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run_batch_analysis

Data PipelineDataBatch

INSTALLATION

$python -c "exec(open('run_batch_analysis.py').read())"

#or paste directly into your Claude Code terminal

[ ABOUT ]

Uses Python's `concurrent.futures.ThreadPoolExecutor` to process multiple coins in parallel, defaulting to 10 concurrent workers. Each worker calls `analyze_coin` independently, and results are collected as futures complete. The `as_completed` iterator returns results in the order they finish, not the order they were submitted.

Parallelism reduces total analysis time from O(n × per_coin_time) to approximately O(per_coin_time) for n ≤ max_workers. On a typical machine with a fast internet connection, analyzing 100 coins with 10 workers takes about the same time as analyzing 10 coins serially. Increase max_workers cautiously — CoinGecko and Anthropic API rate limits can throttle concurrent requests.

[ USAGE EXAMPLE ]

example.py

coins = fetch_top100()
results = batch_analyze(coins, max_workers=10)
cache_analysis(results)

[ FULL CODE ]

run_batch_analysis.py

import concurrent.futures as cf def batch_analyze(coins, max_workers=10): with cf.ThreadPoolExecutor(max_workers) as ex: futures = {ex.submit(analyze_coin, c): c for c in coins} return [f.result() for f in cf.as_completed(futures)]

[ METADATA ]

CategoryData Pipeline

ComplexityIntermediate

LanguagePython 3.10+

TagsData, Batch

[ ASK CLAUDE ]

Ask AI about this skill before installing it.

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