prepare.py 4.9 KB

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  1. from __future__ import annotations
  2. import httpx
  3. import collections
  4. import dataclasses
  5. import csv
  6. import json
  7. import sys
  8. import typing
  9. def main() -> None:
  10. (month,) = sys.argv[1:]
  11. with open(f'rawData/{month}.csv', 'r', newline='') as f:
  12. data = read_data(f)
  13. bases_data: dict[str, dict[str, int]] = {r.company_id: {'bases': r.num, 'rank': r.rank} for r in data['BASES']}
  14. with open(f'www/data/base-data-{month}.json', 'w') as f:
  15. json.dump(bases_data, f)
  16. ships_data: dict[str, dict[str, int]] = {r.company_id: {'ships': r.num, 'rank': r.rank} for r in data['SHIPS']}
  17. with open(f'www/data/ship-data-{month}.json', 'w') as f:
  18. json.dump(ships_data, f)
  19. with open(f'rawData/{month}-prices.json', 'r') as f:
  20. prices = get_prices(f)
  21. prod_data, company_data = get_prod_and_company_data(data, prices)
  22. with open(f'www/data/prod-data-{month}.json', 'w') as f:
  23. json.dump(prod_data, f)
  24. with open(f'www/data/company-data-{month}.json', 'w') as f:
  25. json.dump(company_data, f)
  26. with open('www/data/universe-data.json', 'r+') as f:
  27. universe_data = json.load(f)
  28. universe_data[month] = {
  29. 'volume': sum(mat['volume'] for mat in prod_data.values()),
  30. 'profit': None,
  31. 'bases': sum(co['bases'] for co in bases_data.values()),
  32. 'companies': len(company_data['totals']),
  33. }
  34. f.seek(0)
  35. json.dump(universe_data, f)
  36. f.truncate()
  37. response = httpx.get('https://api.fnar.net/material').raise_for_status()
  38. tickers = frozenset(mat['Ticker'] for mat in response.json() if mat['Ticker'] != 'CMK')
  39. if missing := tickers - prod_data.keys():
  40. print('warning: missing production data for tickers', missing)
  41. def read_data(f: typing.TextIO) -> dict[str, list[Row]]:
  42. data: dict[str, list[Row]] = collections.defaultdict(list)
  43. reader = csv.reader(f)
  44. for row in reader:
  45. data[row[0]].append(Row(int(row[1]), int(row[2]), row[3]))
  46. return data
  47. def get_prices(f: typing.TextIO) -> typing.Mapping[str, float]:
  48. raw_prices: typing.Sequence[Price] = json.load(f)
  49. volumes: dict[str, float] = collections.defaultdict(float)
  50. traded: dict[str, int] = collections.defaultdict(int)
  51. for price in raw_prices:
  52. if price['Traded30D'] is None:
  53. continue
  54. assert price['VWAP30D'] is not None
  55. volumes[price['MaterialTicker']] += price['VWAP30D'] * price['Traded30D']
  56. traded[price['MaterialTicker']] += price['Traded30D']
  57. prices = {ticker: volume / traded[ticker] for ticker, volume in volumes.items()}
  58. hardcoded_prices = {
  59. 'AFP': 65638,
  60. 'ANZ': 70601,
  61. 'ARP': 8457,
  62. 'BID': 55692,
  63. 'BFP': 23408,
  64. 'DD': 30111,
  65. 'GCH': 18303,
  66. 'GEN': 232097,
  67. 'GNZ': 30361,
  68. 'GWS': 9778478,
  69. 'HAM': 4686751,
  70. 'HNZ': 93580,
  71. 'IMM': 101522,
  72. 'JUI': 0,
  73. 'LU': 95730,
  74. 'PFG': 2677222,
  75. 'RDS': 598170,
  76. 'SDM': 1721027,
  77. 'SST': 5863587,
  78. 'SU': 157860,
  79. 'SUD': 84327,
  80. 'TAC': 245797,
  81. 'TOR': 540169,
  82. 'VCB': 673713,
  83. 'VFT': 1781416,
  84. 'VOE': 3699358,
  85. 'VOR': 2547315,
  86. 'VSC': 39446,
  87. }
  88. assert frozenset(prices).isdisjoint(hardcoded_prices), frozenset(prices).intersection(hardcoded_prices)
  89. prices.update(hardcoded_prices)
  90. return prices
  91. def get_prod_and_company_data(data: dict[str, list[Row]], prices: typing.Mapping[str, float]
  92. ) -> tuple[typing.Mapping[str, ProdData], typing.Mapping[str, typing.Any]]:
  93. prod: dict[str, ProdData] = {}
  94. individual: dict[str, dict[str, CompanyTickerData]] = collections.defaultdict(dict)
  95. totals: dict[str, CompanyTotals] = collections.defaultdict(lambda: {'volume': 0.0})
  96. for section, rows in data.items():
  97. if (ticker := get_production_ticker(section)) is None:
  98. continue
  99. price = prices[ticker]
  100. prod_amount = sum(row.num for row in rows) / 30
  101. prod[ticker] = {'amount': prod_amount, 'volume': prod_amount * price}
  102. for row in rows:
  103. amount = row.num / 30
  104. volume = amount * price
  105. individual[row.company_id][ticker] = {
  106. 'amount': amount,
  107. 'volume': volume,
  108. 'rank': row.rank,
  109. }
  110. totals[row.company_id]['volume'] += volume
  111. company_data = {'totals': add_company_ranks(totals), 'individual': dict(individual)}
  112. return prod, company_data
  113. def get_production_ticker(section: str) -> str | None:
  114. prefix = 'PRODUCTION_'
  115. suffix = '_DAYS_30'
  116. if not section.startswith(prefix) or not section.endswith(suffix):
  117. return None
  118. return section[len(prefix):-len(suffix)]
  119. def add_company_ranks(totals: dict[str, CompanyTotals]) -> dict[str, CompanyTotals]:
  120. ranked = sorted(totals.items(), key=lambda item: item[1]['volume'], reverse=True)
  121. for rank, (company_id, company_totals) in enumerate(ranked, start=1):
  122. company_totals['volumeRank'] = rank
  123. return totals
  124. @dataclasses.dataclass(frozen=True, slots=True, eq=False)
  125. class Row:
  126. rank: int
  127. num: int
  128. company_id: str
  129. class Price(typing.TypedDict):
  130. MaterialTicker: str
  131. VWAP30D: float | None
  132. Traded30D: int | None
  133. class ProdData(typing.TypedDict):
  134. amount: float
  135. volume: float
  136. class CompanyTickerData(typing.TypedDict):
  137. amount: float
  138. volume: float
  139. rank: int
  140. class CompanyTotals(typing.TypedDict, total=False):
  141. volume: float
  142. volumeRank: int
  143. if __name__ == '__main__':
  144. main()