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import pandas as pd
from datetime import datetime
from typing import Dict, Any, List, Union
from pathlib import Path
from ..config import config
def format_number_to_2_decimals(value: Union[int, float, str, None]) -> Union[str, None]:
"""
Format numeric values to 2 decimal places.
Args:
value: The value to format
Returns:
Formatted string with 2 decimals, or original value if not numeric
"""
if value is None:
return None
# Keep string values like 'N/A' unchanged
if isinstance(value, str):
return value
# Format numeric values to 2 decimal places
if isinstance(value, (int, float)):
return f"{value:.2f}"
return value
def save_workflow_to_csv(workflow_result: Dict[str, Any], date: str) -> bool:
"""
Save workflow results to a single CSV file with each row representing a different date.
Args:
workflow_result: Complete workflow result containing all analysis data
date: Date string for the analysis
Returns:
bool: True if saved successfully, False otherwise
"""
try:
if not workflow_result.get('success', False):
print(f"No data to save to CSV.")
return False
# Extract results from workflow
results = workflow_result.get('results', {})
symbols = workflow_result.get('symbols', [])
if not symbols:
print(f"No symbols found in workflow result.")
return False
symbol = symbols[0] # We process one symbol at a time
# Get portfolio manager results - Access through symbol key
portfolio_results = results.get('portfolio_manager', {})
if not portfolio_results or symbol not in portfolio_results:
print(f"No portfolio manager results found for {symbol}.")
return False
symbol_portfolio_result = portfolio_results[symbol]
# Check if portfolio analysis was successful
if not symbol_portfolio_result.get('success', False):
print(f"Portfolio analysis failed for {symbol}: {symbol_portfolio_result.get('error', 'Unknown error')}")
return False
# Get data collection results for current price
data_results = results.get('data_collection', {})
market_data = data_results.get('market_data', {}) if data_results else {}
current_price = market_data.get('current_price', 'N/A')
# Get technical analysis results
tech_results = results.get('technical_analysis', {})
tech_indicators = {}
if tech_results and tech_results.get('success'):
indicators_data = tech_results.get('indicators', {})
tech_indicators = indicators_data.get('technical_indicators', {})
# Helper function to get last value from indicator lists
def get_last_value(data):
if isinstance(data, list) and data:
return data[-1]
elif isinstance(data, (int, float)):
return data
return None
# Extract individual technical indicators
sma = get_last_value(tech_indicators.get('SMA', []))
rsi = get_last_value(tech_indicators.get('RSI', []))
adx = get_last_value(tech_indicators.get('ADX', []))
cci = get_last_value(tech_indicators.get('CCI', []))
# MACD components
macd_data = tech_indicators.get('MACD', {})
macd_line = get_last_value(macd_data.get('macd', [])) if isinstance(macd_data, dict) else None
macd_signal = get_last_value(macd_data.get('signal', [])) if isinstance(macd_data, dict) else None
macd_histogram = get_last_value(macd_data.get('histogram', [])) if isinstance(macd_data, dict) else None
# Bollinger Bands components
bb_data = tech_indicators.get('BBANDS', {})
bb_upper = get_last_value(bb_data.get('upper', [])) if isinstance(bb_data, dict) else None
bb_middle = get_last_value(bb_data.get('middle', [])) if isinstance(bb_data, dict) else None
bb_lower = get_last_value(bb_data.get('lower', [])) if isinstance(bb_data, dict) else None
# Get news intelligence results for NLP features
news_results = results.get('news_intelligence', {})
nlp_features = {}
if news_results and news_results.get('success'):
nlp_features = news_results.get('nlp_features', {})
# Create comprehensive CSV data with all indicators and NLP features
# Apply 2-decimal formatting ONLY to technical/financial fields
csv_data = {
# Basic data
'date': date,
'symbol': symbol,
'close': format_number_to_2_decimals(current_price),
'trading_signal': symbol_portfolio_result.get('trading_signal', 'N/A'),
'confidence_level': symbol_portfolio_result.get('confidence_level', 'N/A'), # Keep original precision
'position_size': symbol_portfolio_result.get('position_size', 'N/A'), # Keep original precision
# Technical indicators - format to 2 decimals
'sma': format_number_to_2_decimals(sma),
'rsi': format_number_to_2_decimals(rsi),
'adx': format_number_to_2_decimals(adx),
'cci': format_number_to_2_decimals(cci),
# MACD components - format to 2 decimals
'macd_line': format_number_to_2_decimals(macd_line),
'macd_signal': format_number_to_2_decimals(macd_signal),
'macd_histogram': format_number_to_2_decimals(macd_histogram),
# Bollinger Bands components - format to 2 decimals
'bb_upper': format_number_to_2_decimals(bb_upper),
'bb_middle': format_number_to_2_decimals(bb_middle),
'bb_lower': format_number_to_2_decimals(bb_lower),
# NLP features from news intelligence - keep original precision
'news_relevance': nlp_features.get('news_relevance'),
'sentiment': nlp_features.get('sentiment'),
'price_impact_potential': nlp_features.get('price_impact_potential'),
'trend_direction': nlp_features.get('trend_direction'),
'earnings_impact': nlp_features.get('earnings_impact'),
'investor_confidence': nlp_features.get('investor_confidence'),
'risk_profile_change': nlp_features.get('risk_profile_change')
}
# Create CSV file path - SINGLE FILE for all dates
csv_path = config.csv_output_path
os.makedirs(csv_path, exist_ok=True)
csv_file = os.path.join(csv_path, 'daily_analysis.csv')
# Check if file exists and load existing data
file_exists = os.path.exists(csv_file)
if file_exists:
# Load existing data and append new row
try:
existing_df = pd.read_csv(csv_file)
new_df = pd.DataFrame([csv_data])
combined_df = pd.concat([existing_df, new_df], ignore_index=True)
# Remove duplicate entries for same date/symbol, keeping the latest
combined_df = combined_df.drop_duplicates(subset=['date', 'symbol'], keep='last')
# Sort by date descending (newest first)
combined_df = combined_df.sort_values(by='date', ascending=False)
except pd.errors.EmptyDataError:
# File exists but is empty
combined_df = pd.DataFrame([csv_data])
else:
# Create new file
combined_df = pd.DataFrame([csv_data])
# Save to CSV
combined_df.to_csv(csv_file, index=False)
print(f"CSV saved successfully: {csv_file}")
print(f"Total rows in CSV: {len(combined_df)}")
return True
except Exception as e:
print(f"Error saving CSV: {e}")
import traceback
traceback.print_exc()
return False
def save_workflow_to_symbol_csv(workflow_result: Dict[str, Any], date: str, data_dir: str = "./output/csv") -> bool:
"""
Save workflow results to a per-symbol CSV under data_dir with filename
pattern: daily_analysis_{SYMBOL}.csv.
