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pyfolio/mcp_output/README_MCP.md

@@ -0,0 +1,202 @@
+# MCP Plugin: PyFolio
+
+## Overview
+
+PyFolio is a Python library designed for performance and risk analysis of financial portfolios. Developed by Quantopian Inc., it provides tools to evaluate trading strategies through visualizations and statistical metrics. PyFolio generates "tear sheets," which are comprehensive reports containing visualizations and metrics that help assess various aspects of portfolio performance.
+
+The library integrates seamlessly with the Zipline backtesting library but can also be used independently with any source of portfolio data. PyFolio is modular, allowing users to focus on specific aspects of portfolio analysis or generate comprehensive reports.
+
+## Features
+
+- **Tear Sheet Generation**: Create detailed reports for portfolio performance analysis.
+- **Visualization Tools**: Generate plots for returns, drawdowns, exposures, and more.
+- **Performance Attribution**: Decompose portfolio returns into factor components.
+- **Round Trip Analysis**: Evaluate completed trades and their profitability.
+- **Capacity Analysis**: Assess the scalability of trading strategies.
+- **Integration**: Works with Zipline, Empyrical, and custom data sources.
+
+## Installation
+
+To install PyFolio, ensure you have Python 3.6 or later installed. You can install the library using pip:
+
+```bash
+pip install pyfolio
+```
+
+Alternatively, you can clone the repository and install it manually:
+
+```bash
+git clone https://github.com/quantopian/pyfolio.git
+cd pyfolio
+pip install .
+```
+
+## Dependencies
+
+PyFolio requires the following Python libraries:
+
+- **Required**:
+  - `numpy`
+  - `pandas`
+  - `matplotlib`
+  - `seaborn`
+  - `scipy`
+  - `statsmodels`
+- **Optional**:
+  - `jupyter` (for interactive notebooks)
+  - `pytest` (for testing)
+
+Ensure these dependencies are installed before using PyFolio.
+
+## Usage
+
+PyFolio provides several methods for generating tear sheets and visualizations. Below are examples of common usage scenarios:
+
+### Basic Tear Sheet Generation
+
+To generate a full tear sheet for portfolio analysis:
+
+```python
+import pyfolio as pf
+
+# Example: Generate a tear sheet using returns data
+pf.create_full_tear_sheet(returns)
+```
+
+### Specialized Tear Sheets
+
+PyFolio allows you to generate specific tear sheets for focused analysis:
+
+- **Returns Analysis**:
+  ```python
+  pf.create_returns_tear_sheet(returns)
+  ```
+- **Position Analysis**:
+  ```python
+  pf.create_position_tear_sheet(positions)
+  ```
+- **Transaction Analysis**:
+  ```python
+  pf.create_txn_tear_sheet(transactions)
+  ```
+- **Round Trip Analysis**:
+  ```python
+  pf.create_round_trip_tear_sheet(returns, positions, transactions)
+  ```
+- **Capacity Analysis**:
+  ```python
+  pf.create_capacity_tear_sheet(returns, positions)
+  ```
+- **Performance Attribution**:
+  ```python
+  pf.create_perf_attrib_tear_sheet(returns, factor_returns)
+  ```
+
+### Integration with Zipline
+
+PyFolio integrates seamlessly with Zipline. You can extract portfolio data from Zipline and use it with PyFolio:
+
+```python
+from pyfolio.utils import extract_rets_pos_txn_from_zipline
+
+returns, positions, transactions = extract_rets_pos_txn_from_zipline(zipline_results)
+pf.create_full_tear_sheet(returns, positions=positions, transactions=transactions)
+```
+
+### Visualization Functions
+
+PyFolio includes standalone plotting functions for specific visualizations:
+
+- Plot returns:
+  ```python
+  pf.plot_returns(returns)
+  ```
+- Plot rolling returns:
+  ```python
+  pf.plot_rolling_returns(returns, factor_returns)
+  ```
+- Plot drawdown periods:
+  ```python
+  pf.plot_drawdown_periods(returns)
+  ```
+- Plot monthly returns heatmap:
+  ```python
+  pf.plot_monthly_returns_heatmap(returns)
+  ```
+
+## Available Endpoints
+
+### Tear Sheet Functions
+
+- `create_full_tear_sheet`
+- `create_simple_tear_sheet`
+- `create_returns_tear_sheet`
+- `create_position_tear_sheet`
+- `create_txn_tear_sheet`
+- `create_round_trip_tear_sheet`
+- `create_interesting_times_tear_sheet`
+- `create_capacity_tear_sheet`
+- `create_perf_attrib_tear_sheet`
+
+### Visualization Functions
+
+- `plot_returns`
+- `plot_rolling_returns`
+- `plot_drawdown_periods`
+- `plot_monthly_returns_heatmap`
+- `plot_holdings`
+- `plot_position_exposures`
+- `plot_turnover`
+
+### Utility Functions
+
+- `extract_rets_pos_txn_from_zipline`
+- `get_clean_factor_and_forward_returns`
+
+## Notes and Troubleshooting
+
+### Common Issues
+
+1. **Missing Dependencies**:
+   Ensure all required libraries are installed. Use `pip install` to install missing dependencies.
+
+2. **Data Format Errors**:
+   PyFolio expects input data in specific formats (e.g., pandas DataFrames). Ensure your data conforms to these requirements.
+
+3. **Visualization Errors**:
+   If plots fail to render, check your matplotlib and seaborn installations. Update them to the latest versions if necessary.
+
+4. **Integration with Zipline**:
+   Ensure Zipline results are correctly formatted before extracting data using `extract_rets_pos_txn_from_zipline`.
+
+### Debugging Tips
+
+- Use PyFolio's built-in logging to identify issues:
+  ```python
+  import logging
+  logging.basicConfig(level=logging.DEBUG)
+  ```
+- Validate input data using pandas functions (e.g., `df.info()` or `df.head()`).
+
+### Reporting Issues
+
+If you encounter bugs or have feature requests, please open an issue on the PyFolio GitHub repository:  
+[https://github.com/quantopian/pyfolio/issues](https://github.com/quantopian/pyfolio/issues)
+
+## Development and Contributions
+
+PyFolio is an open-source project. Contributions are welcome! To contribute:
+
+1. Fork the repository.
+2. Create a new branch for your feature or bug fix.
+3. Submit a pull request with a detailed description of your changes.
+
+For development guidelines, refer to the `CONTRIBUTING.md` file in the repository.
+
+## License
+
+PyFolio is licensed under the Apache License 2.0. See the `LICENSE` file for details.
+
+---
+
+For more information, visit the [PyFolio GitHub repository](https://github.com/quantopian/pyfolio).

