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1 | 1 | import os
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2 |
| -import pandas as pd # type: ignore |
| 2 | +import pandas as pd # type: ignore |
3 | 3 | from collections import defaultdict
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4 | 4 | import pickle
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5 | 5 | import json
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| 6 | +from typing import DefaultDict, Dict, Any, BinaryIO |
6 | 7 |
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7 | 8 | # Directory containing the daily CSV files
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8 | 9 | data_dir = "./aggregates_day/"
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34 | 35 | print("Building lookup table...")
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35 | 36 |
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36 | 37 | # Now, build the lookup table with rolling averages and percentage price change
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37 |
| -lookup_table = defaultdict(dict) # Nested dict: ticker -> date -> stats |
| 38 | +lookup_table: DefaultDict[str, Dict[str, Any]] = defaultdict( |
| 39 | + dict |
| 40 | +) # Nested dict: ticker -> date -> stats |
38 | 41 |
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39 | 42 | for ticker, records in trades_data.items():
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40 | 43 | # Convert records to DataFrame
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79 | 82 | print("Lookup table built successfully.")
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80 | 83 |
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81 | 84 | # Convert defaultdict to regular dict for JSON serialization
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82 |
| -lookup_table = {k: v for k, v in lookup_table.items()} |
| 85 | +lookup_table_dict = {k: v for k, v in lookup_table.items()} |
83 | 86 |
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84 | 87 | # Save the lookup table to a JSON file
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85 | 88 | with open("lookup_table.json", "w") as f:
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86 |
| - json.dump(lookup_table, f, indent=4) |
| 89 | + json.dump(lookup_table_dict, f, indent=4) |
87 | 90 |
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88 | 91 | print("Lookup table saved to 'lookup_table.json'.")
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89 | 92 |
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90 | 93 | # Save the lookup table to a file for later use
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91 |
| -with open("lookup_table.pkl", "wb") as f: |
92 |
| - pickle.dump(lookup_table, f) |
| 94 | +with open("lookup_table.pkl", "wb") as f: # type: BinaryIO |
| 95 | + pickle.dump(lookup_table_dict, f) |
93 | 96 |
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94 | 97 | print("Lookup table saved to 'lookup_table.pkl'.")
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