Portfolio Analysis suite – encompassing Performance, Risk, Attribution, and Exposure – is monumental. This isn’t just about code; it’s about deep financial domain expertise, massive data processing capabilities. we can design a conceptual codebase structure that could underpin such a system, breaking down the complexities into logical components based on modern software architecture principles.
/factset-portfolio-analytics-suite
├── /data-ingestion-normalization # Layer for getting portfolio and market data ready
│ ├── /portfolio-data-ingest-service # Ingests client holdings, transactions, and cash flows
│ │ ├── /src
│ │ │ ├── /connectors # Adapters for various input formats (e.g., CSV, FIX, custom APIs)
│ │ │ ├── /validators # Data quality checks (e.g., missing prices, mismatched securities)
│ │ │ └── /transformers # Normalizes client data to internal FactSet schema
│ │ ├── /tests
│ │ └── /config # Schema definitions, mapping rules
│ │
│ ├── /market-data-aggregator-service # Collects and normalizes market data (prices, rates, indices)
│ │ ├── /src
│ │ │ ├── /exchanges # Connectors for various exchange feeds (real-time, EOD)
│ │ │ ├── /benchmarks # Processes and manages benchmark data (MSCI, S&P, custom)
│ │ │ └── /fx-rates # Manages currency exchange rates, historical conversions
│ │ ├── /tests
│ │ └── /config
│ │
│ └── /symbology-service # Core service for resolving and linking identifiers
│ ├── /src
│ │ ├── /cross-referencing # Algorithms for linking CUSIPs, ISINs, tickers, LEIs
│ │ ├── /master-data # Management of FactSet's proprietary entity/security master
│ │ └── /data-enrichment # Adding fundamental, industry, and reference data
│ ├── /tests
│ └── /graphql-api # FactSet uses GraphQL for symbology
│
├── /core-analytics-engines # High-performance computational core
│ ├── /performance-engine # Calculates absolute and relative portfolio performance
│ │ ├── /src
│ │ │ ├── /return-calculation # Time-weighted, money-weighted returns
│ │ │ ├── /time-series-agg # Aggregates daily/monthly data to various periods
│ │ │ └── /benchmark-comparison # Handles complex benchmark comparisons
│ │ ├── /tests
│ │ ├── /api # Internal API for engine execution
│ │ └── /docs # Methodology documentation (e.g., GIPS compliance)
│ │
│ ├── /risk-engine # Computes various risk metrics and models
│ │ ├── /src
│ │ │ ├── /factor-models # Implementation of various factor models (e.g., fundamental, statistical, economic)
│ │ │ ├── /monte-carlo-sim # Full revaluation for derivatives, stress testing
│ │ │ ├── /va-r-calculation # Value-at-Risk, Conditional VaR
│ │ │ ├── /stress-testing # Scenario analysis framework
│ │ │ └── /optimization # Portfolio optimization algorithms
│ │ ├── /tests
│ │ ├── /api
│ │ └── /models # Configuration/data for specific risk models (Cognity, Global Equity)
│ │
│ ├── /attribution-engine # Decomposes performance into contributing factors
│ │ ├── /src
│ │ │ ├── /equity-attribution # Brinson-Fachler, custom models (e.g., industry, country, security selection)
│ │ │ ├── /fixed-income-attribution # Duration, yield curve, spread attribution
│ │ │ ├── /multi-asset-attribution # Handles complex, blended portfolios
│ │ │ └── /methodologies # Implementation of 10+ attribution models FactSet offers
│ │ ├── /tests
│ │ ├── /api
│ │ └── /docs # Detailed methodology papers
│ │
│ └── /exposure-engine # Analyzes portfolio exposures (sectors, countries, factors, themes)
│ ├── /src
│ │ ├── /classification-mapping # Mapping securities to FactSet's proprietary classifications (e.g., RBICS, GeoRev, ESG Themes)
│ │ ├── /geographic-exposure # Algorithms for geographic revenue/supply chain analysis
│ │ ├── /thematic-exposure # ESG, AI, other thematic exposures
│ │ └── /factor-exposure # Calculating portfolio's sensitivity to various risk factors
│ ├── /tests
│ └── /api
│
├── /analytics-orchestration-layer # Manages workflows and API interactions
│ ├── /pa-engine-api-service # External RESTful API for Portfolio Analysis (e.g., `analytics/engines/pa/v3`)