pyfolio/mcp_output/analysis.json

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+    "content": "quantopian/pyfolio\nCore Architecture\nTear Sheets\nPerformance and Risk Analysis\nPlotting Functions\nUtility Functions\nSpecialized Analysis Modules\nPosition Analysis\nTransaction Analysis\nRound Trip and Capacity Analysis\nPerformance Attribution\nIntegration with External Systems\nUsage Examples\nSingle Stock Analysis\nZipline Algorithm Analysis\nBayesian Analysis\nSector Analysis and Round Trips\nDevelopment Guide\nInstallation and Setup\nDocumentation\n.travis.yml\nWHATSNEW.md\ndocs/whatsnew.md\npyfolio/__init__.py\nversioneer.py\nPyFolio is a Python library for performance and risk analysis of financial portfolios. Developed by Quantopian Inc., it provides a comprehensive set of tools to evaluate trading strategies through visualizations and statistical metrics. This document provides a high-level overview of PyFolio's architecture, components, and functionality.\nAt its core, PyFolio takes portfolio performance data (such as returns, positions, and transactions) and generates \"tear sheets\" - collections of visualizations and metrics that help assess different aspects of trading strategy performance. PyFolio works seamlessly with theZiplinebacktesting library but can be used independently with any source of portfolio data.\nFor detailed information about specific components, seeCore ArchitectureorSpecialized Analysis Modules.\nSources:setup.py9-21README.md8-20\nSystem Architecture\nPyFolio follows a modular architecture where distinct components work together to process financial data and generate performance visualizations. The system is organized around core analysis modules and a tear sheet generation system that orchestrates the creation of various reports.\nExternal DependenciesCore Componentstears.py (Tear Sheet Generation)plotting.py (Visualization)timeseries.py (Returns Analysis)utils.py (Helper Functions)pos.py (Position Analysis)txn.py (Transaction Analysis)perf_attrib.py (Performance Attribution)round_trips.py (Round Trip Analysis)capacity.py (Strategy Capacity)empyrical (Performance Metrics)pandas (Data Structures)numpy (Numerical Operations)matplotlib (Base Plotting)seaborn (Enhanced Visualization)\nExternal Dependencies\nCore Components\ntears.py (Tear Sheet Generation)\nplotting.py (Visualization)\ntimeseries.py (Returns Analysis)\nutils.py (Helper Functions)\npos.py (Position Analysis)\ntxn.py (Transaction Analysis)\nperf_attrib.py (Performance Attribution)\nround_trips.py (Round Trip Analysis)\ncapacity.py (Strategy Capacity)\nempyrical (Performance Metrics)\npandas (Data Structures)\nnumpy (Numerical Operations)\nmatplotlib (Base Plotting)\nseaborn (Enhanced Visualization)\nThe architecture centers around the tear sheet generation system (tears.py), which coordinates the creation of various reports by calling specialized analysis modules. Each module focuses on a specific aspect of portfolio analysis:\ntears.py: Central orchestration for creating comprehensive tear sheets\nplotting.py: Visualization functions for creating plots\ntimeseries.py: Time series analysis of returns data\npos.py: Analysis of portfolio positions and exposures\ntxn.py: Transaction analysis and turnover calculations\nperf_attrib.py: Performance attribution to factors\nround_trips.py: Analysis of completed trades\ncapacity.py: Assessment of strategy capacity\nutils.py: Utility functions supporting other modules\nSources:pyfolio/__init__.py1-19setup.py44-54\nPyFolio processes financial data through a pipeline that transforms raw inputs into visual tear sheets. Understanding this data flow is essential to effectively using the library.\nVisualization (Tear Sheets)Processing PipelineInput DataReturns Time SeriesPosition RecordsTransaction RecordsBenchmark ReturnsRisk FactorsReturns Analysis(timeseries.py)Position Analysis(pos.py)Transaction Analysis(txn.py)Round Trip Analysis(round_trips.py)Performance Attribution(perf_attrib.py)Capacity Analysis(capacity.py)Returns Tear SheetPositions Tear SheetTransactions Tear SheetRound Trip Tear SheetPerformance Attribution Tear SheetCapacity Tear SheetFull Tear Sheet\nVisualization (Tear Sheets)\nProcessing Pipeline\nReturns Time Series\nPosition Records\nTransaction Records\nBenchmark Returns\nRisk Factors\nReturns Analysis(timeseries.py)\nPosition Analysis(pos.py)\nTransaction Analysis(txn.py)\nRound Trip Analysis(round_trips.py)\nPerformance Attribution(perf_attrib.py)\nCapacity Analysis(capacity.py)\nReturns Tear Sheet\nPositions Tear Sheet\nTransactions Tear Sheet\nRound Trip Tear Sheet\nPerformance Attribution Tear Sheet\nCapacity Tear Sheet\nFull Tear Sheet\nThe data flow begins with various input data types:\nReturns: Time series of portfolio returns (required)\nPositions: Records of portfolio holdings over time (optional)\nTransactions: Records of trades (optional)\nBenchmark Returns: Returns of a comparison benchmark (optional)\nRisk Factors: Factor returns for attribution analysis (optional)\nThese inputs are processed by specialized analysis modules, which calculate metrics and prepare data for visualization. The results are then rendered as tear sheets - comprehensive visual reports that help evaluate different aspects of portfolio performance.\nSources:README.md15-21\nTear Sheet Hierarchy\nTear sheets are the primary output of PyFolio. They provide visualizations and metrics to assess different aspects of portfolio performance. PyFolio offers several specialized tear sheets that can be used individually or combined into a comprehensive full tear sheet.\nKey Visualizationscreate_full_tear_sheet(Comprehensive Analysis)create_simple_tear_sheet(Basic Analysis)create_returns_tear_sheet(Returns Analysis)create_position_tear_sheet(Position Analysis)create_txn_tear_sheet(Transaction Analysis)create_round_trip_tear_sheet(Round Trip Analysis)create_interesting_times_tear_sheet(Performance During Key Events)create_capacity_tear_sheet(Strategy Capacity)create_perf_attrib_tear_sheet(Factor Attribution)plot_returnsplot_rolling_returnsplot_drawdown_periodsplot_monthly_returns_heatmapplot_holdingsplot_position_exposuresplot_turnover\nKey Visualizations\ncreate_full_tear_sheet(Comprehensive Analysis)\ncreate_simple_tear_sheet(Basic Analysis)\ncreate_returns_tear_sheet(Returns Analysis)\ncreate_position_tear_sheet(Position Analysis)\ncreate_txn_tear_sheet(Transaction Analysis)\ncreate_round_trip_tear_sheet(Round Trip Analysis)\ncreate_interesting_times_tear_sheet(Performance During Key Events)\ncreate_capacity_tear_sheet(Strategy Capacity)\ncreate_perf_attrib_tear_sheet(Factor Attribution)\nplot_returns\nplot_rolling_returns\nplot_drawdown_periods\nplot_monthly_returns_heatmap\nplot_holdings\nplot_position_exposures\nplot_turnover\nThe tear sheet hierarchy shows how different reports are related:\nFull Tear Sheet- A comprehensive analysis combining all specialized tear sheets\nSimple Tear Sheet- A streamlined version focusing on returns, positions, and transactions\nSpecialized Tear Sheets:Returns Tear Sheet- Analysis of returns, drawdowns, and distributionPosition Tear Sheet- Analysis of holdings and exposuresTransaction Tear Sheet- Analysis of trading activity and turnoverRound Trip Tear Sheet- Analysis of completed tradesInteresting Times Tear Sheet- Performance during specific market eventsCapacity Tear Sheet- Assessment of strategy capacityPerformance Attribution Tear Sheet- Decomposition of returns into factor components\nReturns Tear Sheet- Analysis of returns, drawdowns, and distribution\nPosition Tear Sheet- Analysis of holdings and exposures\nTransaction Tear Sheet- Analysis of trading activity and turnover\nRound Trip Tear Sheet- Analysis of completed trades\nInteresting Times Tear Sheet- Performance during specific market events\nCapacity Tear Sheet- Assessment of strategy capacity\nPerformance Attribution Tear Sheet- Decomposition of returns into factor components\nEach tear sheet incorporates various plotting functions that visualize specific aspects of portfolio performance.\nSources:WHATSNEW.md7-16WHATSNEW.md29-43\nIntegration with External Systems\nPyFolio is designed to work with other financial analysis systems, particularly Zipline, but can be used with any source of portfolio data.\n\"Other Data Sources\"\"PyFolio (Analysis)\"\"Zipline (Backtesting)\"User\"Other Data Sources\"\"PyFolio (Analysis)\"\"Zipline (Backtesting)\"UserProcess backtest resultsalt[Direct Analysis]Run trading algorithm backtestextract_rets_pos_txn_from_zipline()Get portfolio dataReturns, positions, transactionsInput portfolio dataGenerate tear sheetsDisplay performance visualizations\nPyFolio integrates with external systems in the following ways:\nZipline Integration:Directly consumes Zipline backtest resultsUses utility functions to extract returns, positions, and transactions\nZipline Integration:\nDirectly consumes Zipline backtest results\nUses utility functions to extract returns, positions, and transactions\nEmpyrical Integration:Leverages the Empyrical library for performance metrics calculationsShares consistent risk and return calculations with Zipline\nEmpyrical Integration:\nLeverages the Empyrical library for performance metrics calculations\nShares consistent risk and return calculations with Zipline\nCustom Data Sources:Accepts pandas DataFrames from any sourceCan work with CSV files, databases, APIs, or other data providers\nCustom Data Sources:\nAccepts pandas DataFrames from any source\nCan work with CSV files, databases, APIs, or other data providers\nThis integration flexibility allows PyFolio to fit into various financial analysis workflows and accommodate different data sources.\nSources:setup.py44-54README.md10-11\nKey Features and Capabilities\nPyFolio provides a comprehensive suite of portfolio analysis tools. The table below summarizes the key features available:\nPyFolio offers both Bayesian and frequentist approaches to performance analysis, allowing for robust statistical assessment of trading strategies. The library continues to evolve with new features being added in each release.\nSources:WHATSNEW.md29-43WHATSNEW.md158-183\nVersion and Development\nPyFolio is an open-source project maintained by Quantopian Inc. The library is continuously improved with new features and bug fixes. 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pyfolio/mcp_output/env_info.json

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pyfolio/mcp_output/mcp_logs/llm_statistics.json

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pyfolio/mcp_output/mcp_logs/run_log.json

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+    "stderr": "hh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/pyfolio/mcp_output/mcp_plugin/mcp_service.py\", line 8, in <module>\n    from pyfolio.tears import (\n  File \"/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/pyfolio/source/pyfolio/__init__.py\", line 1, in <module>\n    from . import utils\n  File \"/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/pyfolio/source/pyfolio/utils.py\", line 21, in <module>\n    from matplotlib.pyplot import cm\n  File \"/home/wshiah/.local/lib/python3.10/site-packages/matplotlib/__init__.py\", line 161, in <module>\n    from . import _api, _version, cbook, _docstring, rcsetup\n  File \"/home/wshiah/.local/lib/python3.10/site-packages/matplotlib/rcsetup.py\", line 28, in <module>\n    from matplotlib.colors import Colormap, is_color_like\n  File \"/home/wshiah/.local/lib/python3.10/site-packages/matplotlib/colors.py\", line 52, in <module>\n    from PIL import Image\nModuleNotFoundError: No module named 'PIL'\n\n"
+  },
+  "run_result": {
+    "success": false,
+    "test_passed": false,
+    "exit_code": 1,
+    "stdout": "",
+    "stderr": "hh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/pyfolio/mcp_output/mcp_plugin/mcp_service.py\", line 8, in <module>\n    from pyfolio.tears import (\n  File \"/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/pyfolio/source/pyfolio/__init__.py\", line 1, in <module>\n    from . import utils\n  File \"/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/pyfolio/source/pyfolio/utils.py\", line 21, in <module>\n    from matplotlib.pyplot import cm\n  File \"/home/wshiah/.local/lib/python3.10/site-packages/matplotlib/__init__.py\", line 161, in <module>\n    from . import _api, _version, cbook, _docstring, rcsetup\n  File \"/home/wshiah/.local/lib/python3.10/site-packages/matplotlib/rcsetup.py\", line 28, in <module>\n    from matplotlib.colors import Colormap, is_color_like\n  File \"/home/wshiah/.local/lib/python3.10/site-packages/matplotlib/colors.py\", line 52, in <module>\n    from PIL import Image\nModuleNotFoundError: No module named 'PIL'\n\n",
+    "timestamp": 1760153154.1335971,
+    "error_type": "ImportError",
+    "error": "Module import failed: ERROR conda.cli.main_run:execute(41): `conda run python mcp_output/start_mcp.py` failed. (See above for error)\nTraceback (most recent call last):\n  File \"/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/pyfolio/mcp_output/start_mcp.py\", line 17, in <module>\n    from mcp_service import create_app\n  File \"/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/pyfolio/mcp_output/mcp_plugin/mcp_service.py\", line 8, in <module>\n    from pyfolio.tears import (\n  File \"/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/pyfolio/source/pyfolio/__init__.py\", line 1, in <module>\n    from . import utils\n  File \"/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/pyfolio/source/pyfolio/utils.py\", line 21, in <module>\n    from matplotlib.pyplot import cm\n  File \"/home/wshiah/.local/lib/python3.10/site-packages/matplotlib/__init__.py\", line 161, in <module>\n    from . import _api, _version, cbook, _docstring, rcsetup\n  File \"/home/wshiah/.local/lib/python3.10/site-packages/matplotlib/rcsetup.py\", line 28, in <module>\n    from matplotlib.colors import Colormap, is_color_like\n  File \"/home/wshiah/.local/lib/python3.10/site-packages/matplotlib/colors.py\", line 52, in <module>\n    from PIL import Image\nModuleNotFoundError: No module named 'PIL'\n\n",
+    "details": {
+      "command": "/home/wshiah/code/miniconda3/bin/conda run -n pyfolio_152676_env --cwd /export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/pyfolio python mcp_output/start_mcp.py",
+      "working_directory": "/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/pyfolio",
+      "environment_type": "conda"
+    }
+  },
+  "environment": {
+    "type": "conda",
+    "name": "pyfolio_152676_env",
+    "files": {},
+    "python": "3.10",
+    "exec_prefix": []
+  },
+  "plugin_info": {
+    "files": {
+      "mcp_output/start_mcp.py": "/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/pyfolio/mcp_output/start_mcp.py",
+      "mcp_output/mcp_plugin/__init__.py": "/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/pyfolio/mcp_output/mcp_plugin/__init__.py",
+      "mcp_output/mcp_plugin/mcp_service.py": "/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/pyfolio/mcp_output/mcp_plugin/mcp_service.py",
+      "mcp_output/mcp_plugin/adapter.py": "/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/pyfolio/mcp_output/mcp_plugin/adapter.py",
+      "mcp_output/mcp_plugin/main.py": "/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/pyfolio/mcp_output/mcp_plugin/main.py",
+      "mcp_output/requirements.txt": "/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/pyfolio/mcp_output/requirements.txt",
+      "mcp_output/README_MCP.md": "/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/pyfolio/mcp_output/README_MCP.md",
+      "mcp_output/tests_mcp/test_mcp_basic.py": "/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/pyfolio/mcp_output/tests_mcp/test_mcp_basic.py"
+    },
+    "adapter_mode": "import",
+    "endpoints": [
+      "create_returns_tear_sheet",
+      "create_position_tear_sheet",
+      "create_txn_tear_sheet",
+      "show_worst_drawdown_periods",
+      "plot_rolling_returns",
+      "plot_rolling_beta",
+      "plot_rolling_volatility",
+      "plot_drawdown_periods",
+      "plot_monthly_returns_heatmap",
+      "plot_monthly_returns_dist",
+      "plot_holdings",
+      "plot_sector_allocations",
+      "cum_returns",
+      "cum_returns_final",
+      "aggregate_returns",
+      "annual_return",
+      "annual_volatility",
+      "sharpe_ratio",
+      "sortino_ratio",
+      "max_drawdown",
+      "alpha_beta",
+      "beta",
+      "alpha",
+      "stability_of_timeseries",
+      "tail_ratio",
+      "value_at_risk",
+      "capture",
+      "up_capture",
+      "down_capture",
+      "create_full_tear_sheet",
+      "create_simple_tear_sheet",
+      "create_interesting_periods_tear_sheet",
+      "compute_max_capacity",
+      "compute_capacity",
+      "extract_returns",
+      "extract_positions",
+      "extract_transactions",
+      "extract_benchmark_returns",
+      "extract_interesting_periods",
+      "extract_sector_allocations"
+    ],
+    "mcp_dir": "/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/pyfolio/mcp_output/mcp_plugin",
+    "tests_dir": "/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/pyfolio/mcp_output/tests_mcp",
+    "main_entry": "start_mcp.py",
+    "readme_path": "/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/pyfolio/mcp_output/README_MCP.md",
+    "requirements": [
+      "fastmcp>=0.1.0",
+      "pydantic>=2.0.0"
+    ]
+  },
+  "fastmcp_installed": false
+}

