# 2026-03-23 CN 10 Tickers / 6 Months Multi-Persona Efficiency Ablation

## Summary
This note consolidates three completed `CN 10 tickers / 6m` multi-persona live backtests into a reusable execution-efficiency ablation. The purpose is not to compare returns, but to quantify how much the optimized execution stack speeds up controlled long-horizon persona studies.

The three runs hold the business setting fixed:

- mode: `multi-personality`
- market: `CN`
- tickers: `600519, 000858, 300750, 601318, 600036, 601888, 600276, 002415, 601899, 000333`
- backtest period: `2025-09-01 ~ 2026-02-28`
- trading days: `102`
- initial cash: `¥100,000`
- analysts: `fundamental`, `technical`, `company_news`
- personas: `conservative`, `balanced`, `aggressive`, `passive`, `equal_weight_index`
- benchmark mode: `index`
- benchmark index: `000300.SH`
- LLM: enabled

## Experimental Conditions

### E0: Baseline
- comparison run id: `20260308_032534_600248`
- report: `reports/multi_personality/20260308_032534_600248/comparison_report.md`
- data: `reports/multi_personality/20260308_032534_600248/comparison_data.json`

### E1: Optimized-Cold
- comparison run id: `20260308_164113_268847`
- log: `logs/cn10_mp_20260308_164108.log`
- report: `reports/multi_personality/20260308_164113_268847/comparison_report.md`
- data: `reports/multi_personality/20260308_164113_268847/comparison_data.json`

### E2: Optimized-Warm
- comparison run id: `20260309_120759_016922`
- log: `logs/cn10_mp_rerun_20260309_120714.log`
- report: `reports/multi_personality/20260309_120759_016922/comparison_report.md`
- data: `reports/multi_personality/20260309_120759_016922/comparison_data.json`

## Core Results

### End-to-End Runtime

| Experiment | Run ID | Total Duration (sec) | Total Duration (hours) | Relative to E0 |
|-----------|--------|----------------------|------------------------|----------------|
| `E0` baseline | `20260308_032534_600248` | `157429.02` | `43.73` | `1.00x` |
| `E1` optimized-cold | `20260308_164113_268847` | `38828.22` | `10.79` | `4.05x` |
| `E2` optimized-warm | `20260309_120759_016922` | `38965.22` | `10.82` | `4.04x` |

### LLM Usage (comparison report accounting)

| Experiment | LLM Calls | Total Tokens | Estimated Cost |
|-----------|-----------|--------------|----------------|
| `E0` baseline | `11219` | `10255794` | `¥12.0572` |
| `E1` optimized-cold | `8160` | `9299500` | `¥10.7245` |
| `E2` optimized-warm | `8160` | `9264269` | `¥10.6699` |

### Shared Phase-One Statistics

| Metric | `E0` | `E1` | `E2` |
|-------|------|------|------|
| artifact cache hits | `n/a` | `0` | `102` |
| artifact cache misses | `n/a` | `102` | `0` |
| sync load seconds | `n/a` | `82.08` | `0.19` |
| prefetch submitted | `n/a` | `101` | `101` |
| prefetch hits | `n/a` | `101` | `101` |
| prefetch compute seconds | `n/a` | `7633.76` | `65.83` |
| pipeline hidden seconds | `n/a` | `7633.76` | `65.82` |
| pipeline utilization | `n/a` | `100.0%` | `100.0%` |
| phase2 execution mode | `n/a` | `parallel_threads` | `parallel_threads` |

## Interpretation

### E0 -> E1: Execution-Model Gain
- total duration: `157429.02s -> 38828.22s`
- absolute savings: `118600.80s` (`32.94` hours)
- relative speedup: `4.05x`
- runtime reduction: `75.34%`

In the same accounting convention:
- LLM calls: `11219 -> 8160` (`-27.27%`)
- total tokens: `10255794 -> 9299500` (`-9.32%`)
- estimated cost: `¥12.0572 -> ¥10.7245` (`-11.05%`)

### E1 -> E2: Warm-Cache Gain
- total duration: `38828.22s -> 38965.22s`
- end-to-end change: `+137.00s` (effectively flat)

But at the phase-one level:
- sync load: `82.08s -> 0.19s` (`-99.76%`)
- prefetch compute: `7633.76s -> 65.83s` (`-99.14%`)
- hidden pipeline seconds: `7633.76s -> 65.82s` (`-99.14%`)

This shows that artifact caching nearly eliminates repeated phase-one work, while the main end-to-end bottleneck after `E1` has already shifted to later persona-specific stages.

## Notes on Accounting

The call/token/cost reductions should be interpreted as values under the comparison-report accounting scheme. In the older baseline run, shared phase-one token usage is counted separately in the comparison data, while the newer execution path reports shared phase-one activity differently. We therefore use these values as report-level observations rather than as a claim about exactly identical physical API savings under every instrumentation scheme.