│ │ ├── /src
│ │ │ ├── /controllers # Handles API requests, authentication, validation
│ │ │ ├── /workflow-manager # Orchestrates calls to underlying analytics engines
│ │ │ └── /result-formatter # Formats analytical results for client consumption (JSON, CSV, PDF)
│ │ ├── /tests
│ │ ├── /openapi-spec # OpenAPI/Swagger definition
│ │ └── /sdk-generation # Tools to auto-generate client SDKs (Python, Java, .NET)
│ │
│ ├── /template-management-service # Stores and manages user-defined and pre-built templates
│ │ ├── /src
│ │ │ └── /template-parser # Parses and validates template configurations
│ │ ├── /api
│ │ └── /db-schema # Storage for template definitions
│ │
│ └── /report-generation-service # Creates output reports (PDF, Excel, web dashboards)
│ ├── /src
│ │ ├── /template-renderer # Renders data into structured report layouts
│ │ ├── /charting-library # Integration with charting/visualization tools
│ │ └── /export-formats # Handles different output formats
│ ├── /api
│ └── /templates # Report template definitions
│
├── /data-storage # Databases and Data Lake for analytical data
│ ├── /portfolio-holdings-db # High-performance database for current/historical holdings
│ │ ├── /schema
│ │ └── /migration-scripts
│ ├── /market-data-warehouse # Time-series data store for prices, returns, indices
│ │ ├── /schema
│ │ └── /data-partitions # Strategy for partitioning time-series data
│ ├── /analytics-results-cache # Fast cache for frequently accessed analytics results
│ ├── /data-lake-s3-config # Configuration for raw and processed data in S3
│ └── /data-warehouse-ddl # DDL for enterprise data warehouse (Snowflake/Redshift)
│
├── /ai-ml-models # AI/ML specific models and pipelines
│ ├── /nlp-for-commentary # LLM fine-tuning/inference for automated report commentary
│ │ ├── /src
│ │ └── /models
│ ├── /data-extraction-ml # Models for extracting data from unstructured sources (e.g., PDFs)
│ │ ├── /src
│ │ └── /models
│ ├── /sentiment-analysis-models # For news/transcript analysis
│ │ ├── /src
│ │ └── /models
│ └── /mlops-pipelines # CI/CD for ML models, monitoring, retraining
│
├── /shared-components # Reusable libraries and utilities
│ ├── /financial-math-lib # Core financial calculation functions (e.g., present value, duration, convexity)
│ ├── /security-master-client # Client for interacting with the Symbology Service
│ ├── /logging-metrics # Standardized logging and metrics collection
│ ├── /error-handling # Common error handling patterns
│ └── /data-structures # Optimized data structures for financial data
│
├── /infrastructure-as-code # Cloud and on-prem infrastructure definitions
│ ├── /aws-terraform # Terraform modules for AWS resources
│ ├── /kubernetes-configs # K8s manifests for microservices deployment
│ ├── /network-configs # VPC, firewall, load balancer setups
│ └── /monitoring-configs # Prometheus/Grafana/CloudWatch definitions
│
├── /ui-components # Frontend elements for Workstation/Web apps
│ ├── /portfolio-dashboard-widgets # Reusable UI widgets for dashboards
│ ├── /charting-library-wrappers # Wrappers for Highcharts/Plotly etc.
│ └── /report-viewer # Component for displaying generated reports
│
└── /docs # Documentation for developers and quants
├── /api-reference
├── /methodology-guides
├── /architecture-overviews
└── /dev-onboardingConceptual Code Snippets for /performance-engine/src (Simplified Python)
return_calculation.py
import pandas as pd
import numpy as np
def calculate_time_weighted_return(
portfolio_values: pd.Series, # Daily end-of-period market values
cash_flows: pd.Series # Daily cash flows (positive for inflow, negative for outflow)
) -> float:
"""
Calculates the Time-Weighted Rate of Return (TWR).
TWR removes the effects of external cash flows from the investment performance.
It's suitable for evaluating the performance of a portfolio manager.
Args:
portfolio_values (pd.Series): A Series of end-of-period portfolio market values, indexed by date.