pyfolio/mcp_output/mcp_plugin/adapter.py

@@ -0,0 +1,326 @@
+import os
+import sys
+
+# Path settings
+source_path = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))), "source")
+sys.path.insert(0, source_path)
+
+# Import statements
+from pyfolio.tears import create_full_tear_sheet, create_simple_tear_sheet, create_returns_tear_sheet, create_position_tear_sheet, create_txn_tear_sheet, create_round_trip_tear_sheet, create_interesting_times_tear_sheet, create_capacity_tear_sheet, create_perf_attrib_tear_sheet
+from pyfolio.plotting import plot_returns, plot_rolling_returns, plot_drawdown_periods, plot_monthly_returns_heatmap, plot_holdings, plot_position_exposures, plot_turnover
+from pyfolio.timeseries import timeseries
+from pyfolio.utils import utils
+from pyfolio.capacity import capacity
+from pyfolio.round_trips import round_trips
+from pyfolio.perf_attrib import perf_attrib
+from pyfolio.txn import txn
+from pyfolio.pos import pos
+
+# Adapter class
+class Adapter:
+    """
+    Adapter class for the MCP plugin to integrate with the PyFolio library.
+    Provides methods to utilize all identified functions and classes from the PyFolio library.
+    """
+
+    def __init__(self):
+        """
+        Initialize the Adapter class with default mode set to 'import'.
+        """
+        self.mode = "import"
+
+    # -------------------------------------------------------------------------
+    # Tear Sheet Generation Methods
+    # -------------------------------------------------------------------------
+
+    def generate_full_tear_sheet(self, returns, positions=None, transactions=None, **kwargs):
+        """
+        Generate a comprehensive full tear sheet.
+
+        Parameters:
+        - returns: pandas.Series of portfolio returns.
+        - positions: pandas.DataFrame of portfolio positions (optional).
+        - transactions: pandas.DataFrame of portfolio transactions (optional).
+        - kwargs: Additional arguments for customization.
+
+        Returns:
+        - dict: Status and result of the operation.
+        """
+        try:
+            create_full_tear_sheet(returns, positions=positions, transactions=transactions, **kwargs)
+            return {"status": "success", "message": "Full tear sheet generated successfully."}
+        except Exception as e:
+            return {"status": "error", "message": f"Failed to generate full tear sheet: {str(e)}"}
+
+    def generate_simple_tear_sheet(self, returns, **kwargs):
+        """
+        Generate a simple tear sheet.
+
+        Parameters:
+        - returns: pandas.Series of portfolio returns.
+        - kwargs: Additional arguments for customization.
+
+        Returns:
+        - dict: Status and result of the operation.
+        """
+        try:
+            create_simple_tear_sheet(returns, **kwargs)
+            return {"status": "success", "message": "Simple tear sheet generated successfully."}
+        except Exception as e:
+            return {"status": "error", "message": f"Failed to generate simple tear sheet: {str(e)}"}
+
+    def generate_returns_tear_sheet(self, returns, **kwargs):
+        """
+        Generate a returns tear sheet.
+
+        Parameters:
+        - returns: pandas.Series of portfolio returns.
+        - kwargs: Additional arguments for customization.
+
+        Returns:
+        - dict: Status and result of the operation.
+        """
+        try:
+            create_returns_tear_sheet(returns, **kwargs)
+            return {"status": "success", "message": "Returns tear sheet generated successfully."}
+        except Exception as e:
+            return {"status": "error", "message": f"Failed to generate returns tear sheet: {str(e)}"}
+
+    def generate_position_tear_sheet(self, positions, **kwargs):
+        """
+        Generate a position tear sheet.
+
+        Parameters:
+        - positions: pandas.DataFrame of portfolio positions.
+        - kwargs: Additional arguments for customization.
+
+        Returns:
+        - dict: Status and result of the operation.
+        """
+        try:
+            create_position_tear_sheet(positions, **kwargs)
+            return {"status": "success", "message": "Position tear sheet generated successfully."}
+        except Exception as e:
+            return {"status": "error", "message": f"Failed to generate position tear sheet: {str(e)}"}
+
+    def generate_transaction_tear_sheet(self, transactions, **kwargs):
+        """
+        Generate a transaction tear sheet.
+
+        Parameters:
+        - transactions: pandas.DataFrame of portfolio transactions.
+        - kwargs: Additional arguments for customization.
+
+        Returns:
+        - dict: Status and result of the operation.
+        """
+        try:
+            create_txn_tear_sheet(transactions, **kwargs)
+            return {"status": "success", "message": "Transaction tear sheet generated successfully."}
+        except Exception as e:
+            return {"status": "error", "message": f"Failed to generate transaction tear sheet: {str(e)}"}
+
+    def generate_round_trip_tear_sheet(self, transactions, **kwargs):
+        """
+        Generate a round trip tear sheet.
+
+        Parameters:
+        - transactions: pandas.DataFrame of portfolio transactions.
+        - kwargs: Additional arguments for customization.
+
+        Returns:
+        - dict: Status and result of the operation.
+        """
+        try:
+            create_round_trip_tear_sheet(transactions, **kwargs)
+            return {"status": "success", "message": "Round trip tear sheet generated successfully."}
+        except Exception as e:
+            return {"status": "error", "message": f"Failed to generate round trip tear sheet: {str(e)}"}
+
+    def generate_interesting_times_tear_sheet(self, returns, **kwargs):
+        """
+        Generate an interesting times tear sheet.
+
+        Parameters:
+        - returns: pandas.Series of portfolio returns.
+        - kwargs: Additional arguments for customization.
+
+        Returns:
+        - dict: Status and result of the operation.
+        """
+        try:
+            create_interesting_times_tear_sheet(returns, **kwargs)
+            return {"status": "success", "message": "Interesting times tear sheet generated successfully."}
+        except Exception as e:
+            return {"status": "error", "message": f"Failed to generate interesting times tear sheet: {str(e)}"}
+
+    def generate_capacity_tear_sheet(self, returns, **kwargs):
+        """
+        Generate a capacity tear sheet.
+
+        Parameters:
+        - returns: pandas.Series of portfolio returns.
+        - kwargs: Additional arguments for customization.
+
+        Returns:
+        - dict: Status and result of the operation.
+        """
+        try:
+            create_capacity_tear_sheet(returns, **kwargs)
+            return {"status": "success", "message": "Capacity tear sheet generated successfully."}
+        except Exception as e:
+            return {"status": "error", "message": f"Failed to generate capacity tear sheet: {str(e)}"}
+
+    def generate_performance_attribution_tear_sheet(self, returns, factor_returns, **kwargs):
+        """
+        Generate a performance attribution tear sheet.
+
+        Parameters:
+        - returns: pandas.Series of portfolio returns.
+        - factor_returns: pandas.DataFrame of factor returns.
+        - kwargs: Additional arguments for customization.
+
+        Returns:
+        - dict: Status and result of the operation.
+        """
+        try:
+            create_perf_attrib_tear_sheet(returns, factor_returns, **kwargs)
+            return {"status": "success", "message": "Performance attribution tear sheet generated successfully."}
+        except Exception as e:
+            return {"status": "error", "message": f"Failed to generate performance attribution tear sheet: {str(e)}"}
+
+    # -------------------------------------------------------------------------
+    # Plotting Methods
+    # -------------------------------------------------------------------------
+
+    def plot_portfolio_returns(self, returns):
+        """
+        Plot portfolio returns.
+
+        Parameters:
+        - returns: pandas.Series of portfolio returns.
+
+        Returns:
+        - dict: Status and result of the operation.
+        """
+        try:
+            plot_returns(returns)
+            return {"status": "success", "message": "Portfolio returns plotted successfully."}
+        except Exception as e:
+            return {"status": "error", "message": f"Failed to plot portfolio returns: {str(e)}"}
+
+    def plot_rolling_returns(self, returns, benchmark_returns=None):
+        """
+        Plot rolling returns.
+
+        Parameters:
+        - returns: pandas.Series of portfolio returns.
+        - benchmark_returns: pandas.Series of benchmark returns (optional).
+
+        Returns:
+        - dict: Status and result of the operation.
+        """
+        try:
+            plot_rolling_returns(returns, benchmark_returns=benchmark_returns)
+            return {"status": "success", "message": "Rolling returns plotted successfully."}
+        except Exception as e:
+            return {"status": "error", "message": f"Failed to plot rolling returns: {str(e)}"}
+
+    def plot_drawdown_periods(self, returns):
+        """
+        Plot drawdown periods.
+
+        Parameters:
+        - returns: pandas.Series of portfolio returns.
+
+        Returns:
+        - dict: Status and result of the operation.
+        """
+        try:
+            plot_drawdown_periods(returns)
+            return {"status": "success", "message": "Drawdown periods plotted successfully."}
+        except Exception as e:
+            return {"status": "error", "message": f"Failed to plot drawdown periods: {str(e)}"}
+
+    def plot_monthly_returns_heatmap(self, returns):
+        """
+        Plot monthly returns heatmap.
+
+        Parameters:
+        - returns: pandas.Series of portfolio returns.
+
+        Returns:
+        - dict: Status and result of the operation.
+        """
+        try:
+            plot_monthly_returns_heatmap(returns)
+            return {"status": "success", "message": "Monthly returns heatmap plotted successfully."}
+        except Exception as e:
+            return {"status": "error", "message": f"Failed to plot monthly returns heatmap: {str(e)}"}
+
+    def plot_holdings(self, positions):
+        """
+        Plot portfolio holdings.
+
+        Parameters:
+        - positions: pandas.DataFrame of portfolio positions.
+
+        Returns:
+        - dict: Status and result of the operation.
+        """
+        try:
+            plot_holdings(positions)
+            return {"status": "success", "message": "Portfolio holdings plotted successfully."}
+        except Exception as e:
+            return {"status": "error", "message": f"Failed to plot portfolio holdings: {str(e)}"}
+
+    def plot_position_exposures(self, positions):
+        """
+        Plot position exposures.
+
+        Parameters:
+        - positions: pandas.DataFrame of portfolio positions.
+
+        Returns:
+        - dict: Status and result of the operation.
+        """
+        try:
+            plot_position_exposures(positions)
+            return {"status": "success", "message": "Position exposures plotted successfully."}
+        except Exception as e:
+            return {"status": "error", "message": f"Failed to plot position exposures: {str(e)}"}
+
+    def plot_turnover(self, transactions):
+        """
+        Plot portfolio turnover.
+
+        Parameters:
+        - transactions: pandas.DataFrame of portfolio transactions.
+
+        Returns:
+        - dict: Status and result of the operation.
+        """
+        try:
+            plot_turnover(transactions)
+            return {"status": "success", "message": "Portfolio turnover plotted successfully."}
+        except Exception as e:
+            return {"status": "error", "message": f"Failed to plot portfolio turnover: {str(e)}"}
+
+    # -------------------------------------------------------------------------
+    # Utility Methods
+    # -------------------------------------------------------------------------
+
+    # Add additional utility methods as needed based on the analysis results.
+
+    # -------------------------------------------------------------------------
+    # Error Handling and Fallback
+    # -------------------------------------------------------------------------
+
+    def handle_import_failure(self):
+        """
+        Handle cases where imports fail.
+
+        Returns:
+        - dict: Status and fallback message.
+        """
+        return {"status": "error", "message": "Failed to import required modules. Ensure the PyFolio library is correctly installed and accessible."}

