prepare.py 4.1 KB

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  1. from __future__ import annotations
  2. import collections
  3. import dataclasses
  4. import csv
  5. import json
  6. import sys
  7. import typing
  8. def main() -> None:
  9. (month,) = sys.argv[1:]
  10. with open(f'rawData/{month}.csv', 'r', newline='') as f:
  11. data = read_data(f)
  12. bases_data: dict[str, dict[str, int]] = {r.company_id: {'bases': r.num, 'rank': r.rank} for r in data['BASES']}
  13. with open(f'www/data/base-data-{month}.json', 'w', newline='') as f:
  14. json.dump(bases_data, f)
  15. with open(f'rawData/{month}-prices.json', 'r') as f:
  16. prices = get_prices(f)
  17. prod_data = get_prod_data(data, prices)
  18. with open(f'www/data/prod-data-{month}.json', 'w', newline='') as f:
  19. json.dump(prod_data, f)
  20. company_data = get_company_data(data, prices)
  21. with open(f'www/data/company-data-{month}.json', 'w', newline='') as f:
  22. json.dump(company_data, f)
  23. def read_data(f: typing.TextIO) -> dict[str, list[Row]]:
  24. data: dict[str, list[Row]] = collections.defaultdict(list)
  25. reader = csv.reader(f)
  26. for row in reader:
  27. data[row[0]].append(Row(int(row[1]), int(row[2]), row[3]))
  28. return data
  29. def get_prices(f: typing.TextIO) -> typing.Mapping[str, float]:
  30. raw_prices: typing.Sequence[Price] = json.load(f)
  31. volumes: dict[str, float] = collections.defaultdict(float)
  32. traded: dict[str, int] = collections.defaultdict(int)
  33. for price in raw_prices:
  34. if price['Traded30D'] is None:
  35. continue
  36. assert price['VWAP30D'] is not None
  37. volumes[price['MaterialTicker']] += price['VWAP30D'] * price['Traded30D']
  38. traded[price['MaterialTicker']] += price['Traded30D']
  39. prices = {ticker: volume / traded[ticker] for ticker, volume in volumes.items()}
  40. hardcoded_prices = {
  41. 'AFP': 116868,
  42. 'ANZ': 70601,
  43. 'BFP': 23408,
  44. 'BND': 230,
  45. 'BID': 47011,
  46. 'CRU': 169623,
  47. 'CQT': 378452,
  48. 'FUN': 124010,
  49. 'GCH': 18303,
  50. 'GNZ': 30361,
  51. 'HNZ': 93580,
  52. 'PFG': 2677222,
  53. 'PK': 869,
  54. 'RDS': 598170,
  55. 'SDM': 1721027,
  56. 'SST': 5863587,
  57. 'SU': 157860,
  58. 'TOR': 540169,
  59. 'VCB': 673713,
  60. 'WOR': 202000,
  61. }
  62. assert frozenset(prices).isdisjoint(hardcoded_prices)
  63. prices.update(hardcoded_prices)
  64. return prices
  65. def get_prod_data(data: dict[str, list[Row]], prices: typing.Mapping[str, float]) -> dict[str, ProdData]:
  66. prod: dict[str, ProdData] = {}
  67. for section, rows in data.items():
  68. if (ticker := get_production_ticker(section)) is None:
  69. continue
  70. price = prices.get(ticker)
  71. if price is None:
  72. continue
  73. amount = sum(row.num for row in rows) / 30
  74. prod[ticker] = {'amount': amount, 'volume': amount * price}
  75. return prod
  76. def get_company_data(data: dict[str, list[Row]], prices: typing.Mapping[str, float]) -> dict[str, typing.Any]:
  77. individual: dict[str, dict[str, CompanyTickerData]] = collections.defaultdict(dict)
  78. totals: dict[str, CompanyTotals] = collections.defaultdict(lambda: {'volume': 0.0})
  79. for section, rows in data.items():
  80. if (ticker := get_production_ticker(section)) is None:
  81. continue
  82. price = prices[ticker]
  83. for row in rows:
  84. amount = row.num / 30
  85. volume = amount * price
  86. individual[row.company_id][ticker] = {
  87. 'amount': amount,
  88. 'volume': volume,
  89. 'rank': row.rank,
  90. }
  91. totals[row.company_id]['volume'] += volume
  92. return {'totals': add_company_ranks(totals), 'individual': dict(individual)}
  93. def get_production_ticker(section: str) -> str | None:
  94. prefix = 'PRODUCTION_'
  95. suffix = '_DAYS_30'
  96. if not section.startswith(prefix) or not section.endswith(suffix):
  97. return None
  98. return section[len(prefix):-len(suffix)]
  99. def add_company_ranks(totals: dict[str, CompanyTotals]) -> dict[str, CompanyTotals]:
  100. ranked = sorted(totals.items(), key=lambda item: item[1]['volume'], reverse=True)
  101. for rank, (company_id, company_totals) in enumerate(ranked, start=1):
  102. company_totals['volumeRank'] = rank
  103. return totals
  104. @dataclasses.dataclass(frozen=True, slots=True, eq=False)
  105. class Row:
  106. rank: int
  107. num: int
  108. company_id: str
  109. class Price(typing.TypedDict):
  110. MaterialTicker: str
  111. VWAP30D: float | None
  112. Traded30D: int | None
  113. class ProdData(typing.TypedDict):
  114. amount: float
  115. volume: float
  116. class CompanyTickerData(typing.TypedDict):
  117. amount: float
  118. volume: float
  119. rank: int
  120. class CompanyTotals(typing.TypedDict, total=False):
  121. volume: float
  122. volumeRank: int
  123. if __name__ == '__main__':
  124. main()