28 lines
1.3 KiB
Python
28 lines
1.3 KiB
Python
with open('src/sentiment_engine/nlp/entity_extraction.py', 'r') as f:
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lines = f.readlines()
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new_lines = []
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for line in lines:
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stripped = line.strip()
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if stripped == '"MOVING", "HARD", "SOFT", "FAST", "SLOW", "BIG", "SMALL",':
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new_lines.append(' "MOVING", "HARD", "SOFT", "FAST", "SLOW", "BIG", "SMALL",\n')
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elif stripped == '"LONG", "SHORT", "HIGH", "LOW", "OPEN", "CLOSE",':
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new_lines.append(' "LONG", "SHORT", "HIGH", "LOW", "OPEN", "CLOSE",\n')
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elif stripped == '"BULL", "BEAR", "FLAT", "VOL", "VOLS",':
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new_lines.append(' "BULL", "BEAR", "FLAT", "VOL", "VOLS",\n')
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elif stripped == '"BID", "ASK", "MID", "VWAP", "TWAP",':
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new_lines.append(' "BID", "ASK", "MID", "VWAP", "TWAP",\n')
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elif stripped == '"RSI", "MACD", "BB", "EMA", "SMA", "WMA",':
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new_lines.append(' "RSI", "MACD", "BB", "EMA", "SMA", "WMA",\n')
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elif stripped == '"ATR", "ADX", "CCI", "STOCH", "RSI",':
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new_lines.append(' "ATR", "ADX", "CCI", "STOCH", "RSI",\n')
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elif stripped == '"K", "M", "B", "T", "MM", "BB", "TT",':
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new_lines.append(' "K", "M", "B", "T", "MM", "BB", "TT",\n')
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else:
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new_lines.append(line)
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with open('src/sentiment_engine/nlp/entity_extraction.py', 'w') as f:
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f.writelines(new_lines)
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print('Fixed indentation')
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