pyfolio/mcp_output/mcp_plugin/main.py

@@ -0,0 +1,13 @@
+"""
+MCP Service Auto-Wrapper - Auto-generated
+"""
+from mcp_service import create_app
+
+def main():
+    """Main entry point"""
+    app = create_app()
+    return app
+
+if __name__ == "__main__":
+    app = main()
+    app.run()

pyfolio/mcp_output/mcp_plugin/mcp_service.py

@@ -0,0 +1,179 @@
+import os
+import sys
+
+source_path = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))), "source")
+sys.path.insert(0, source_path)
+
+from fastmcp import FastMCP
+from pyfolio.tears import (
+    create_full_tear_sheet,
+    create_simple_tear_sheet,
+    create_returns_tear_sheet,
+    create_position_tear_sheet,
+    create_txn_tear_sheet,
+    create_round_trip_tear_sheet,
+    create_interesting_times_tear_sheet,
+    create_capacity_tear_sheet,
+    create_perf_attrib_tear_sheet,
+)
+from pyfolio.plotting import (
+    axes_style,
+    customize,
+    plot_annual_returns,
+    plot_capacity_sweep,
+    plot_cones,
+    plot_daily_turnover_hist,
+    plot_daily_volume,
+    plot_drawdown_periods,
+    plot_drawdown_underwater,
+    plot_exposures,
+    plot_gross_leverage,
+    plot_holdings,
+    plot_long_short_holdings,
+    plot_max_median_position_concentration,
+    plot_monthly_returns_dist,
+    plot_monthly_returns_heatmap,
+    plot_monthly_returns_timeseries,
+    plot_perf_stats,
+    plot_prob_profit_trade,
+    plot_return_quantiles,
+    plot_returns,
+    plot_rolling_beta,
+    plot_rolling_returns,
+    plot_rolling_sharpe,
+    plot_rolling_volatility,
+    plot_round_trip_lifetimes,
+    plot_sector_allocations,
+    plot_slippage_sensitivity,
+    plot_slippage_sweep,
+    plot_turnover,
+    plot_txn_time_hist,
+    plotting_context,
+    show_and_plot_top_positions,
+    show_perf_stats,
+    show_profit_attribution,
+    show_worst_drawdown_periods,
+)
+
+mcp = FastMCP("pyfolio_service")
+
+@mcp.tool(name="generate_full_tear_sheet", description="Generate a comprehensive tear sheet for portfolio analysis.")
+def generate_full_tear_sheet(returns: list, positions: dict = None, transactions: dict = None, benchmark_rets: list = None) -> dict:
+    try:
+        create_full_tear_sheet(returns, positions, transactions, benchmark_rets)
+        return {"success": True, "result": "Full tear sheet generated successfully.", "error": None}
+    except Exception as e:
+        return {"success": False, "result": None, "error": str(e)}
+
+@mcp.tool(name="generate_simple_tear_sheet", description="Generate a basic tear sheet for portfolio analysis.")
+def generate_simple_tear_sheet(returns: list) -> dict:
+    try:
+        create_simple_tear_sheet(returns)
+        return {"success": True, "result": "Simple tear sheet generated successfully.", "error": None}
+    except Exception as e:
+        return {"success": False, "result": None, "error": str(e)}
+
+@mcp.tool(name="generate_returns_tear_sheet", description="Generate a tear sheet for returns analysis.")
+def generate_returns_tear_sheet(returns: list) -> dict:
+    try:
+        create_returns_tear_sheet(returns)
+        return {"success": True, "result": "Returns tear sheet generated successfully.", "error": None}
+    except Exception as e:
+        return {"success": False, "result": None, "error": str(e)}
+
+@mcp.tool(name="generate_position_tear_sheet", description="Generate a tear sheet for position analysis.")
+def generate_position_tear_sheet(positions: dict) -> dict:
+    try:
+        create_position_tear_sheet(positions)
+        return {"success": True, "result": "Position tear sheet generated successfully.", "error": None}
+    except Exception as e:
+        return {"success": False, "result": None, "error": str(e)}
+
+@mcp.tool(name="generate_transaction_tear_sheet", description="Generate a tear sheet for transaction analysis.")
+def generate_transaction_tear_sheet(transactions: dict) -> dict:
+    try:
+        create_txn_tear_sheet(transactions)
+        return {"success": True, "result": "Transaction tear sheet generated successfully.", "error": None}
+    except Exception as e:
+        return {"success": False, "result": None, "error": str(e)}
+
+@mcp.tool(name="generate_round_trip_tear_sheet", description="Generate a tear sheet for round trip analysis.")
+def generate_round_trip_tear_sheet(round_trips: dict) -> dict:
+    try:
+        create_round_trip_tear_sheet(round_trips)
+        return {"success": True, "result": "Round trip tear sheet generated successfully.", "error": None}
+    except Exception as e:
+        return {"success": False, "result": None, "error": str(e)}
+
+@mcp.tool(name="generate_interesting_times_tear_sheet", description="Generate a tear sheet for performance during key events.")
+def generate_interesting_times_tear_sheet(returns: list, events: list) -> dict:
+    try:
+        create_interesting_times_tear_sheet(returns, events)
+        return {"success": True, "result": "Interesting times tear sheet generated successfully.", "error": None}
+    except Exception as e:
+        return {"success": False, "result": None, "error": str(e)}
+
+@mcp.tool(name="generate_capacity_tear_sheet", description="Generate a tear sheet for strategy capacity analysis.")
+def generate_capacity_tear_sheet(returns: list, positions: dict) -> dict:
+    try:
+        create_capacity_tear_sheet(returns, positions)
+        return {"success": True, "result": "Capacity tear sheet generated successfully.", "error": None}
+    except Exception as e:
+        return {"success": False, "result": None, "error": str(e)}
+
+@mcp.tool(name="generate_performance_attribution_tear_sheet", description="Generate a tear sheet for performance attribution analysis.")
+def generate_performance_attribution_tear_sheet(returns: list, factors: dict) -> dict:
+    try:
+        create_perf_attrib_tear_sheet(returns, factors)
+        return {"success": True, "result": "Performance attribution tear sheet generated successfully.", "error": None}
+    except Exception as e:
+        return {"success": False, "result": None, "error": str(e)}
+
+@mcp.tool(name="plot_annual_returns", description="Plot annual returns as a bar chart.")
+def plot_annual_returns_tool(returns: list) -> dict:
+    try:
+        ax = plot_annual_returns(returns)
+        return {"success": True, "result": ax, "error": None}
+    except Exception as e:
+        return {"success": False, "result": None, "error": str(e)}
+
+@mcp.tool(name="plot_monthly_returns_heatmap", description="Plot a heatmap of monthly returns.")
+def plot_monthly_returns_heatmap_tool(returns: list) -> dict:
+    try:
+        ax = plot_monthly_returns_heatmap(returns)
+        return {"success": True, "result": ax, "error": None}
+    except Exception as e:
+        return {"success": False, "result": None, "error": str(e)}
+
+@mcp.tool(name="plot_drawdown_periods", description="Plot cumulative returns highlighting top drawdown periods.")
+def plot_drawdown_periods_tool(returns: list, top: int = 10) -> dict:
+    try:
+        ax = plot_drawdown_periods(returns, top)
+        return {"success": True, "result": ax, "error": None}
+    except Exception as e:
+        return {"success": False, "result": None, "error": str(e)}
+
+@mcp.tool(name="plot_rolling_returns", description="Plot cumulative rolling returns versus benchmarks.")
+def plot_rolling_returns_tool(returns: list, factor_returns: list = None, live_start_date: str = None) -> dict:
+    try:
+        ax = plot_rolling_returns(returns, factor_returns, live_start_date)
+        return {"success": True, "result": ax, "error": None}
+    except Exception as e:
+        return {"success": False, "result": None, "error": str(e)}
+
+@mcp.tool(name="plot_turnover", description="Plot turnover over time.")
+def plot_turnover_tool(returns: list, transactions: dict, positions: dict, turnover_denom: str = "AGB") -> dict:
+    try:
+        ax = plot_turnover(returns, transactions, positions, turnover_denom)
+        return {"success": True, "result": ax, "error": None}
+    except Exception as e:
+        return {"success": False, "result": None, "error": str(e)}
+
+def create_app() -> FastMCP:
+    """
+    Create and return the FastMCP application instance.
+
+    Returns:
+        FastMCP: The FastMCP application instance.
+    """
+    return mcp

pyfolio/mcp_output/requirements.txt

@@ -0,0 +1,12 @@
+fastmcp>=0.1.0
+pydantic>=2.0.0
+numpy
+pandas
+matplotlib
+seaborn
+scipy
+statsmodels
+
+# Optional Dependencies
+# jupyter
+# pytest

pyfolio/mcp_output/simple_revise_error_analysis.json

@@ -0,0 +1,36 @@
+{
+  "status": "FAIL",
+  "next_action": "fix_directly",
+  "confidence": 0.9,
+  "summary": {
+    "error_type": "ModuleNotFoundError",
+    "error_message": "No module named 'PIL'",
+    "analysis": [
+      {
+        "description": "The error occurs because the Python Imaging Library (Pillow) is not installed in the environment. The 'PIL' module is part of Pillow, which is required by matplotlib.colors.",
+        "can_be_fixed_directly": true,
+        "repair_strategy": "Install the missing dependency 'Pillow' using the appropriate package manager (e.g., pip or conda)."
+      },
+      {
+        "historical_errors": [
+          {
+            "node": "CodeCheckNode",
+            "type": "ImportError",
+            "message": "Function 'plot_position_exposures' does not exist in module 'pyfolio.plotting'"
+          },
+          {
+            "node": "RunNode",
+            "type": "PluginSmokeFailed",
+            "message": "Module import failed: ERROR conda.cli.main_run:execute(41): `conda run python mcp_output/start_mcp.py` failed."
+          },
+          {
+            "node": "RunNode",
+            "type": "RuntimeError",
+            "message": "Module import failed: ERROR conda.cli.main_run:execute(41): `conda run python mcp_output/start_mcp.py` failed."
+          }
+        ],
+        "note": "The current error is unrelated to the historical errors but may indicate broader dependency management issues in the project."
+      }
+    ]
+  }
+}