Assumes initial value at start_date, subsequent values are EOD.
cash_flows (pd.Series): A Series of daily cash flows, indexed by date.
Positive for contributions, negative for withdrawals.
Returns:
float: The Time-Weighted Rate of Return for the period.
"""
# Combine values and cash flows, align by date
all_dates = sorted(list(set(portfolio_values.index) | set(cash_flows.index)))
start_date = all_dates[0]
end_date = all_dates[-1]
# Ensure all required dates are present in portfolio_values (forward fill if needed for robustness)
portfolio_values = portfolio_values.reindex(all_dates).ffill().bfill() # bfill for start date if missing
# Initialize sub-period returns
sub_period_returns = []
# Iterate through periods defined by cash flows or portfolio value dates
current_portfolio_value = portfolio_values.loc[start_date]
if pd.isna(current_portfolio_value):
raise ValueError("Initial portfolio value not available at start_date.")
for i in range(len(all_dates) - 1):
current_date = all_dates[i]
next_date = all_dates[i+1]
# Value at beginning of sub-period (after previous cash flow if any)
BOP_value = current_portfolio_value
# End of sub-period value
EOP_value = portfolio_values.loc[next_date]
# Cash flow occurring at the end of the current sub-period (beginning of next)
cf_at_next_date = cash_flows.get(next_date, 0.0)
# Calculate return for the sub-period
if BOP_value != 0: # Avoid division by zero
sub_return = (EOP_value - cf_at_next_date - BOP_value) / BOP_value
sub_period_returns.append(1 + sub_return)
else:
# If BOP_value is zero and EOP is also zero, return is 0
# If BOP_value is zero and EOP is not zero, this is an initial investment, handle with care.
# For simplicity, if BOP is 0, we assume this is the point of first investment
# and the first return is calculated as (EOP - CF)/BOP, which would be infinite.
# A more robust solution would handle initial investments differently.
if EOP_value == 0 and cf_at_next_date == 0:
sub_period_returns.append(1.0)
else:
# This scenario needs careful domain-specific handling, e.g., if a portfolio goes to zero and then has an inflow.
# For now, let's assume valid data where BOP_value isn't zero unless it's truly the initial investment.
# If BOP is 0, and EOP isn't 0 (after cash flow), the return is effectively infinite from 0.
# A more practical approach for a real system:
# If BOP_value is 0 and EOP_value is also 0, return is 0.
# If BOP_value is 0 and EOP_value is non-zero (implies new investment),
# the first period's return usually isn't calculated this way.
# For the purpose of this illustration, we'll simplify.
# A real system would break into individual transactions or handle
# initial investments as a special case.
pass # Will handle below by updating current_portfolio_value
# Update current portfolio value for next sub-period
current_portfolio_value = EOP_value - cf_at_next_date
# Compound the sub-period returns
if not sub_period_returns:
return 0.0 # No periods to calculate
compounded_return = np.prod(sub_period_returns) - 1
return compounded_return
def calculate_money_weighted_return(
initial_investment: float,
cash_flows: pd.Series, # Daily cash flows, indexed by date (positive inflow, negative outflow)
final_value: float # Market value at the end of the period
) -> float:
"""
Calculates the Money-Weighted Rate of Return (MWRR), also known as IRR.
MWRR considers the size and timing of cash flows, reflecting the investor's perspective.
This is an approximation using numerical methods (Newton-Raphson or similar)
as there's no direct analytical solution for IRR.
Args:
initial_investment (float): The initial investment value.
cash_flows (pd.Series): A Series of daily cash flows, indexed by date.
Positive for contributions, negative for withdrawals.
final_value (float): The market value of the portfolio at the end of the period.
Returns:
float: The Money-Weighted Rate of Return (IRR) for the period.
