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+from __future__ import annotations
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+
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+import collections
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+import sqlite3
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+import sys
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+import typing
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+
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+import prepare
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+
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+def main() -> None:
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+ month, sqlite_path, run_id_s = sys.argv[1:]
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+ run_id = int(run_id_s)
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+
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+ db = sqlite3.connect(sqlite_path)
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+ db.row_factory = sqlite3.Row
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+
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+ cur = db.execute('SELECT started_at, finished_at, type, range, materials_failed FROM runs WHERE id = ?', (run_id,))
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+ (run,) = cur.fetchall()
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+ print(', '.join(f'{k}: {run[k]}' for k in run.keys())) # noqa: SIM118
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+ assert run['type'] == 'PRODUCTION' and run['range'] == 'DAYS_30' and run['materials_failed'] == 0
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+
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+ with open(f'rawData/{month}-prices.json', 'r') as f:
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+ prices = prepare.get_prices(f)
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+ prod_data, company_data = get_prod_and_company_data(db, run_id, prices)
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+
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+ with open(f'rawData/{month}.csv', 'r', newline='') as f:
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+ data = prepare.read_data(f)
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+ prepare.write_data(month, data, prod_data, company_data)
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+ prepare.check_missing_tickers(prod_data)
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+
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+def get_prod_and_company_data(db: sqlite3.Connection, run_id: int, prices: typing.Mapping[str, float]
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+ ) -> tuple[typing.Mapping[str, prepare.ProdData], typing.Mapping[str, typing.Any]]:
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+ cur = db.execute('''
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+ SELECT ticker, entity_id, score, rank FROM leaderboard_scores
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+ JOIN materials ON materials.material_pk = leaderboard_scores.material_rowid
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+ JOIN entities ON entities.id = leaderboard_scores.entity_rowid
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+ WHERE run_id = ?''', (run_id,))
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+
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+ individual: dict[str, dict[str, prepare.CompanyTickerData]] = collections.defaultdict(dict)
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+ company_totals: dict[str, prepare.CompanyTotals] = collections.defaultdict(lambda: {'volume': 0.0})
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+ universe_total_score: dict[str, float] = collections.defaultdict(int)
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+
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+ while row := cur.fetchone():
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+ ticker = row['ticker']
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+ price = prices[ticker]
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+ universe_total_score[ticker] += row['score']
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+ amount = row['score'] / 30
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+ volume = amount * price
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+ individual[row['entity_id']][ticker] = {
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+ 'amount': amount,
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+ 'volume': volume,
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+ 'rank': row['rank'],
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+ }
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+ company_totals[row['entity_id']]['volume'] += volume
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+
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+ prod: dict[str, prepare.ProdData] = {} # TODO: universe / 30
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+ for ticker, total_score in universe_total_score.items():
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+ amount = total_score / 30
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+ prod[ticker] = {'amount': amount, 'volume': amount * prices[ticker]}
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+
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+ company_data = {'totals': prepare.add_company_ranks(company_totals), 'individual': dict(individual)}
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+ return prod, company_data
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+
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+if __name__ == '__main__':
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+ main()
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