pyfolio/mcp_output/start_mcp.py

@@ -0,0 +1,39 @@
+"""
+MCP Service Startup Entry
+"""
+import sys
+import os
+
+project_root = os.path.dirname(os.path.abspath(__file__))
+mcp_plugin_dir = os.path.join(project_root, "mcp_plugin")
+if mcp_plugin_dir not in sys.path:
+    sys.path.insert(0, mcp_plugin_dir)
+
+# Set path to source directory
+source_path = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))), "source")
+sys.path.insert(0, source_path)
+
+# Ensure required dependencies are available
+try:
+    from PIL import Image  # Verify Pillow is installed
+except ImportError:
+    raise ImportError("Pillow is not installed. Please install it using 'pip install pillow'.")
+
+from mcp_service import create_app
+
+def main():
+    """Start FastMCP service"""
+    app = create_app()
+    # Use environment variable to configure port, default 8000
+    port = int(os.environ.get("MCP_PORT", "8000"))
+    
+    # Choose transport mode based on environment variable
+    transport = os.environ.get("MCP_TRANSPORT", "stdio")
+    if transport == "http":
+        app.run(transport="http", host="0.0.0.0", port=port)
+    else:
+        # Default to STDIO mode
+        app.run()
+
+if __name__ == "__main__":
+    main()

pyfolio/mcp_output/tests_mcp/test_mcp_basic.py

@@ -0,0 +1,49 @@
+"""
+MCP Service Basic Test
+"""
+import sys
+import os
+
+project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+mcp_plugin_dir = os.path.join(project_root, "mcp_plugin")
+if mcp_plugin_dir not in sys.path:
+    sys.path.insert(0, mcp_plugin_dir)
+
+source_path = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))), "source")
+sys.path.insert(0, source_path)
+
+def test_import_mcp_service():
+    """Test if MCP service can be imported normally"""
+    try:
+        from mcp_service import create_app
+        app = create_app()
+        assert app is not None
+        print("MCP service imported successfully")
+        return True
+    except Exception as e:
+        print("MCP service import failed: " + str(e))
+        return False
+
+def test_adapter_init():
+    """Test if adapter can be initialized normally"""
+    try:
+        from adapter import Adapter
+        adapter = Adapter()
+        assert adapter is not None
+        print("Adapter initialized successfully")
+        return True
+    except Exception as e:
+        print("Adapter initialization failed: " + str(e))
+        return False
+
+if __name__ == "__main__":
+    print("Running MCP service basic test...")
+    test1 = test_import_mcp_service()
+    test2 = test_adapter_init()
+    
+    if test1 and test2:
+        print("All basic tests passed")
+        sys.exit(0)
+    else:
+        print("Some tests failed")
+        sys.exit(1)

pyfolio/mcp_output/tests_smoke/test_smoke.py

@@ -0,0 +1,29 @@
+import importlib, sys
+import os
+
+# Add current directory to Python path
+sys.path.insert(0, os.getcwd())
+
+source_dir = os.path.join(os.getcwd(), "source")
+if os.path.exists(source_dir):
+    sys.path.insert(0, source_dir)
+
+
+try:
+    importlib.import_module("pyfolio")
+    print("OK - Successfully imported pyfolio")
+except ImportError as e:
+    print(f"Failed to import pyfolio: {e}")
+    fallback_packages = []
+
+    fallback_packages = ['pyfolio']
+
+    for pkg in fallback_packages:
+        try:
+            importlib.import_module(pkg)
+            print(f"OK - Successfully imported {pkg}")
+            break
+        except ImportError:
+            continue
+    else:
+        print("All import attempts failed")

pyfolio/source/.gitattributes

@@ -0,0 +1 @@
+pyfolio/_version.py export-subst

pyfolio/source/.github/ISSUE_TEMPLATE.md

@@ -0,0 +1,21 @@
+## Problem Description
+
+**Please provide a minimal, self-contained, and reproducible example:**
+```python
+[Paste code here]
+```
+
+**Please provide the full traceback:**
+```python
+[Paste traceback here]
+```
+
+**Please provide any additional information below:**
+
+
+## Versions
+
+* Pyfolio version: 
+* Python version: 
+* Pandas version: 
+* Matplotlib version: 

pyfolio/source/.gitignore

@@ -0,0 +1,63 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*,cover
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# VIM
+*.sw?
+
+# IPython notebook checkpoints
+.ipynb_checkpoints/

pyfolio/source/.travis.yml

@@ -0,0 +1,54 @@
+language: python
+sudo: false
+
+python: 
+  - 2.7
+  - 3.7
+
+env:
+  - PANDAS_VERSION=0.18.1
+  - PANDAS_VERSION=0.25.0
+
+matrix:
+  exclude:
+  - python: 3.7
+    env: PANDAS_VERSION=0.18.1
+  - python: 2.7
+    env: PANDAS_VERSION=0.25.0
+
+before_install:
+  # We do this conditionally because it saves us some downloading if the
+  # version is the same.
+  - if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then
+      wget https://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh;
+    else
+      wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh;
+    fi
+  - bash miniconda.sh -b -p $HOME/miniconda
+  - export PATH="$HOME/miniconda/bin:$PATH"
+  # required for using mkl-service
+  - export MKL_THREADING_LAYER=GNU
+  - hash -r
+  - conda config --set always_yes yes --set changeps1 no
+  - conda update -q conda
+  # Useful for debugging any issues with conda
+  - conda info -a
+  - cp pyfolio/tests/matplotlibrc .
+
+install:
+  - conda create -q -n testenv --yes python=$TRAVIS_PYTHON_VERSION ipython numpy scipy nose matplotlib pandas=$PANDAS_VERSION flake8 seaborn scikit-learn runipy pandas-datareader
+  - source activate testenv
+  - pip install -e .[all]
+
+before_script:
+  - "flake8 pyfolio"
+
+script:
+  - nosetests $TESTCMD
+
+notifications:
+  email: false
+
+branches:
+  only:
+    - master

pyfolio/source/LICENSE

@@ -0,0 +1,202 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
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+      negligent acts) or agreed to in writing, shall any Contributor be
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+
+   9. Accepting Warranty or Additional Liability. While redistributing
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+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
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+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "{}"
+      replaced with your own identifying information. (Don't include
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+
+   Copyright 2018 Quantopian, Inc.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
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+   See the License for the specific language governing permissions and
+   limitations under the License.
+

pyfolio/source/MANIFEST.in

@@ -0,0 +1,3 @@
+include versioneer.py
+include pyfolio/_version.py
+include LICENSE

pyfolio/source/README.md

@@ -0,0 +1,81 @@
+![pyfolio](https://media.quantopian.com/logos/open_source/pyfolio-logo-03.png "pyfolio")
+
+# pyfolio
+
+[![Join the chat at https://gitter.im/quantopian/pyfolio](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/quantopian/pyfolio?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
+[![build status](https://travis-ci.org/quantopian/pyfolio.png?branch=master)](https://travis-ci.org/quantopian/pyfolio)
+
+pyfolio is a Python library for performance and risk analysis of
+financial portfolios developed by
+[Quantopian Inc](https://www.quantopian.com). It works well with the
+[Zipline](https://www.zipline.io/) open source backtesting library.
+Quantopian also offers a [fully managed service for professionals](https://factset.quantopian.com) 
+that includes Zipline, Alphalens, Pyfolio, FactSet data, and more.
+
+At the core of pyfolio is a so-called tear sheet that consists of
+various individual plots that provide a comprehensive image of the
+performance of a trading algorithm. Here's an example of a simple tear
+sheet analyzing a strategy:
+
+![simple tear 0](https://github.com/quantopian/pyfolio/raw/master/docs/simple_tear_0.png "Example tear sheet created from a Zipline algo")
+![simple tear 1](https://github.com/quantopian/pyfolio/raw/master/docs/simple_tear_1.png "Example tear sheet created from a Zipline algo")
+
+Also see [slides of a talk about
+pyfolio](https://nbviewer.jupyter.org/format/slides/github/quantopian/pyfolio/blob/master/pyfolio/examples/pyfolio_talk_slides.ipynb#/).
+
+## Installation
+
+To install pyfolio, run:
+
+```bash
+pip install pyfolio
+```
+
+#### Development
+
+For development, you may want to use a [virtual environment](https://docs.python-guide.org/en/latest/dev/virtualenvs/) to avoid dependency conflicts between pyfolio and other Python projects you have. To get set up with a virtual env, run:
+```bash
+mkvirtualenv pyfolio
+```
+
+Next, clone this git repository and run `python setup.py develop`
+and edit the library files directly.
+
+#### Matplotlib on OSX
+
+If you are on OSX and using a non-framework build of Python, you may need to set your backend:
+``` bash
+echo "backend: TkAgg" > ~/.matplotlib/matplotlibrc
+```
+
+## Usage
+
+A good way to get started is to run the pyfolio examples in
+a [Jupyter notebook](https://jupyter.org/). To do this, you first want to
+start a Jupyter notebook server:
+
+```bash
+jupyter notebook
+```
+
+From the notebook list page, navigate to the pyfolio examples directory
+and open a notebook. Execute the code in a notebook cell by clicking on it
+and hitting Shift+Enter.
+
+
+## Questions?
+
+If you find a bug, feel free to [open an issue](https://github.com/quantopian/pyfolio/issues) in this repository.
+
+You can also join our [mailing list](https://groups.google.com/forum/#!forum/pyfolio) or
+our [Gitter channel](https://gitter.im/quantopian/pyfolio).
+
+## Support
+
+Please [open an issue](https://github.com/quantopian/pyfolio/issues/new) for support.
+
+## Contributing
+
+If you'd like to contribute, a great place to look is the [issues marked with help-wanted](https://github.com/quantopian/pyfolio/issues?q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22).
+
+For a list of core developers and outside collaborators, see [the GitHub contributors list](https://github.com/quantopian/pyfolio/graphs/contributors).

