bugfixing options bubble data
This commit is contained in:
MuslemRahimi
parent
1ee1e10e72
commit
0513fced3d
@ -119,7 +119,7 @@ async def download_data(ticker, con, start_date, end_date, skip_downloading):
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#Threshold of enough datapoints needed!
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if len(ratios) < 50:
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print('Not enough data points')
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print(f'Not enough data points for {ticker}')
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return
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@ -225,7 +225,7 @@ async def download_data(ticker, con, start_date, end_date, skip_downloading):
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# Compute combinations for each group of columns
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compute_column_ratios(fundamental_columns, df_combined, new_columns)
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compute_column_ratios(stats_columns, df_combined, new_columns)
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#compute_column_ratios(ta_columns, df_combined, new_columns)
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compute_column_ratios(ta_columns, df_combined, new_columns)
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# Concatenate the new ratio columns with the original DataFrame
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df_combined = pd.concat([df_combined, pd.DataFrame(new_columns, index=df_combined.index)], axis=1)
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@ -272,6 +272,7 @@ async def chunked_gather(tickers, con, skip_downloading, chunk_size):
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chunk_results = await asyncio.gather(*tasks)
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train_list = []
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test_list = []
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for ticker, df in zip(chunk, chunk_results):
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try:
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@ -280,24 +281,19 @@ async def chunked_gather(tickers, con, skip_downloading, chunk_size):
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train_data = df.iloc[:split_size]
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test_data = df.iloc[split_size:]
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# Store test data for this ticker in a dictionary
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df_test_dict[ticker] = test_data
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# Append train data for combined training
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train_list.append(train_data)
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# Collect all test data for overall evaluation
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all_test_data.append(test_data)
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test_list.append(test_data)
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except:
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pass
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# Concatenate all train data together
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if train_list:
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df_train = pd.concat(train_list, ignore_index=True)
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df_train = pd.concat(train_list, ignore_index=True)
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df_test = pd.concat(test_list, ignore_index=True)
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# Shuffle the combined training data
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df_train = df_train.sample(frac=1, random_state=42).reset_index(drop=True)
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df_test = df_test.sample(frac=1, random_state=42).reset_index(drop=True)
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print('====== Start Training Model on Combined Data ======')
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predictor = ScorePredictor()
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@ -308,40 +304,50 @@ async def chunked_gather(tickers, con, skip_downloading, chunk_size):
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print(f'Training complete on {len(df_train)} samples.')
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# Evaluate the model on the overall test dataset
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if all_test_data:
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overall_test_data = pd.concat(all_test_data, ignore_index=True)
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print('====== Evaluating on Overall Test Dataset ======')
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overall_evaluation_data = predictor.evaluate_model(overall_test_data[selected_features], overall_test_data['Target'])
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print(f'Overall Evaluation Metrics: {overall_evaluation_data}')
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# Evaluate the model for each ticker separately
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for ticker, test_data in df_test_dict.items():
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try:
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print(f"Fine-tuning the model for {ticker}")
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predictor.fine_tune_model(df_train[selected_features], df_train['Target'])
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print(f"Evaluating model for {ticker}")
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data = predictor.evaluate_model(test_data[selected_features], test_data['Target'])
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# Check if the evaluation data meets the criteria
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if (data['precision'] >= 50 and data['accuracy'] >= 50 and
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data['accuracy'] < 100 and data['precision'] < 100 and
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data['f1_score'] >= 50 and data['recall_score'] >= 50 and
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data['roc_auc_score'] >= 50):
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# Save the evaluation data to a JSON file
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await save_json(ticker, data)
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print(f"Saved results for {ticker}")
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except Exception as e:
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print(e)
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pass
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print('====== Evaluating on Overall Test Dataset ======')
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data = predictor.evaluate_model(df_test[selected_features], df_test['Target'])
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print(f'Overall Evaluation Metrics: {data}')
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async def warm_start_training(tickers, con, skip_downloading):
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dfs = await chunked_gather(tickers, con, skip_downloading, chunk_size=100)
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async def fine_tune_and_evaluate(ticker, con, start_date, end_date, test_size, skip_downloading):
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try:
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df_train = pd.DataFrame()
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df_test_dict = {} # Store test data for each ticker
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all_test_data = [] # Store all test data for overall evaluation
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df = await download_data(ticker, con, start_date, end_date, skip_downloading)
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split_size = int(len(df) * (1 - test_size))
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df_train = df.iloc[:split_size]
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df_test = df.iloc[split_size:]
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# Shuffle the combined training data
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df_train = df_train.sample(frac=1, random_state=42).reset_index(drop=True)
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print('====== Start Fine-tuning Model ======')
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predictor = ScorePredictor()
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selected_features = [col for col in df_train if col not in ['price', 'date', 'Target']]
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# Train the model on the combined training data
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predictor.fine_tune_model(df_train[selected_features], df_train['Target'])
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print(f'Training complete on {len(df_train)} samples.')
