months — new invitees per month, conversion rate trajectory, monthly commission, daily-active-trader trends. Triggers: "拉新趋势", "近 N 个月的拉新", "monthly trend", "show me the last 6 months", "DAT history", "when was my peak", "analyze acquisition over time", "what's the YoY". Do NOT use for single-day briefings (use `daily-briefing`) or single-user history (use `whale-deep-dive`). - Skill: `okx/acquisition-trends` (Agent Skill) - Install (CLI): `npx skillmds@latest add okx/acquisition-trends` - Raw SKILL.md: https://api.skillmd.com/api/skills/okx/acquisition-trends/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: okx (https://skillmd.com/u/okx) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/okx/acquisition-trends --- # Acquisition trends — multi-month performance analysis > "分析下我过去 6 个月的拉新情况" / "show me the last 12 months" Time-series view of the affiliate node: new invitees, activation rates, deposits, volume, commission, and active-trader counts. Surfaces inflection points and the events behind them. ## When to use this skill - User asks for trends, history, MoM, QoQ, YoY analysis - Quarterly review / pitch deck preparation - Investigating a sudden change ("why is commission down 30% this month?") - Replicating a past success ("what did we do in August?") ## What the agent does ### Step 1 — Choose the window Default to 12 months if user says "trend" / "history" without a number. For specific requests like "last 6 months" or "Q1 2026" honor that exactly. ### Step 2 — One call per calendar month Loop over each month in the window: ```json { "name": "okx-affiliate-performance-summary", "arguments": { "begin": "", "end": "" } } ``` Cap parallelism at ≤ 5 concurrent calls; the endpoint rate-limits with `code: 50011`. Insert a 200–500 ms delay between batches. ### Step 3 — For finer-grained trends, pull daily data If the user asks for daily-active-trader (DAT) history or wants to identify peak days, loop day-by-day over the window: ```json { "name": "okx-affiliate-performance-summary", "arguments": { "begin": "", "end": "" } } ``` 90 days = 90 calls. Run serially with `sleep 0.4` between requests to avoid 429s. For >180 days, suggest the user request only monthly granularity unless they truly need the daily resolution. ### Step 4 — Cache aggregates Save results as CSV (one row per period) for follow-up analysis without re-pulling: ``` date,invitees,firstTimeTrader,traderCnt,depAmt,vol,commission ``` Use the `details[]` array from the response to sum across SPOT/DERIVATIVE/BSC. ## Insights to extract - **Direction of travel** — first 30 days vs middle 30 vs last 30. Quote the deltas. - **Peak month and peak day** — the highest value of each metric and the date - **Acquisition velocity vs commission lag** — new invitees in month M often produce most of their commission in months M+1 to M+3; surface this lag if it's pronounced - **Activation rate trend** — `firstTimeTrader / invitees` per month. Healthy nodes hold this ratio above 40%; below 30% signals quality erosion - **Volume-per-trader trend** — total `vol` / `traderCnt`. Falling per-trader volume with steady trader count = users trading smaller (market conditions or product-mix shift) - **Spot vs derivatives mix drift** — if derivatives share is climbing, the user base is getting more risk-tolerant; useful for product recommendations - **Concentration risk** — % of monthly commission from the top 3 users. If > 50% the node is fragile ## Sample output format ``` 📊 Acquisition trend — to ( months) 📈 Monthly snapshot | Month | New inv | First-time | Activation | Deposit | Volume | Commission | | 2025-06 | 338 | 135 | 40% | $2.13M | $756M | $61,970 | | ... 📊 Quarterly view • Q2 (M–M): X new / Y first / $Z commission — strongest acquisition quarter • Q3 (M–M): ... • ... 🏔️ Peaks • Peak monthly new invitees: in • Peak monthly commission: $X in • Peak DAT day: traders on 🔍 Key insights 1. 2. 3. 4. 📌 Suggested action 📁 Raw data CSV with daily/monthly rows saved at . Useful for replay against BTC price, campaign timestamps, etc. ``` ## Recommended follow-ups - *"Why did spike?"* — pull the channel-level (`okx-affiliate-link-list`) data for that month to see if a specific link drove it - *"Cross-reference with BTC price"* — pull a price chart from blockbeats and overlay - *"Who were the top users in ?"* — re-run `high-potential-invitees` with the peak month's date range and see if they're still active ## Gotchas - **Custom-range timestamps are Unix milliseconds** in UTC. `int(datetime(2026,4,1).timestamp()*1000)` in Python. Off-by-year mistakes are common. - **Months have different lengths** — when comparing MoM, normalize daily averages or be explicit about absolute vs. per-day numbers. - **The endpoint returns 429 above ~5 RPS**, then succeeds again seconds later. Build retry logic with exponential backoff; do not panic on a single 429. - **`okx-affiliate-sub-affiliate-list` is lifetime-only** in the new MCP — for time-windowed sub-affiliate analysis pull `invitee-list` with `subAffiliateUid`... but that filter is currently broken, so client-side filtering is needed. - **For long backfills**, consider chunked monthly calls (12–24 calls) rather than daily (300+ calls). The user usually wants the shape, not every day.