pyfolio/source/WHATSNEW.md

@@ -0,0 +1,310 @@
+# What's New
+
+These are new features and improvements of note in each release.
+
+## v0.9.0 (Aug 1st, 2018)
+
+### New features
+
+ - Previously, `pyfolio` has required a benchmark, usually the U.S. market
+   returns `SPY`. In order to provide support for international equities and
+   alternative data sets, `pyfolio` is now completely independent of benchmarks.
+   If a benchmark is passed, all benchmark-related analyses will be performed;
+   if not, they will simply be skipped. By [George Ho](https://github.com/eigenfoo)
+  - Performance attribution tearsheet [PR441](https://github.com/quantopian/pyfolio/pull/441), [PR433](https://github.com/quantopian/pyfolio/pull/433), [PR442](https://github.com/quantopian/pyfolio/pull/442). By [Vikram Narayan](https://github.com/vikram-narayan).
+  - Improved implementation of `get_turnover` [PR332](https://github.com/quantopian/pyfolio/pull/432). By [Gus Gordon](https://github.com/gusgordon).
+  - Users can now pass in extra rows (as a dict or OrderedDict) to display in the perf_stats table [PR445](https://github.com/quantopian/pyfolio/pull/445). By [Gus Gordon](https://github.com/gusgordon).
+
+### Maintenance
+
+ - Many features have been more extensively troubleshooted, maintained and
+   tested. By [Ana Ruelas](https://github.com/ahgnaw) and [Vikram
+   Narayan](https://github.com/vikram-narayan).
+ - Various fixes to support pandas versions >= 0.18.1 [PR443](https://github.com/quantopian/pyfolio/pull/443). By [Andrew Daniels](https://github.com/yankees714).
+
+## v0.8.0 (Aug 23rd, 2017)
+
+This is a major release from `0.7.0`, and all users are recommended to upgrade.
+
+### New features
+
+ - Risk tear sheet: added a new tear sheet to analyze risk exposures to common
+   factors (e.g. mean reversion and momentum), sector (e.g. Morningstar
+   sectors), market cap and illiquid stocks. By [George
+   Ho](https://github.com/eigenfoo).
+ - Simple tear sheet: added a new tear sheet that presents only the most
+   important plots in the full tear sheet, for a quick general overview of a
+   portfolio's performance. By [George Ho](https://github.com/eigenfoo).
+ - Performance attribution: added new table to do performance attribution
+   analysis, such as the amount of returns attributable to common factors, and
+   summary statistics such as the multi-factor alpha and multi-factor Sharpe
+   ratio. By [Vikram Narayan](https://github.com/vikram-narayan).
+ - Volatility plot: added a rolling annual volatility plot to the returns tear
+   sheet. By [hkopp](https://github.com/hkopp).
+
+### Bugfixes
+
+ - Yahoo and pandas data-reader: fixed bug regarding Yahoo backend for market
+   data and pandas data-reader. By [Thomas Wiecki](https://github.com/twiecki)
+   and [Gus Gordon](https://github.com/gusgordon).
+ - `empyrical` compatibility: removed `information_ratio` to remain compatible
+   with `empyrical`. By [Thomas Wiecki](https://github.com/twiecki).
+ - Fama-French rolling multivariate regression: fixed bug where the rolling
+   Fama-French plot performed separate linear regressions instead of a
+   multivariate regression. By [George Ho](https://github.com/eigenfoo).
+ - Other minor bugfixes. By [Scott Sanderson](https://github.com/ssanderson),
+   [Jonathan Ng](https://github.com/jonathanng),
+   [SylvainDe](https://github.com/SylvainDe) and
+   [mckelvin](https://github.com/mckelvin).
+
+### Maintenance
+
+ - Documentation: updated and improved `pyfolio` documentation and example
+   Jupyter notebooks. By [George Ho](https://github.com/eigenfoo).
+ - Data loader migration: all data loaders have been migrated from `pyfolio` to
+   `empyrical`. By [James Christopher](https://github.com/jameschristopher).
+ - Improved plotting style: fixed issues with formatting and presentation of
+   plots. By [George Ho](https://github.com/eigenfoo).
+
+## v0.7.0 (Jan 28th, 2017)
+
+This is a major release from `0.6.0`, and all users are recommended to upgrade.
+
+### New features
+
+ - Adds a transaction timing plot, which gives insight into the strategies'
+   trade times.
+ - Adds a plot showing the number of longs and shorts held over time.
+ - New round trips plot selects a sample of held positions (16 by default) and
+   shows their round trips. This replaces the old round trip plot, which became
+   unreadable for strategies that traded many positions.
+ - Adds basic capability for analyzing intraday strategies. If a strategy makes
+   a large amount of transactions relative to its end-of-day positions, then
+   pyfolio will attempt to reconstruct the intraday positions, take the point of
+   peak exposure to the market during each day, and plot that data with the
+   positions tear sheet. By default pyfolio will automatically detect this, but
+   the behavior can be changed by passing either `estimate_intraday=True` or
+   `estimate_intraday=False` to the tear sheet functions ([see
+   here](https://github.com/quantopian/pyfolio/blob/master/pyfolio/tears.py#L131)).
+ - Now formats [zipline](https://github.com/quantopian/zipline) assets,
+   displaying their ticker symbol.
+ - Gross leverage is no longer required to be passed, and will now be calculated
+   from the passed positions DataFrame.
+
+### Bugfixes
+
+ - Cone plotting location is now correct.
+ - Adjust scaling of beta and Fama-French plots.
+ - Removed multiple dependencies, some of which were previously unused.
+ - Various text fixes.
+
+## v0.6.0 (Oct 17, 2016)
+
+This is a major new release from `0.5.1`. All users are recommended to upgrade.
+
+### New features
+
+* Computation of performance and risk measures has been split off into
+  [`empyrical`](https://github.com/quantopian/empyrical). This allows
+  [`Zipline`](https://zipline.io) and `pyfolio` to use the same code to
+  calculate its risk statistics. By [Ana Ruelas](https://github.com/ahgnaw) and
+  [Abhi Kalyan](https://github.com/abhijeetkalyan).
+* New multistrike cone which redraws the cone when it crossed its initial bounds
+  [PR310](https://github.com/quantopian/pyfolio/pull/310). By [Ana
+  Ruelas](https://github.com/ahgnaw) and [Abhi
+  Kalyan](https://github.com/abhijeetkalyan).
+
+### Bugfixes
+
+* Can use most recent PyMC3 now.
+* Depends on seaborn 0.7.0 or later now
+  [PR331](https://github.com/quantopian/pyfolio/pull/331).
+* Disable buggy computation of round trips per day and per month
+  [PR339](https://github.com/quantopian/pyfolio/pull/339).
+
+## v0.5.1 (June 10, 2016)
+
+This is a bugfix release from `0.5.0` with limited new functionality. All users are recommended to upgrade.
+
+### New features
+
+* OOS data is now overlaid on top of box plot
+  [PR306](https://github.com/quantopian/pyfolio/pull/306) by [Ana
+  Ruelas](https://github.com/ahgnaw)
+* New logo [PR298](https://github.com/quantopian/pyfolio/pull/298) by [Taso
+  Petridis](https://github.com/tasopetridis) and [Richard
+  Frank](https://github.com/richafrank)
+* Raw returns plot and cumulative log returns plot
+  [PR294](https://github.com/quantopian/pyfolio/pull/294) by [Thomas
+  Wiecki](https://github.com/twiecki)
+* Net exposure line to the long/short exposure plot
+  [PR301](https://github.com/quantopian/pyfolio/pull/301) by [Ana
+  Ruelas](https://github.com/ahgnaw)
+
+### Bugfixes
+
+* Fix drawdown behavior and pandas exception in tear-sheet creation
+  [PR297](https://github.com/quantopian/pyfolio/pull/297) by [Flavio
+  Duarte](https://github.com/flaviodrt)
+
+## v0.5.0 (April 21, 2016) -- Olympia
+
+This is a major release from `0.4.0` that includes many new analyses and
+features. We recommend that all users upgrade to this new version. Also update
+your dependencies, specifically, `pandas>=0.18.0`, `seaborn>=0.6.0` and
+`zipline>=0.8.4`.
+
+### New features
+
+* New capacity tear-sheet to assess how much capital can be traded on a strategy
+  [PR284](https://github.com/quantopian/pyfolio/pull/284). [Andrew
+  Campbell](https://github.com/a-campbell).
+* Bootstrap analysis to assess uncertainty in performance metrics
+  [PR261](https://github.com/quantopian/pyfolio/pull/261). [Thomas
+  Wiecki](https://github.com/twiecki)
+* Refactored round-trip analysis to be more general and have better output. Now
+  does full portfolio reconstruction to match trades
+  [PR293](https://github.com/quantopian/pyfolio/pull/293). [Thomas
+  Wiecki](https://github.com/twiecki), [Andrew
+  Campbell](https://github.com/a-campbell). See the
+  [tutorial](http://quantopian.github.io/pyfolio/round_trip_example/) for more
+  information.
+* Prettier printing of tables in notebooks
+  [PR289](https://github.com/quantopian/pyfolio/pull/289). [Thomas
+  Wiecki](https://github.com/twiecki)
+* Faster max-drawdown calculation
+  [PR281](https://github.com/quantopian/pyfolio/pull/281). [Devin
+  Stevenson](https://github.com/devinstevenson)
+* New metrics tail-ratio and common sense ratio
+  [PR276](https://github.com/quantopian/pyfolio/pull/276). [Thomas
+  Wiecki](https://github.com/twiecki)
+* Log-scaled cumulative returns plot and raw returns plot
+  [PR294](https://github.com/quantopian/pyfolio/pull/294). [Thomas
+  Wiecki](https://github.com/twiecki)
+
+### Bug fixes
+* Many depracation fixes for Pandas 0.18.0, seaborn 0.6.0, and zipline 0.8.4
+
+
+## v0.4.0 (Dec 10, 2015)
+
+This is a major release from 0.3.1 that includes new features and quite a few bug fixes. We recommend that all users upgrade to this new version.
+
+### New features
+
+* Round-trip analysis [PR210](https://github.com/quantopian/pyfolio/pull/210)
+  Andrew, Thomas
+* Improved cone to forecast returns that uses a bootstrap instead of linear
+  forecasting [PR233](https://github.com/quantopian/pyfolio/pull/233) Andrew,
+  Thomas
+* Plot max and median long/short exposures
+  [PR237](https://github.com/quantopian/pyfolio/pull/237) Andrew
+
+### Bug fixes
+
+* Sharpe ratio was calculated incorrectly
+  [PR219](https://github.com/quantopian/pyfolio/pull/219) Thomas, Justin
+* annual_return() now only computes CAGR in the correct way
+  [PR234](https://github.com/quantopian/pyfolio/pull/234) Justin
+* Cache SPY and Fama-French returns in home-directory instead of
+  install-directory [PR241](https://github.com/quantopian/pyfolio/pull/241) Joe
+* Remove data files from package
+  [PR241](https://github.com/quantopian/pyfolio/pull/241) Joe
+* Cast factor.name to str
+  [PR223](https://github.com/quantopian/pyfolio/pull/223) Scotty
+* Test all `create_*_tear_sheet` functions in all configurations
+  [PR247](https://github.com/quantopian/pyfolio/pull/247) Thomas
+
+
+## v0.3.1 (Nov 12, 2015)
+
+This is a minor release from 0.3 that includes mostly bugfixes but also some new features. We recommend that all users upgrade to this new version.
+
+### New features
+
+* Add Information Ratio [PR194](https://github.com/quantopian/pyfolio/pull/194)
+  by @MridulS
+* Bayesian tear-sheet now accepts 'Fama-French' option to do Bayesian
+  multivariate regression against Fama-French risk factors
+  [PR200](https://github.com/quantopian/pyfolio/pull/200) by Shane Bussman
+* Plotting of monthly returns
+  [PR195](https://github.com/quantopian/pyfolio/pull/195)
+
+### Bug fixes
+
+* `pos.get_percent_alloc` was not handling short allocations correctly
+  [PR201](https://github.com/quantopian/pyfolio/pull/201)
+* UTC bug with cached Fama-French factors
+  [commit](https://github.com/quantopian/pyfolio/commit/709553a55b5df7c908d17f443cb17b51854a65be)
+* Sector map was not being passed from `create_returns_tearsheet`
+  [commit](https://github.com/quantopian/pyfolio/commit/894b753e365f9cb4861ffca2ef214c5a64b2bef4)
+* New sector mapping feature was not Python 3 compatible
+  [PR201](https://github.com/quantopian/pyfolio/pull/201)
+
+
+### Maintenance
+
+* We now depend on pandas-datareader as the yahoo finance loaders from pandas
+  will be deprecated [PR181](https://github.com/quantopian/pyfolio/pull/181) by
+  @tswrightsandpointe
+
+### Contributors
+
+Besiders the core developers, we have seen an increase in outside contributions
+which we greatly appreciate. Specifically, these people contributed to this
+release:
+
+* Shane Bussman
+* @MridulS
+* @YihaoLu
+* @jkrauss82
+* @tswrightsandpointe
+* @cgdeboer
+
+
+## v0.3 (Oct 23, 2015)
+
+This is a major release from 0.2 that includes many exciting new features. We
+recommend that all users upgrade to this new version.
+
+### New features
+
+* Sector exposures: sum positions by sector given a dictionary or series of
+  symbol to sector mappings
+  [PR166](https://github.com/quantopian/pyfolio/pull/166)
+* Ability to make cones with multiple shades stdev regions
+  [PR168](https://github.com/quantopian/pyfolio/pull/168)
+* Slippage sweep: See how an algorithm performs with various levels of slippage
+  [PR170](https://github.com/quantopian/pyfolio/pull/170)
+* Stochastic volatility model in Bayesian tear sheet
+  [PR174](https://github.com/quantopian/pyfolio/pull/174)
+* Ability to suppress display of position information
+  [PR177](https://github.com/quantopian/pyfolio/pull/177)
+
+### Bug fixes
+
+* Various fixes to make pyfolio pandas 0.17 compatible
+
+## v0.2 (Oct 16, 2015)
+
+This is a major release from 0.1 that includes mainly bugfixes and refactorings
+but also some new features. We recommend that all users upgrade to this new
+version.
+
+### New features
+
+* Volatility matched cumulative returns plot
+  [PR126](https://github.com/quantopian/pyfolio/pull/126).
+* Allow for different periodicity (annualization factors) in the annual_()
+  methods [PR164](https://github.com/quantopian/pyfolio/pull/164).
+* Users can supply their own interesting periods
+  [PR163](https://github.com/quantopian/pyfolio/pull/163).
+* Ability to weight a portfolio of holdings by a metric valued
+  [PR161](https://github.com/quantopian/pyfolio/pull/161).
+
+### Bug fixes
+
+* Fix drawdown overlaps [PR150](https://github.com/quantopian/pyfolio/pull/150).
+* Monthly returns distribution should not stack by year
+  [PR162](https://github.com/quantopian/pyfolio/pull/162).
+* Fix gross leverage [PR147](https://github.com/quantopian/pyfolio/pull/147)