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print(f"Evaluating model for {ticker}")
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data = predictor.evaluate_model(df_test[selected_features], df_test['Target'])
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print(f'Overall Evaluation Metrics: {data}')
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if (data['precision'] >= 50 and data['accuracy'] >= 50 and
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data['accuracy'] < 100 and data['precision'] < 100 and
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data['f1_score'] >= 50 and data['recall_score'] >= 50 and
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data['roc_auc_score'] >= 50):
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# Save the evaluation data to a JSON file
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await save_json(ticker, data)
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print(f"Saved results for {ticker}")
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except:
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pass
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async def run():
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train_mode = True # Set this to False for fine-tuning and evaluation
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skip_downloading = False
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@ -351,6 +357,14 @@ async def run():
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if train_mode:
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# Warm start training
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warm_start_symbols = list(set(['APO','UNM','CVS','SAVE','SIRI','EA','TTWO','NTDOY','GRC','ODP','IMAX','YUM','UPS','FI','DE','MDT','INFY','ICE','SNY','HON','BSX','C','ADP','CB','LOW','PFE','RTX','DIS','MS','BHP','BAC','PG','BABA','ACN','TMO','LLY','XOM','JPM','UNH','COST','HD','ASML','BRK-A','BRK-B','CAT','TT','SAP','APH','CVS','NOG','DVN','COP','OXY','MRO','MU','AVGO','INTC','LRCX','PLD','AMT','JNJ','ACN','TSM','V','ORCL','MA','BAC','BA','NFLX','ADBE','IBM','GME','NKE','ANGO','PNW','SHEL','XOM','WMT','BUD','AMZN','PEP','AMD','NVDA','AWR','TM','AAPL','GOOGL','META','MSFT','LMT','TSLA','DOV','PG','KO']))
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print(f'Warm Start Training: Total Tickers {len(warm_start_symbols)}')
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await warm_start_training(warm_start_symbols, con, skip_downloading)
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else:
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start_date = datetime(1995, 1, 1).strftime("%Y-%m-%d")
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end_date = datetime.today().strftime("%Y-%m-%d")
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test_size = 0.2
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cursor.execute("""
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SELECT DISTINCT symbol
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FROM stocks
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@ -358,11 +372,11 @@ async def run():
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AND symbol NOT LIKE '%.%'
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AND symbol NOT LIKE '%-%'
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""")
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warm_start_symbols = ['PEP'] #[row[0] for row in cursor.fetchall()]
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stock_symbols = [row[0] for row in cursor.fetchall()]
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for ticker in tqdm(stock_symbols):
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await fine_tune_and_evaluate(ticker, con, start_date, end_date, test_size, skip_downloading)
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print(f'Warm Start Training: Total Tickers {len(warm_start_symbols)}')
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await warm_start_training(warm_start_symbols, con, skip_downloading)
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con.close()
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if __name__ == "__main__":
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@ -42,11 +42,13 @@ def options_bubble_data(chunk):
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start_date_str = start_date.strftime('%Y-%m-%d')
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res_list = []
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for page in range(0, 5000):
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page = 0
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while True:
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try:
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data = fin.options_activity(company_tickers=company_tickers, page=page, pagesize=1000, date_from=start_date_str, date_to=end_date_str)
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data = ujson.loads(fin.output(data))['option_activity']
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res_list += data
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page +=1
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except:
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break
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@ -54,33 +56,39 @@ def options_bubble_data(chunk):
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for option_type in ['CALL', 'PUT']:
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for item in res_filtered:
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if item['put_call'].upper() == option_type:
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item['dte'] = calculate_dte(item['date_expiration'])
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if item['ticker'] in ['BRK.A', 'BRK.B']:
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item['ticker'] = f"BRK-{item['ticker'][-1]}"
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try:
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if item['put_call'].upper() == option_type:
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item['dte'] = calculate_dte(item['date_expiration'])
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if item['ticker'] in ['BRK.A', 'BRK.B']:
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item['ticker'] = f"BRK-{item['ticker'][-1]}"
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except:
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pass
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#Save raw data for each ticker for options page stack bar chart
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for ticker in chunk:
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ticker_filtered_data = [entry for entry in res_filtered if entry['ticker'] == ticker]
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if len(ticker_filtered_data) != 0:
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#sum up calls and puts for each day for the plot
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summed_data = {}
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for entry in ticker_filtered_data:
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volume = int(entry['volume'])
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open_interest = int(entry['open_interest'])
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put_call = entry['put_call']
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if entry['date'] not in summed_data:
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summed_data[entry['date']] = {'CALL': {'volume': 0, 'open_interest': 0}, 'PUT': {'volume': 0, 'open_interest': 0}}
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summed_data[entry['date']][put_call]['volume'] += volume
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summed_data[entry['date']][put_call]['open_interest'] += open_interest
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try:
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ticker_filtered_data = [entry for entry in res_filtered if entry['ticker'] == ticker]
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if len(ticker_filtered_data) != 0:
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#sum up calls and puts for each day for the plot
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summed_data = {}
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for entry in ticker_filtered_data:
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volume = int(entry['volume'])
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open_interest = int(entry['open_interest'])
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put_call = entry['put_call']
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if entry['date'] not in summed_data:
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summed_data[entry['date']] = {'CALL': {'volume': 0, 'open_interest': 0}, 'PUT': {'volume': 0, 'open_interest': 0}}
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summed_data[entry['date']][put_call]['volume'] += volume
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summed_data[entry['date']][put_call]['open_interest'] += open_interest
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result_list = [{'date': date, 'CALL': summed_data[date]['CALL'], 'PUT': summed_data[date]['PUT']} for date in summed_data]
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#reverse the list
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result_list = result_list[::-1]
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with open(f"json/options-flow/company/{ticker}.json", 'w') as file:
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ujson.dump(result_list, file)
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result_list = [{'date': date, 'CALL': summed_data[date]['CALL'], 'PUT': summed_data[date]['PUT']} for date in summed_data]
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#reverse the list
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result_list = result_list[::-1]
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with open(f"json/options-flow/company/{ticker}.json", 'w') as file:
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ujson.dump(result_list, file)
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except:
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pass
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#Save bubble data for each ticker for overview page
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for ticker in chunk:
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@ -131,7 +139,7 @@ async def main():
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chunk_size = len(total_symbols) // 2000 # Divide the list into N chunks
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chunks = [total_symbols[i:i + chunk_size] for i in range(0, len(total_symbols), chunk_size)]
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print(chunks)
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loop = asyncio.get_running_loop()
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with ThreadPoolExecutor(max_workers=4) as executor:
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tasks = [loop.run_in_executor(executor, options_bubble_data, chunk) for chunk in chunks]
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Binary file not shown.