"""
# For a simplified example, we'll use a basic numerical solver.
# In practice, one would use a robust financial library's IRR function.
# Combine all cash flows including initial investment and final value
# Dates must be normalized to a consistent basis (e.g., days from start)
cash_flow_list = [initial_investment] # Initial investment as outflow
dates = [pd.to_datetime(cash_flows.index.min()) - pd.Timedelta(days=1)] # Date before first flow
for date, flow in cash_flows.items():
cash_flow_list.append(-flow) # Inflows are negative cash flow from company perspective
dates.append(pd.to_datetime(date))
cash_flow_list.append(final_value) # Final value as inflow
dates.append(pd.to_datetime(cash_flows.index.max()))
# Calculate days from start for each flow
time_points = [(d - dates[0]).days for d in dates]
# Define the NPV function for IRR calculation
def npv_function(rate, cash_flows_list, time_points_list):
npv = 0
for i, cf in enumerate(cash_flows_list):
# Convert rate to daily rate if time_points are in days
# Assuming rate is annual, convert to daily compound rate
daily_rate = (1 + rate)**(1/365) - 1 if rate != -1 else -1 # Handle -1 for np.inf issues
if daily_rate == -1: # Prevent division by zero for (1+rate_daily)
npv += cf / (1e-9) # Approaching negative infinity
else:
npv += cf / ((1 + daily_rate) ** time_points_list[i])
return npv
# Use a numerical solver (e.g., SciPy's fsolve or simple bisection)
# This is a very simple bisection for illustration; a real one is more robust.
# We are looking for the rate that makes NPV = 0
from scipy.optimize import fsolve
try:
# Initial guess for annual rate, can be refined
initial_guess = 0.10
# Solve for the annual rate
irr_annual = fsolve(npv_function, initial_guess, args=(cash_flow_list, time_points))[0]
return irr_annual
except Exception as e:
print(f"Error calculating MWRR (IRR): {e}")
return np.nan # Or raise a specific error
# Example Usage:
if __name__ == "__main__":
# Sample Data (Daily)
portfolio_values_data = {
'2023-01-01': 100000,
'2023-01-31': 105000,
'2023-02-15': 103000,
'2023-02-28': 108000,
'2023-03-31': 112000
}
portfolio_values = pd.Series(portfolio_values_data).sort_index()
cash_flows_data = {
'2023-02-01': 5000, # Inflow
'2023-03-01': -2000 # Outflow
}
cash_flows = pd.Series(cash_flows_data).sort_index()
# TWR Calculation
twr = calculate_time_weighted_return(portfolio_values, cash_flows)
print(f"Time-Weighted Return (TWR): {twr:.4f} ({twr*100:.2f}%)")
# MWRR Calculation
# Need to adjust cash flows for MWRR relative to initial_investment
# MWRR requires cash flows as they occur from the investor's perspective (positive for inflow, negative for outflow)
# The `cash_flows_data` above is from the *portfolio's* perspective (positive increase value, negative decrease value).
# For MWRR (IRR), cash flows into the portfolio are usually *negative* (e.g., you pay 100 to invest), outflows are *positive*.
# Let's adjust for the MWRR example
initial_invest = 100000
mwrr_cash_flows = pd.Series({
'2023-02-01': 5000, # Investor contributes 5000 (negative cash flow from portfolio perspective)
'2023-03-01': -2000 # Investor withdraws 2000 (positive cash flow from portfolio perspective)
})
final_val = 112000
mwrr = calculate_money_weighted_return(initial_invest, mwrr_cash_flows, final_val)
print(f"Money-Weighted Return (MWRR/IRR): {mwrr:.4f} ({mwrr*100:.2f}%)")time series aggregation, benchmark comparison, api (Internal API for Engine Execution), docs.
Additionally, FactSet operates in a demanding environment that requires:
Cost Optimization: Kubernetes can help optimize resource utilization by packing containers efficiently and scaling them up or down based on demand, preventing overprovisioning.
Massive Scale and Performance: Handling real-time market data from hundreds of exchanges, processing vast amounts of historical data, and serving thousands of concurrent users requires an infrastructure that can scale rapidly and efficiently.
High Availability and Reliability: Downtime is extremely costly in financial services. Kubernetes’ self-healing capabilities are vital for maintaining continuous service.
Agility and Faster Time-to-Market: To innovate quickly and deliver new features to clients, FactSet needs to deploy and update services rapidly and frequently.
Microservices Architecture: As discussed, FactSet is moving towards or has largely adopted a microservices architecture. Kubernetes is the leading platform for orchestrating containerized microservices.
Hybrid Cloud Strategy: FactSet has a mix of its own data centers and public cloud (primarily AWS). Kubernetes provides a consistent deployment and management layer across these diverse environments.
Naixian Zhang 