pyfolio/source/__init__.py

@@ -0,0 +1,4 @@
+# -*- coding: utf-8 -*-
+"""
+pyfolio Project Package Initialization File
+"""

pyfolio/source/build_and_deploy_docs.sh

@@ -0,0 +1,7 @@
+#!/bin/bash
+
+pushd docs
+bash convert_nbs_to_md.sh
+popd
+mkdocs build --clean
+mkdocs gh-deploy

pyfolio/source/conda/bld.bat

@@ -0,0 +1,8 @@
+"%PYTHON%" setup.py install
+if errorlevel 1 exit 1
+
+:: Add more build steps here, if they are necessary.
+
+:: See
+:: http://docs.continuum.io/conda/build.html
+:: for a list of environment variables that are set during the build process.

pyfolio/source/conda/build.sh

@@ -0,0 +1,9 @@
+#!/bin/bash
+
+$PYTHON setup.py install --single-version-externally-managed --record=record.txt
+
+# Add more build steps here, if they are necessary.
+
+# See
+# http://docs.continuum.io/conda/build.html
+# for a list of environment variables that are set during the build process.

pyfolio/source/conda/meta.yaml

@@ -0,0 +1,43 @@
+package:
+  name: pyfolio
+  version: "0.6.0"
+
+source:
+  fn: pyfolio-0.6.0.tar.gz
+  url: https://pypi.python.org/packages/74/b6/bd9064f071ab71312256dc0dcf792440f2e41a66f6736bd2aa90ba965fb6/pyfolio-0.6.0.tar.gz#md5=f3f02df1c1b77209eb33c64e34f00031
+  md5: f3f02df1c1b77209eb33c64e34f00031
+
+build:
+  noarch_python: True
+
+requirements:
+  build:
+    - python
+    - setuptools
+
+  run:
+    - python
+    - matplotlib >=1.4.0
+    - numpy >=1.9.1
+    - pandas >=0.18.0
+    - pytz >=2014.10
+    - scipy >=0.14.0
+    - seaborn >=0.6.0
+    - pandas-datareader >=0.2
+    - ipython
+    - empyrical >=0.2.1
+
+test:
+  # Python imports
+  imports:
+    - pyfolio
+    - pyfolio.tests
+
+  #commands:
+  #  - nosetests  # You can put test commands to be run here.  Use this to test that the
+  #  # entry points work.
+
+about:
+  home: http://quantopian.github.io/pyfolio/
+  license: Apache Software License
+  summary: 'pyfolio is a Python library for performance and risk analysis of financial portfolios'

pyfolio/source/docs/convert_nbs_to_md.sh

@@ -0,0 +1,10 @@
+#!/bin/bash
+NBDIR=notebooks
+
+for fullfile in $NBDIR/*.ipynb; do
+    echo "Processing $fullfile file..";
+    filename=$(basename "$fullfile")
+    extension="${filename##*.}"
+    filename="${filename%.*}"
+    jupyter nbconvert $fullfile --to markdown --output $filename
+done

pyfolio/source/docs/extra.css

@@ -0,0 +1,4 @@
+img[title="pyfolio"] {
+  background-color: transparent !important;
+  border: 0 !important;
+}

pyfolio/source/docs/index.md

@@ -0,0 +1,81 @@
+![pyfolio](https://media.quantopian.com/logos/open_source/pyfolio-logo-03.png "pyfolio")
+
+# pyfolio
+
+[![Join the chat at https://gitter.im/quantopian/pyfolio](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/quantopian/pyfolio?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
+[![build status](https://travis-ci.org/quantopian/pyfolio.png?branch=master)](https://travis-ci.org/quantopian/pyfolio)
+
+pyfolio is a Python library for performance and risk analysis of
+financial portfolios developed by
+[Quantopian Inc](https://www.quantopian.com). It works well with the
+[Zipline](https://www.zipline.io/) open source backtesting library.
+Quantopian also offers a [fully managed service for professionals](https://factset.quantopian.com) 
+that includes Zipline, Alphalens, Pyfolio, FactSet data, and more.
+
+At the core of pyfolio is a so-called tear sheet that consists of
+various individual plots that provide a comprehensive image of the
+performance of a trading algorithm. Here's an example of a simple tear
+sheet analyzing a strategy:
+
+![simple tear 0](https://github.com/quantopian/pyfolio/raw/master/docs/simple_tear_0.png "Example tear sheet created from a Zipline algo")
+![simple tear 1](https://github.com/quantopian/pyfolio/raw/master/docs/simple_tear_1.png "Example tear sheet created from a Zipline algo")
+
+Also see [slides of a talk about
+pyfolio](https://nbviewer.jupyter.org/format/slides/github/quantopian/pyfolio/blob/master/pyfolio/examples/pyfolio_talk_slides.ipynb#/).
+
+## Installation
+
+To install pyfolio, run:
+
+```bash
+pip install pyfolio
+```
+
+#### Development
+
+For development, you may want to use a [virtual environment](https://docs.python-guide.org/en/latest/dev/virtualenvs/) to avoid dependency conflicts between pyfolio and other Python projects you have. To get set up with a virtual env, run:
+```bash
+mkvirtualenv pyfolio
+```
+
+Next, clone this git repository and run `python setup.py develop`
+and edit the library files directly.
+
+#### Matplotlib on OSX
+
+If you are on OSX and using a non-framework build of Python, you may need to set your backend:
+``` bash
+echo "backend: TkAgg" > ~/.matplotlib/matplotlibrc
+```
+
+## Usage
+
+A good way to get started is to run the pyfolio examples in
+a [Jupyter notebook](https://jupyter.org/). To do this, you first want to
+start a Jupyter notebook server:
+
+```bash
+jupyter notebook
+```
+
+From the notebook list page, navigate to the pyfolio examples directory
+and open a notebook. Execute the code in a notebook cell by clicking on it
+and hitting Shift+Enter.
+
+
+## Questions?
+
+If you find a bug, feel free to [open an issue](https://github.com/quantopian/pyfolio/issues) in this repository.
+
+You can also join our [mailing list](https://groups.google.com/forum/#!forum/pyfolio) or
+our [Gitter channel](https://gitter.im/quantopian/pyfolio).
+
+## Support
+
+Please [open an issue](https://github.com/quantopian/pyfolio/issues/new) for support.
+
+## Contributing
+
+If you'd like to contribute, a great place to look is the [issues marked with help-wanted](https://github.com/quantopian/pyfolio/issues?q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22).
+
+For a list of core developers and outside collaborators, see [the GitHub contributors list](https://github.com/quantopian/pyfolio/graphs/contributors).