@ -23,7 +23,7 @@ class StockPredictor:
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self.ticker = ticker
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self.start_date = start_date
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self.end_date = end_date
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self.nth_day = 60
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self.nth_day = 10
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self.model = None #RandomForestClassifier(n_estimators=3500, min_samples_split=100, random_state=42, n_jobs=-1) #XGBClassifier(n_estimators=200, max_depth=2, learning_rate=1, objective='binary:logistic')
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self.horizons = [3,5,10, 15, 20]
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self.test_size = 0.2
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@ -134,19 +134,19 @@ class StockPredictor:
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model.add(Dropout(0.2))
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model.add(Dense(units=1, activation='sigmoid'))
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# Learning rate scheduler
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reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=0.001)
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# Early stopping
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early_stop = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)
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model.compile(optimizer=Adam(lr=0.001), loss='binary_crossentropy', metrics=['accuracy'])
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model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
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return model, [reduce_lr, early_stop]
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return model
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def train_model(self, X_train, y_train):
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self.model, callbacks = self.build_lstm_model((X_train.shape[1], X_train.shape[2]))
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history = self.model.fit(X_train, y_train, epochs=500, batch_size=32, validation_split=0.1, callbacks=callbacks)
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# Learning rate scheduler
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#reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=0.001)
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# Early stopping
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early_stop = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)
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self.model = self.build_lstm_model((X_train.shape[1], X_train.shape[2]))
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history = self.model.fit(X_train, y_train, epochs=500, batch_size=1024, validation_split=0.1, callbacks=[early_stop])
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def evaluate_model(self, X_test, y_test):
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# Reshape X_test to remove the extra dimension
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@ -202,7 +202,7 @@ if __name__ == "__main__":
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X = df[predictors].values
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y = df['Target'].values
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print(df)
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# Normalize features
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scaler = MinMaxScaler(feature_range=(0, 1))
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X = scaler.fit_transform(X)
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@ -2,8 +2,13 @@ import pandas as pd
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from datetime import datetime, timedelta
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import numpy as np
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from xgboost import XGBClassifier
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from sklearn.ensemble import StackingClassifier, RandomForestClassifier
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from sklearn.neighbors import KNeighborsClassifier
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from sklearn.naive_bayes import GaussianNB
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from sklearn.svm import SVC
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from sklearn.linear_model import LogisticRegression
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from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, accuracy_score
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from sklearn.preprocessing import MinMaxScaler
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from sklearn.preprocessing import MinMaxScaler, StandardScaler
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from sklearn.decomposition import PCA
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import lightgbm as lgb
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@ -19,97 +24,119 @@ import os
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class ScorePredictor:
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def __init__(self):
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self.scaler = MinMaxScaler()
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self.pca = PCA(n_components=0.95) # Retain components explaining 95% variance
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self.warm_start_model_path = 'ml_models/weights/ai-score/warm_start_weights.pkl'
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self.model = lgb.LGBMClassifier(
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n_estimators=20_000, # Number of boosting iterations - good balance between performance and training time
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learning_rate=0.001, # Smaller learning rate for better generalization
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max_depth=6, # Controlled depth to prevent overfitting
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num_leaves=2**6-1, # 2^max_depth, prevents overfitting while maintaining model complexity
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colsample_bytree=0.1,
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n_jobs=10, # Use N CPU cores
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verbose=0, # Reduce output noise
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self.scaler = StandardScaler()
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self.pca = PCA(n_components=0.95)
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# Define base models
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self.xgb_model = XGBClassifier(
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n_estimators=100,
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max_depth=10,
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learning_rate=0.001,
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random_state=42,
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n_jobs=10,
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tree_method='gpu_hist',
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)
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'''
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XGBClassifier(
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n_estimators=200,
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max_depth=5,
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learning_rate=0.1,
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random_state=42,
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self.lgb_model = lgb.LGBMClassifier(
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n_estimators=100,
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learning_rate=0.001,
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max_depth=10,
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n_jobs=10
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)
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'''
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self.rf_model = RandomForestClassifier(
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n_estimators=100,
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max_depth=10,
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random_state=42,
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n_jobs=10
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)
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self.svc_model = SVC(probability=True, kernel='rbf')
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self.knn_model = KNeighborsClassifier(n_neighbors=5)
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self.nb_model = GaussianNB()
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# Stacking ensemble (XGBoost + LightGBM) with Logistic Regression as meta-learner
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self.model = StackingClassifier(
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estimators=[
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('xgb', self.xgb_model),
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#('lgb', self.lgb_model),
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('rf', self.rf_model),
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('svc', self.svc_model),
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('knn', self.knn_model),
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('nb', self.nb_model)
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],
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final_estimator=LogisticRegression(),
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n_jobs=10
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)
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self.warm_start_model_path = 'ml_models/weights/ai-score/stacking_weights.pkl'
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def preprocess_train_data(self, X):
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"""Preprocess training data by scaling and applying PCA."""