The file will include at least: date, symbol, close, trading_signal,
confidence_level, position_size, and selected indicators and NLP features.
Args:
workflow_result: Workflow result dict
date: Analysis date (YYYY-MM-DD)
data_dir: Target directory for symbol CSV files (default ./data)
Returns:
bool: True if saved successfully
"""
try:
if not workflow_result.get('success', False):
return False
results = workflow_result.get('results', {})
symbols = workflow_result.get('symbols', [])
if not symbols:
return False
symbol = symbols[0].upper()
portfolio_results = results.get('portfolio_manager', {})
if not portfolio_results or symbol not in portfolio_results:
return False
symbol_portfolio_result = portfolio_results[symbol]
if not symbol_portfolio_result.get('success', False):
return False
data_results = results.get('data_collection', {})
market_data = data_results.get('market_data', {}) if data_results else {}
current_price = market_data.get('current_price', 'N/A')
tech_results = results.get('technical_analysis', {})
tech_indicators = {}
if tech_results and tech_results.get('success'):
indicators_data = tech_results.get('indicators', {})
tech_indicators = indicators_data.get('technical_indicators', {})
def get_last_value(data):
if isinstance(data, list) and data:
return data[-1]
elif isinstance(data, (int, float)):
return data
return None
sma = get_last_value(tech_indicators.get('SMA', []))
rsi = get_last_value(tech_indicators.get('RSI', []))
adx = get_last_value(tech_indicators.get('ADX', []))
cci = get_last_value(tech_indicators.get('CCI', []))
macd_data = tech_indicators.get('MACD', {})
macd_line = get_last_value(macd_data.get('macd', [])) if isinstance(macd_data, dict) else None
macd_signal = get_last_value(macd_data.get('signal', [])) if isinstance(macd_data, dict) else None
macd_histogram = get_last_value(macd_data.get('histogram', [])) if isinstance(macd_data, dict) else None
bb_data = tech_indicators.get('BBANDS', {})
bb_upper = get_last_value(bb_data.get('upper', [])) if isinstance(bb_data, dict) else None
bb_middle = get_last_value(bb_data.get('middle', [])) if isinstance(bb_data, dict) else None
bb_lower = get_last_value(bb_data.get('lower', [])) if isinstance(bb_data, dict) else None
news_results = results.get('news_intelligence', {})
nlp_features = news_results.get('nlp_features', {}) if news_results and news_results.get('success') else {}
csv_row = {
'date': date,
'symbol': symbol,
'close': format_number_to_2_decimals(current_price),
'trading_signal': symbol_portfolio_result.get('trading_signal', 'N/A'),
'confidence_level': symbol_portfolio_result.get('confidence_level', 'N/A'),
'position_size': symbol_portfolio_result.get('position_size', 'N/A'),
'sma': format_number_to_2_decimals(sma),
'rsi': format_number_to_2_decimals(rsi),
'adx': format_number_to_2_decimals(adx),
'cci': format_number_to_2_decimals(cci),
'macd_line': format_number_to_2_decimals(macd_line),
'macd_signal': format_number_to_2_decimals(macd_signal),
'macd_histogram': format_number_to_2_decimals(macd_histogram),
'bb_upper': format_number_to_2_decimals(bb_upper),
'bb_middle': format_number_to_2_decimals(bb_middle),
'bb_lower': format_number_to_2_decimals(bb_lower),
'news_relevance': nlp_features.get('news_relevance'),
'sentiment': nlp_features.get('sentiment'),
'price_impact_potential': nlp_features.get('price_impact_potential'),
'trend_direction': nlp_features.get('trend_direction'),
'earnings_impact': nlp_features.get('earnings_impact'),
'investor_confidence': nlp_features.get('investor_confidence'),
'risk_profile_change': nlp_features.get('risk_profile_change'),
}
# Ensure directory and file path
Path(data_dir).mkdir(parents=True, exist_ok=True)
symbol_csv = Path(data_dir) / f"daily_analysis_{symbol}.csv"
# Append/update row
if symbol_csv.exists():
try:
existing_df = pd.read_csv(symbol_csv)
new_df = pd.DataFrame([csv_row])
combined = pd.concat([existing_df, new_df], ignore_index=True)
combined = combined.drop_duplicates(subset=['date'], keep='last')
combined = combined.sort_values(by='date', ascending=True)
except pd.errors.EmptyDataError:
combined = pd.DataFrame([csv_row])
else:
combined = pd.DataFrame([csv_row])
combined.to_csv(symbol_csv, index=False)
print(f"Symbol CSV saved: {symbol_csv}")
return True
except Exception as e:
print(f"Error saving symbol CSV: {e}")
return False
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