-Quantitative-Analyst-Agent
🥈SilverThe Quantitative Analyst AI agent uses Python to conduct in-depth statistical analysis and financial modeling using real-time market data. Ideal for finance professionals seeking to enhance decision-making through data-driven insights.
610Updated Today
Advanced15 minutes to implementfinance
Saves ~180 min per use
Quick InstallView Source
claude install Fahadqureshi0/-Quantitative-Analyst-AgentWorks with:
ChatGPTGitHub Copilot
Overview
About This Skill
The Quantitative Analyst AI agent is a Python-based tool designed for financial professionals who need to perform statistical analysis and financial modeling using real-time market data. It enables data-driven decision-making by processing quantitative information to generate insights. This agent is particularly useful for those seeking to leverage computational analysis to improve financial outcomes and strategy development.
How to Use
[{"step":"Prepare your data inputs","action":"Export spend data from Coupa's Spend Analysis module (filter by date range and categories of interest). Export real-time market data from your financial data provider (e.g., Bloomberg, Refinitiv). Ensure both datasets are in CSV format with consistent date fields.","tips":["Use Coupa's 'Export to CSV' feature with columns: transaction_date, category, amount, supplier_id","For market data, include: date, sp500_index, wti_crude_price, usd_index","Validate data completeness by checking for missing dates or values"]},{"step":"Customize the analysis task","action":"Replace [TASK] in the prompt with your specific financial analysis goal. Examples: 'Identify top 5 spend categories with highest volatility in Q3 2024', 'Model the impact of a 10% increase in energy prices on total spend', or 'Forecast Q4 2024 spend with 90% confidence intervals'.","tips":["Be specific about time periods and categories to focus analysis","Include any constraints (e.g., 'exclude capital expenditures')","Specify desired output format (e.g., 'include Python code for reproducibility')"]},{"step":"Execute the analysis","action":"Copy the customized prompt into your AI assistant (Claude/ChatGPT). Ensure your Python environment has the required libraries installed (pandas, numpy, statsmodels, matplotlib). Upload your data files when prompted.","tips":["For large datasets (>100K rows), pre-aggregate data in Coupa to improve performance","Use Coupa's 'Spend Cube' feature to generate pre-filtered datasets","Consider running the Python code in Jupyter Notebook for interactive exploration"]},{"step":"Validate and refine results","action":"Review the AI-generated report for statistical validity. Check R² values, p-values, and confidence intervals. Adjust parameters if results don't meet expectations (e.g., change ARIMA order or regression model).","tips":["Compare AI findings with historical benchmarks from Coupa's spend analytics","Consult Coupa's 'Spend Variance Analysis' reports for validation","Iterate with different time periods or categories to identify patterns"]},{"step":"Implement recommendations","action":"Use the actionable insights to inform procurement decisions. In Coupa, create procurement strategies based on the analysis (e.g., set up hedging contracts, renegotiate supplier terms). Monitor progress using Coupa's spend tracking and reporting tools.","tips":["Set up automated alerts in Coupa for spend thresholds identified in the analysis","Schedule quarterly reviews of financial risk metrics using Coupa's dashboard","Document assumptions and methodology for future reference"]}]
Use Cases
Performing risk assessment for investment portfolios
Developing predictive models for stock prices
Analyzing market trends using historical data
Creating financial reports and dashboards
Best For
GrowthRevOps
Tags
Setup & Installation
Quick Install
Terminal
claude install Fahadqureshi0/-Quantitative-Analyst-AgentAlternative Install (Git Clone)
git clone https://github.com/Fahadqureshi0/-Quantitative-Analyst-AgentRequirements
- Claude Code or compatible AI agent
- Works with: ChatGPT, GitHub Copilot
- Advanced skill - requires experience with AI agents
Quick Start Guide
1
Install the Skill
Copy the install command above and run it in your terminal.
2
Open Your AI Agent
Launch Claude Code, Cursor, or your preferred AI coding agent.
3
Try It Out
Use the prompt template or examples below to test the skill.
4
Customize
Adapt the skill to your specific use case and workflow.
Usage Examples
Prompt Template
Act as a Quantitative Analyst Agent. Using Python and real-time market data from Coupa's spend analysis and reporting tools, perform the following: [TASK]. Ensure your analysis includes statistical validation, financial modeling, and actionable recommendations. Use Coupa's [SPEND_DATASET] and [MARKET_DATA] as inputs. Format the output as a structured report with sections for methodology, key findings, and recommendations. Include Python code snippets for reproducibility.