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X = np.where(np.isinf(X), np.nan, X)
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X = np.nan_to_num(X)
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X = self.scaler.fit_transform(X) # Transform using the fitted scaler
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return self.pca.fit_transform(X) # Fit PCA and transform
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X = self.scaler.fit_transform(X)
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return self.pca.fit_transform(X)
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def preprocess_test_data(self, X):
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"""Preprocess test data by scaling and applying PCA."""
|
||||
X = np.where(np.isinf(X), np.nan, X)
|
||||
X = np.nan_to_num(X)
|
||||
X = self.scaler.transform(X) # Transform using the fitted scaler
|
||||
return self.pca.transform(X) # Transform using the fitted PCA
|
||||
X = self.scaler.transform(X)
|
||||
return self.pca.transform(X)
|
||||
|
||||
def warm_start_training(self, X_train, y_train):
|
||||
X_train = self.preprocess_train_data(X_train)
|
||||
if os.path.exists(self.warm_start_model_path):
|
||||
with open(f'{self.warm_start_model_path}', 'rb') as f:
|
||||
with open(self.warm_start_model_path, 'rb') as f:
|
||||
self.model = pickle.load(f)
|
||||
self.model.fit(X_train, y_train)
|
||||
pickle.dump(self.model, open(f'{self.warm_start_model_path}', 'wb'))
|
||||
pickle.dump(self.model, open(self.warm_start_model_path, 'wb'))
|
||||
print("Warm start model saved.")
|
||||
|
||||
|
||||
def fine_tune_model(self, X_train, y_train):
|
||||
X_train = self.preprocess_train_data(X_train)
|
||||
with open(f'{self.warm_start_model_path}', 'rb') as f:
|
||||
with open(self.warm_start_model_path, 'rb') as f:
|
||||
self.model = pickle.load(f)
|
||||
|
||||
self.model.fit(X_train, y_train)
|
||||
print("Model fine-tuned")
|
||||
|
||||
|
||||
def evaluate_model(self, X_test, y_test):
|
||||
X_test = self.preprocess_test_data(X_test)
|
||||
|
||||
test_predictions = self.model.predict_proba(X_test)
|
||||
class_1_probabilities = test_predictions[:, 1]
|
||||
binary_predictions = (class_1_probabilities >= 0.5).astype(int)
|
||||
#print(test_predictions)
|
||||
|
||||
# Calculate and print metrics
|
||||
test_precision = precision_score(y_test, binary_predictions)
|
||||
test_accuracy = accuracy_score(y_test, binary_predictions)
|
||||
test_f1_score = f1_score(y_test, binary_predictions)
|
||||
test_recall_score = recall_score(y_test, binary_predictions)
|
||||
test_roc_auc_score = roc_auc_score(y_test, binary_predictions)
|
||||
|
||||
print("Test Set Metrics:")
|
||||
print(f"Precision: {round(test_precision * 100)}%")
|
||||
print(f"Accuracy: {round(test_accuracy * 100)}%")
|
||||
|
||||
print(f"Test Precision: {round(test_precision * 100)}%")
|
||||
print(f"Test Accuracy: {round(test_accuracy * 100)}%")
|
||||
print(f"F1 Score: {round(test_f1_score * 100)}%")
|
||||
print(f"Recall Score: {round(test_recall_score * 100)}%")
|
||||
print(f"ROC AUC Score: {round(test_roc_auc_score * 100)}%")
|
||||
|
||||
print(pd.DataFrame({'y_test': y_test, 'y_pred': binary_predictions}))
|
||||
print(f"Recall: {round(test_recall_score * 100)}%")
|
||||
print(f"ROC AUC: {round(test_roc_auc_score * 100)}%")
|
||||
|
||||
last_prediction_prob = class_1_probabilities[-1]
|
||||
print(f"Last prediction probability: {last_prediction_prob}")
|
||||
|
||||
thresholds = [0.8, 0.75, 0.7, 0.6, 0.5, 0.45, 0.4, 0.35, 0.3, 0]
|
||||
scores = [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]
|
||||
|
||||
last_prediction_prob = class_1_probabilities[-1]
|
||||
score = None
|
||||
print(f"Last prediction probability: {last_prediction_prob}")
|
||||
|
||||
score = None
|
||||
for threshold, value in zip(thresholds, scores):
|
||||
if last_prediction_prob >= threshold:
|
||||
score = value
|
||||
break
|
||||
|
||||
return {'accuracy': round(test_accuracy * 100),
|
||||
'precision': round(test_precision * 100),
|
||||
'f1_score': round(test_f1_score * 100),
|
||||
'recall_score': round(test_recall_score * 100),
|
||||
'roc_auc_score': round(test_roc_auc_score * 100),
|
||||
'score': score}
|
||||
return {
|
||||
'accuracy': round(test_accuracy * 100),
|
||||
'precision': round(test_precision * 100),
|
||||
'f1_score': round(test_f1_score * 100),
|
||||
'recall_score': round(test_recall_score * 100),
|
||||
'roc_auc_score': round(test_roc_auc_score * 100),
|
||||
'score': score
|
||||
}
|
||||
@ -283,10 +283,7 @@ def run_executive():
|
||||
|
||||
def run_options_bubble_ticker():
|
||||
week = datetime.today().weekday()
|
||||
current_time = datetime.now().time()
|
||||
start_time = datetime_time(15, 30)
|
||||
end_time = datetime_time(22, 30)
|
||||
if week <= 4 and start_time <= current_time < end_time:
|
||||
if week <= 4:
|
||||
run_command(["python3", "cron_options_bubble.py"])
|
||||
|
||||
command = ["sudo", "rsync", "-avz", "-e", "ssh", "/root/backend/app/json/options-bubble", f"root@{useast_ip_address}:/root/backend/app/json"]
|
||||
|
||||
Binary file not shown.
