key stringlengths 10 111 | value stringlengths 536 23.8k |
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Analyze BTC kline data and provide statistics | #!/usr/bin/env python3
# Generator: Investment and Research Assistant
# Description: Analyze BTC kline data and provide statistics
# Arguments:
# - input_csv: path to the input CSV file with kline data
# - output_csv: optional path to save the analysis results as CSV
import pandas as pd
import numpy as np
import argpa... |
Analyze feature importance and correlations for daily features | #!/usr/bin/env python3
# Generator: Claude
# Description: Analyze feature importance and correlations for daily features
# Arguments:
# - features_file: Path to the daily features CSV file
# - output_dir: Output directory for results (default: current directory)
import pandas as pd
import numpy as np
import matplotlib... |
Analyze the generated feature matrix and provide summary statistics | #!/usr/bin/env python3
# Generator: Claude
# Description: Analyze the generated feature matrix and provide summary statistics
# Arguments:
# - input_file: Path to the input feature matrix CSV file
import pandas as pd
import numpy as np
import sys
import os
import argparse
def main():
# Parse command line argument... |
Analyze large transactions in more detail | #!/usr/bin/env python3
# Generator: Investment and Research Assistant
# Description: Analyze large transactions in more detail
# Arguments:
# - csv_path: path to the CSV file containing large transactions data
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
import argparse
from datetim... |
Backtest BTC trading strategy using RandomForest predictions | #!/usr/bin/env python3
# Generator: Claude
# Description: Backtest BTC trading strategy using RandomForest predictions
# Arguments:
# - features_file: Path to the features CSV file (default: btc_features.csv)
# - model_file: Path to the trained model file (default: btc_model.pkl)
# - output_file: Path to save the backt... |
Backtest BTC trading strategy using RandomForest predictions (fixed version) | #!/usr/bin/env python3
# Generator: Claude
# Description: Backtest BTC trading strategy using RandomForest predictions (fixed version)
# Arguments:
# - features_file: Path to the features CSV file (default: btc_features.csv)
# - model_file: Path to the trained model file (default: btc_model.pkl)
# - output_file: Path t... |
Very simplified backtest for BTC trading strategy | #!/usr/bin/env python3
# Generator: Claude
# Description: Very simplified backtest for BTC trading strategy
# Arguments: None
import pandas as pd
import numpy as np
import pickle
import os
import sys
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
def load_data(features_file, model_file):
"""Loa... |
Simplified backtest for BTC trading strategy | #!/usr/bin/env python3
# Generator: Claude
# Description: Simplified backtest for BTC trading strategy
# Arguments: None
import pandas as pd
import numpy as np
import pickle
import os
import sys
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
def load_data(features_file, model_file):
"""Load fea... |
Simplified backtest for BTC trading strategy (fixed version) | #!/usr/bin/env python3
# Generator: Claude
# Description: Simplified backtest for BTC trading strategy (fixed version)
# Arguments: None
import pandas as pd
import numpy as np
import pickle
import os
import sys
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
def load_data(features_file, model_file):... |
Creates a basic grayscale version of an image using Python's standard library | #!/usr/bin/env python3
# Generator: Investment and Research Expert
# Description: Creates a basic grayscale version of an image using Python's standard library
# Arguments:
# - input_path: Path to the input image file
# - output_path: Path where the grayscale image will be saved
import sys
import os
import base64
def... |
Calculate and visualize Bollinger Bands for 1-minute BTC/USDT data | #!/usr/bin/env python3
# Generator: Claude
# Description: Calculate and visualize Bollinger Bands for 1-minute BTC/USDT data
# Arguments:
# - db_file: Path to the SQLite database file
# - start_date: Start date in YYYY-MM-DD format (optional)
# - end_date: End date in YYYY-MM-DD format (optional)
# - period: Bollinger ... |
Backtest BTC/USDT CTA strategy using a trained RandomForest model | #!/usr/bin/env python3
# Generator: Claude
# Description: Backtest BTC/USDT CTA strategy using a trained RandomForest model
# Arguments:
# - db_file: Path to the SQLite database file
# - model_file: Path to the trained model file
# - sample_size: Number of rows to sample from the dataset (default: 5000)
# - buy_thresho... |
Train a direct RandomForest model for BTC/USDT CTA strategy using data from SQLite | #!/usr/bin/env python3
# Generator: Claude
# Description: Train a direct RandomForest model for BTC/USDT CTA strategy using data from SQLite
# Arguments:
# - db_file: Path to the SQLite database file
# - sample_size: Number of rows to sample from the dataset (default: 5000)
# - lookback_window: Lookback window size in ... |
Generate price and volume features for BTC/USDT CTA strategy using feature_toolkit | #!/usr/bin/env python3
# Generator: Claude
# Description: Generate price and volume features for BTC/USDT CTA strategy using feature_toolkit
# Arguments:
# - db_file: Path to the SQLite database file containing BTC/USDT klines
# - output_file: Path to save the generated features CSV file
# - analyze: Optional flag to a... |
Generate price and volume features for BTC/USDT CTA strategy (fixed version) | #!/usr/bin/env python3
# Generator: Claude
# Description: Generate price and volume features for BTC/USDT CTA strategy (fixed version)
# Arguments:
# - db_file: Path to the SQLite database file containing BTC/USDT klines
# - output_file: Path to save the generated features CSV file
# - analyze: Optional flag to analyze... |
Hyperparameter optimization for BTC price prediction models using Optuna (Final version) | #!/usr/bin/env python3
# Generator: Claude
# Description: Hyperparameter optimization for BTC price prediction models using Optuna (Final version)
# Arguments:
# - db_file: Path to the SQLite database file
# - model_type: Model type (xgboost or randomforest, default: xgboost)
# - train_ratio: Training data ratio (defau... |
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