Example Output
{"structured_report":{"title":"Q3 2024 Financial Risk Assessment: Spend Volatility & Market Correlation Analysis","methodology":{"data_sources":["Coupa Spend Analysis Dataset (Q3 2024): $12.4M total spend across 1,247 transactions","Real-time market data (Bloomberg API): S&P 500, USD Index, WTI Crude Oil futures","Python libraries: pandas (2.1.4), numpy (1.26.3), statsmodels (0.14.0), matplotlib (3.8.2)"],"analysis_approach":"Applied multivariate regression to model spend volatility against market indices. Used ARIMA(2,1,1) for time-series forecasting of quarterly spend trends. Conducted Monte Carlo simulation (10,000 iterations) to estimate 95% confidence intervals for spend projections.","validation":"Cross-validated model using 20% holdout sample. Achieved R² = 0.87 for training set and 0.82 for test set. All coefficients statistically significant at p<0.01."},"key_findings":{"spend_volatility":{"description":"Total spend volatility increased by 18.7% QoQ, driven by energy and raw material costs.","metrics":{"volatility_index":0.234,"95%_confidence_interval":["$11.8M","$13.1M"],"correlation_with_S&P500":0.71,"correlation_with_WTI_Crude":0.89}},"category_breakdown":{"high_risk_categories":[{"category":"Indirect Materials","spend":"$3.2M (25.8% of total)","volatility":0.34,"recommendation":"Implement forward contracts for 60% of volume to lock in prices"},{"category":"Professional Services","spend":"$2.8M (22.6% of total)","volatility":0.28,"recommendation":"Negotiate fixed-fee contracts with performance incentives"}],"low_risk_categories":[{"category":"Technology Software","spend":"$1.9M (15.3% of total)","volatility":0.09,"recommendation":"Maintain current procurement strategy"}]},"market_exposure":{"description":"89% of spend volatility attributable to energy and commodity prices.","hedging_opportunities":["WTI Crude Oil futures: 3-month contracts at $78.20/bbl (current spot: $79.50)","Natural Gas futures: 6-month contracts at $2.85/MMBtu (current spot: $3.10)"]}},"recommendations":{"short_term":["Execute hedging transactions for 40% of energy-related spend within 2 weeks","Initiate renegotiation of professional services contracts with 15% cost reduction target","Implement daily spend monitoring dashboard in Coupa Compose"],"long_term":["Develop category-specific hedging strategies aligned with market cycles","Establish quarterly financial risk review meetings with procurement teams","Integrate Coupa spend data with ERP systems for real-time financial modeling"]}},"python_code":{"spend_analysis":"import pandas as pd\nfrom statsmodels.tsa.arima.model import ARIMA\nfrom statsmodels.regression.linear_model import OLS\nimport numpy as np\n\n# Load Coupa spend data\nspend_data = pd.read_csv('coupa_spend_q3_2024.csv', parse_dates=['transaction_date'])\n\n# Load market data\nmarket_data = pd.read_csv('market_data_q3_2024.csv', parse_dates=['date'])\n\n# Merge datasets\ncombined_data = pd.merge(spend_data, market_data, left_on='transaction_date', right_on='date')\n\n# Calculate volatility index\ncombined_data['daily_spend'] = combined_data.groupby('transaction_date')['amount'].transform('sum')\nvolatility = combined_data['daily_spend'].pct_change().rolling(30).std() * np.sqrt(252)\n\n# ARIMA modeling\nmodel = ARIMA(combined_data['daily_spend'], order=(2,1,1))\nresults = model.fit()\n\n# Monte Carlo simulation\nsimulations = 10000\nlast_observed = combined_data['daily_spend'].iloc[-1]\nforecast = results.forecast(steps=63) # 90-day forecast\nsimulated = np.random.normal(forecast.mean(), forecast.std(), simulations)\nci_lower, ci_upper = np.percentile(simulated, [2.5, 97.5])","market_correlation":"from scipy.stats import pearsonr\n\n# Calculate correlations\nspend_returns = combined_data['daily_spend'].pct_change().dropna()\nsp500_returns = combined_data['sp500_return'].dropna()\nwti_returns = combined_data['wti_return'].dropna()\n\nsp500_corr, _ = pearsonr(spend_returns, sp500_returns)\nwti_corr, _ = pearsonr(spend_returns, wti_returns)\n\nprint(f\"S&P 500 Correlation: {sp500_corr:.2f}\")\nprint(f\"WTI Crude Correlation: {wti_corr:.2f}\")"}}Apply to these tools
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