@ -94,19 +94,20 @@ def generate_ta_features(df):
|
||||
df_features['aroon_indicator'] = aroon.aroon_indicator()
|
||||
df_features['aroon_up'] = aroon.aroon_up()
|
||||
|
||||
df_features['ultimate_oscillator'] = UltimateOscillator(high=df['high'], low=df['low'], close=df['close']).ultimate_oscillator()
|
||||
df_features['choppiness'] = 100 * np.log10((df['high'].rolling(window=60).max() - df['low'].rolling(window=30).min()) / df_features['atr']) / np.log10(14)
|
||||
#df_features['ultimate_oscillator'] = UltimateOscillator(high=df['high'], low=df['low'], close=df['close']).ultimate_oscillator()
|
||||
#df_features['choppiness'] = 100 * np.log10((df['high'].rolling(window=60).max() - df['low'].rolling(window=30).min()) / df_features['atr']) / np.log10(14)
|
||||
df_features['ulcer'] = UlcerIndex(df['close'],window=60).ulcer_index()
|
||||
df_features['keltner_hband'] = keltner_channel_hband_indicator(high=df['high'],low=df['low'],close=df['close'],window=60)
|
||||
df_features['keltner_lband'] = keltner_channel_lband_indicator(high=df['high'],low=df['low'],close=df['close'],window=60)
|
||||
#df_features['keltner_hband'] = keltner_channel_hband_indicator(high=df['high'],low=df['low'],close=df['close'],window=60)
|
||||
#df_features['keltner_lband'] = keltner_channel_lband_indicator(high=df['high'],low=df['low'],close=df['close'],window=60)
|
||||
|
||||
df_features = df_features.dropna()
|
||||
return df_features
|
||||
|
||||
def generate_statistical_features(df, windows=[50,200], price_col='close',
|
||||
def generate_statistical_features(df, windows=[20,50,200], price_col='close',
|
||||
high_col='high', low_col='low', volume_col='volume'):
|
||||
"""
|
||||
Generate comprehensive statistical features for financial time series data.
|
||||
Focuses purely on statistical measures without technical indicators.
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
@ -132,7 +133,6 @@ def generate_statistical_features(df, windows=[50,200], price_col='close',
|
||||
# Create a copy of the dataframe to avoid modifying the original
|
||||
df_features = df.copy()
|
||||
|
||||
|
||||
# Calculate features for each window size
|
||||
for window in windows:
|
||||
# Returns
|
||||
@ -144,11 +144,18 @@ def generate_statistical_features(df, windows=[50,200], price_col='close',
|
||||
df_features[f'log_returns_std_{window}'] = log_returns.rolling(window=window).std()
|
||||
|
||||
# Statistical moments
|
||||
df_features[f'mean_{window}'] = df[price_col].rolling(window=window).mean()
|
||||
df_features[f'std_{window}'] = df[price_col].rolling(window=window).std()
|
||||
df_features[f'var_{window}'] = df[price_col].rolling(window=window).var()
|
||||
df_features[f'skew_{window}'] = df[price_col].rolling(window=window).skew()
|
||||
df_features[f'kurt_{window}'] = df[price_col].rolling(window=window).kurt()
|
||||
|
||||
# Quantile measures
|
||||
df_features[f'quantile_25_{window}'] = df[price_col].rolling(window=window).quantile(0.25)
|
||||
df_features[f'quantile_75_{window}'] = df[price_col].rolling(window=window).quantile(0.75)
|
||||
df_features[f'iqr_{window}'] = (
|
||||
df_features[f'quantile_75_{window}'] - df_features[f'quantile_25_{window}'])
|
||||
|
||||
# Volatility measures
|
||||
df_features[f'realized_vol_{window}'] = (
|
||||
df_features[f'returns_{window}'].rolling(window=window).std() * np.sqrt(252))
|
||||
@ -156,33 +163,48 @@ def generate_statistical_features(df, windows=[50,200], price_col='close',
|
||||
(df[high_col].rolling(window=window).max() -
|
||||
df[low_col].rolling(window=window).min()) / df[price_col])
|
||||
|
||||
# Z-scores and normalized prices
|
||||
# Z-scores and normalized values
|
||||
df_features[f'zscore_{window}'] = (
|
||||
(df[price_col] - df[price_col].rolling(window=window).mean()) /
|
||||
df[price_col].rolling(window=window).std())
|
||||
|
||||
|
||||
# Price dynamics
|
||||
|
||||
# Volume statistics
|
||||
df_features[f'volume_mean_{window}'] = df[volume_col].rolling(window=window).mean()
|
||||
df_features[f'volume_std_{window}'] = df[volume_col].rolling(window=window).std()
|
||||
df_features[f'volume_zscore_{window}'] = (
|
||||
(df[volume_col] - df[volume_col].rolling(window=window).mean()) /
|
||||
df[volume_col].rolling(window=window).std())
|
||||
df_features[f'volume_skew_{window}'] = df[volume_col].rolling(window=window).skew()
|
||||
df_features[f'volume_kurt_{window}'] = df[volume_col].rolling(window=window).kurt()
|
||||
|
||||
# Price-volume correlations
|
||||
df_features[f'price_volume_corr_{window}'] = (
|
||||
df[price_col].rolling(window=window)
|
||||
.corr(df[volume_col]))
|
||||
|
||||
# Higher-order moments of returns
|
||||
returns = df[price_col].pct_change()
|
||||
df_features[f'returns_skew_{window}'] = returns.rolling(window=window).skew()
|
||||
df_features[f'returns_kurt_{window}'] = returns.rolling(window=window).kurt()
|
||||
|
||||
# Cross-sectional statistics
|
||||
df_features['price_acceleration'] = df[price_col].diff().diff()
|
||||
df_features['momentum_change'] = df[price_col].pct_change().diff()
|
||||
df_features['returns_acceleration'] = df[price_col].pct_change().diff()
|
||||
|
||||
# Advanced volatility
|
||||
# Advanced volatility estimators
|
||||
df_features['parkinson_vol'] = np.sqrt(
|
||||
1/(4*np.log(2)) * (np.log(df[high_col]/df[low_col])**2))
|
||||
|
||||
# Efficiency ratio
|
||||
df_features['price_efficiency'] = (
|
||||
abs(df[price_col] - df[price_col].shift(20)) /
|
||||
(df[high_col].rolling(20).max() - df[low_col].rolling(20).min())
|
||||
df_features['garman_klass_vol'] = np.sqrt(
|
||||
0.5 * np.log(df[high_col]/df[low_col])**2 -
|
||||
(2*np.log(2)-1) * np.log(df[price_col]/df['open'])**2
|
||||
)
|
||||
|
||||
# Deviation metrics
|
||||
df_features['deviation_from_vwap'] = (
|
||||
(df[price_col] - df[price_col].rolling(window=20).mean()) /
|
||||
df[price_col].rolling(window=20).mean()
|
||||
)
|
||||
|
||||
df_features['stock_return'] = df['close'].pct_change()
|
||||
# Dispersion measures
|
||||
df_features['price_range'] = df[high_col] - df[low_col]
|
||||
df_features['price_range_pct'] = df_features['price_range'] / df[price_col]
|
||||
|
||||
# Clean up any NaN values
|
||||
df_features = df_features.dropna()
|
||||
return df_features
|
||||
|
||||
return df_features
|
||||
Loading…
x
Reference in New Issue
Block a user