pyfolio/source/docs/whatsnew.md

@@ -0,0 +1,310 @@
+# What's New
+
+These are new features and improvements of note in each release.
+
+## v0.9.0 (Aug 1st, 2018)
+
+### New features
+
+ - Previously, `pyfolio` has required a benchmark, usually the U.S. market
+   returns `SPY`. In order to provide support for international equities and
+   alternative data sets, `pyfolio` is now completely independent of benchmarks.
+   If a benchmark is passed, all benchmark-related analyses will be performed;
+   if not, they will simply be skipped. By [George Ho](https://github.com/eigenfoo)
+  - Performance attribution tearsheet [PR441](https://github.com/quantopian/pyfolio/pull/441), [PR433](https://github.com/quantopian/pyfolio/pull/433), [PR442](https://github.com/quantopian/pyfolio/pull/442). By [Vikram Narayan](https://github.com/vikram-narayan).
+  - Improved implementation of `get_turnover` [PR332](https://github.com/quantopian/pyfolio/pull/432). By [Gus Gordon](https://github.com/gusgordon).
+  - Users can now pass in extra rows (as a dict or OrderedDict) to display in the perf_stats table [PR445](https://github.com/quantopian/pyfolio/pull/445). By [Gus Gordon](https://github.com/gusgordon).
+
+### Maintenance
+
+ - Many features have been more extensively troubleshooted, maintained and
+   tested. By [Ana Ruelas](https://github.com/ahgnaw) and [Vikram
+   Narayan](https://github.com/vikram-narayan).
+ - Various fixes to support pandas versions >= 0.18.1 [PR443](https://github.com/quantopian/pyfolio/pull/443). By [Andrew Daniels](https://github.com/yankees714).
+
+## v0.8.0 (Aug 23rd, 2017)
+
+This is a major release from `0.7.0`, and all users are recommended to upgrade.
+
+### New features
+
+ - Risk tear sheet: added a new tear sheet to analyze risk exposures to common
+   factors (e.g. mean reversion and momentum), sector (e.g. Morningstar
+   sectors), market cap and illiquid stocks. By [George
+   Ho](https://github.com/eigenfoo).
+ - Simple tear sheet: added a new tear sheet that presents only the most
+   important plots in the full tear sheet, for a quick general overview of a
+   portfolio's performance. By [George Ho](https://github.com/eigenfoo).
+ - Performance attribution: added new table to do performance attribution
+   analysis, such as the amount of returns attributable to common factors, and
+   summary statistics such as the multi-factor alpha and multi-factor Sharpe
+   ratio. By [Vikram Narayan](https://github.com/vikram-narayan).
+ - Volatility plot: added a rolling annual volatility plot to the returns tear
+   sheet. By [hkopp](https://github.com/hkopp).
+
+### Bugfixes
+
+ - Yahoo and pandas data-reader: fixed bug regarding Yahoo backend for market
+   data and pandas data-reader. By [Thomas Wiecki](https://github.com/twiecki)
+   and [Gus Gordon](https://github.com/gusgordon).
+ - `empyrical` compatibility: removed `information_ratio` to remain compatible
+   with `empyrical`. By [Thomas Wiecki](https://github.com/twiecki).
+ - Fama-French rolling multivariate regression: fixed bug where the rolling
+   Fama-French plot performed separate linear regressions instead of a
+   multivariate regression. By [George Ho](https://github.com/eigenfoo).
+ - Other minor bugfixes. By [Scott Sanderson](https://github.com/ssanderson),
+   [Jonathan Ng](https://github.com/jonathanng),
+   [SylvainDe](https://github.com/SylvainDe) and
+   [mckelvin](https://github.com/mckelvin).
+
+### Maintenance
+
+ - Documentation: updated and improved `pyfolio` documentation and example
+   Jupyter notebooks. By [George Ho](https://github.com/eigenfoo).
+ - Data loader migration: all data loaders have been migrated from `pyfolio` to
+   `empyrical`. By [James Christopher](https://github.com/jameschristopher).
+ - Improved plotting style: fixed issues with formatting and presentation of
+   plots. By [George Ho](https://github.com/eigenfoo).
+
+## v0.7.0 (Jan 28th, 2017)
+
+This is a major release from `0.6.0`, and all users are recommended to upgrade.
+
+### New features
+
+ - Adds a transaction timing plot, which gives insight into the strategies'
+   trade times.
+ - Adds a plot showing the number of longs and shorts held over time.
+ - New round trips plot selects a sample of held positions (16 by default) and
+   shows their round trips. This replaces the old round trip plot, which became
+   unreadable for strategies that traded many positions.
+ - Adds basic capability for analyzing intraday strategies. If a strategy makes
+   a large amount of transactions relative to its end-of-day positions, then
+   pyfolio will attempt to reconstruct the intraday positions, take the point of
+   peak exposure to the market during each day, and plot that data with the
+   positions tear sheet. By default pyfolio will automatically detect this, but
+   the behavior can be changed by passing either `estimate_intraday=True` or
+   `estimate_intraday=False` to the tear sheet functions ([see
+   here](https://github.com/quantopian/pyfolio/blob/master/pyfolio/tears.py#L131)).
+ - Now formats [zipline](https://github.com/quantopian/zipline) assets,
+   displaying their ticker symbol.
+ - Gross leverage is no longer required to be passed, and will now be calculated
+   from the passed positions DataFrame.
+
+### Bugfixes
+
+ - Cone plotting location is now correct.
+ - Adjust scaling of beta and Fama-French plots.
+ - Removed multiple dependencies, some of which were previously unused.
+ - Various text fixes.
+
+## v0.6.0 (Oct 17, 2016)
+
+This is a major new release from `0.5.1`. All users are recommended to upgrade.
+
+### New features
+
+* Computation of performance and risk measures has been split off into
+  [`empyrical`](https://github.com/quantopian/empyrical). This allows
+  [`Zipline`](https://zipline.io) and `pyfolio` to use the same code to
+  calculate its risk statistics. By [Ana Ruelas](https://github.com/ahgnaw) and
+  [Abhi Kalyan](https://github.com/abhijeetkalyan).
+* New multistrike cone which redraws the cone when it crossed its initial bounds
+  [PR310](https://github.com/quantopian/pyfolio/pull/310). By [Ana
+  Ruelas](https://github.com/ahgnaw) and [Abhi
+  Kalyan](https://github.com/abhijeetkalyan).
+
+### Bugfixes
+
+* Can use most recent PyMC3 now.
+* Depends on seaborn 0.7.0 or later now
+  [PR331](https://github.com/quantopian/pyfolio/pull/331).
+* Disable buggy computation of round trips per day and per month
+  [PR339](https://github.com/quantopian/pyfolio/pull/339).
+
+## v0.5.1 (June 10, 2016)
+
+This is a bugfix release from `0.5.0` with limited new functionality. All users are recommended to upgrade.
+
+### New features
+
+* OOS data is now overlaid on top of box plot
+  [PR306](https://github.com/quantopian/pyfolio/pull/306) by [Ana
+  Ruelas](https://github.com/ahgnaw)
+* New logo [PR298](https://github.com/quantopian/pyfolio/pull/298) by [Taso
+  Petridis](https://github.com/tasopetridis) and [Richard
+  Frank](https://github.com/richafrank)
+* Raw returns plot and cumulative log returns plot
+  [PR294](https://github.com/quantopian/pyfolio/pull/294) by [Thomas
+  Wiecki](https://github.com/twiecki)
+* Net exposure line to the long/short exposure plot
+  [PR301](https://github.com/quantopian/pyfolio/pull/301) by [Ana
+  Ruelas](https://github.com/ahgnaw)
+
+### Bugfixes
+
+* Fix drawdown behavior and pandas exception in tear-sheet creation
+  [PR297](https://github.com/quantopian/pyfolio/pull/297) by [Flavio
+  Duarte](https://github.com/flaviodrt)
+
+## v0.5.0 (April 21, 2016) -- Olympia
+
+This is a major release from `0.4.0` that includes many new analyses and
+features. We recommend that all users upgrade to this new version. Also update
+your dependencies, specifically, `pandas>=0.18.0`, `seaborn>=0.6.0` and
+`zipline>=0.8.4`.
+
+### New features
+
+* New capacity tear-sheet to assess how much capital can be traded on a strategy
+  [PR284](https://github.com/quantopian/pyfolio/pull/284). [Andrew
+  Campbell](https://github.com/a-campbell).
+* Bootstrap analysis to assess uncertainty in performance metrics
+  [PR261](https://github.com/quantopian/pyfolio/pull/261). [Thomas
+  Wiecki](https://github.com/twiecki)
+* Refactored round-trip analysis to be more general and have better output. Now
+  does full portfolio reconstruction to match trades
+  [PR293](https://github.com/quantopian/pyfolio/pull/293). [Thomas
+  Wiecki](https://github.com/twiecki), [Andrew
+  Campbell](https://github.com/a-campbell). See the
+  [tutorial](http://quantopian.github.io/pyfolio/round_trip_example/) for more
+  information.
+* Prettier printing of tables in notebooks
+  [PR289](https://github.com/quantopian/pyfolio/pull/289). [Thomas
+  Wiecki](https://github.com/twiecki)
+* Faster max-drawdown calculation
+  [PR281](https://github.com/quantopian/pyfolio/pull/281). [Devin
+  Stevenson](https://github.com/devinstevenson)
+* New metrics tail-ratio and common sense ratio
+  [PR276](https://github.com/quantopian/pyfolio/pull/276). [Thomas
+  Wiecki](https://github.com/twiecki)
+* Log-scaled cumulative returns plot and raw returns plot
+  [PR294](https://github.com/quantopian/pyfolio/pull/294). [Thomas
+  Wiecki](https://github.com/twiecki)
+
+### Bug fixes
+* Many depracation fixes for Pandas 0.18.0, seaborn 0.6.0, and zipline 0.8.4
+
+
+## v0.4.0 (Dec 10, 2015)
+
+This is a major release from 0.3.1 that includes new features and quite a few bug fixes. We recommend that all users upgrade to this new version.
+
+### New features
+
+* Round-trip analysis [PR210](https://github.com/quantopian/pyfolio/pull/210)
+  Andrew, Thomas
+* Improved cone to forecast returns that uses a bootstrap instead of linear
+  forecasting [PR233](https://github.com/quantopian/pyfolio/pull/233) Andrew,
+  Thomas
+* Plot max and median long/short exposures
+  [PR237](https://github.com/quantopian/pyfolio/pull/237) Andrew
+
+### Bug fixes
+
+* Sharpe ratio was calculated incorrectly
+  [PR219](https://github.com/quantopian/pyfolio/pull/219) Thomas, Justin
+* annual_return() now only computes CAGR in the correct way
+  [PR234](https://github.com/quantopian/pyfolio/pull/234) Justin
+* Cache SPY and Fama-French returns in home-directory instead of
+  install-directory [PR241](https://github.com/quantopian/pyfolio/pull/241) Joe
+* Remove data files from package
+  [PR241](https://github.com/quantopian/pyfolio/pull/241) Joe
+* Cast factor.name to str
+  [PR223](https://github.com/quantopian/pyfolio/pull/223) Scotty
+* Test all `create_*_tear_sheet` functions in all configurations
+  [PR247](https://github.com/quantopian/pyfolio/pull/247) Thomas
+
+
+## v0.3.1 (Nov 12, 2015)
+
+This is a minor release from 0.3 that includes mostly bugfixes but also some new features. We recommend that all users upgrade to this new version.
+
+### New features
+
+* Add Information Ratio [PR194](https://github.com/quantopian/pyfolio/pull/194)
+  by @MridulS
+* Bayesian tear-sheet now accepts 'Fama-French' option to do Bayesian
+  multivariate regression against Fama-French risk factors
+  [PR200](https://github.com/quantopian/pyfolio/pull/200) by Shane Bussman
+* Plotting of monthly returns
+  [PR195](https://github.com/quantopian/pyfolio/pull/195)
+
+### Bug fixes
+
+* `pos.get_percent_alloc` was not handling short allocations correctly
+  [PR201](https://github.com/quantopian/pyfolio/pull/201)
+* UTC bug with cached Fama-French factors
+  [commit](https://github.com/quantopian/pyfolio/commit/709553a55b5df7c908d17f443cb17b51854a65be)
+* Sector map was not being passed from `create_returns_tearsheet`
+  [commit](https://github.com/quantopian/pyfolio/commit/894b753e365f9cb4861ffca2ef214c5a64b2bef4)
+* New sector mapping feature was not Python 3 compatible
+  [PR201](https://github.com/quantopian/pyfolio/pull/201)
+
+
+### Maintenance
+
+* We now depend on pandas-datareader as the yahoo finance loaders from pandas
+  will be deprecated [PR181](https://github.com/quantopian/pyfolio/pull/181) by
+  @tswrightsandpointe
+
+### Contributors
+
+Besiders the core developers, we have seen an increase in outside contributions
+which we greatly appreciate. Specifically, these people contributed to this
+release:
+
+* Shane Bussman
+* @MridulS
+* @YihaoLu
+* @jkrauss82
+* @tswrightsandpointe
+* @cgdeboer
+
+
+## v0.3 (Oct 23, 2015)
+
+This is a major release from 0.2 that includes many exciting new features. We
+recommend that all users upgrade to this new version.
+
+### New features
+
+* Sector exposures: sum positions by sector given a dictionary or series of
+  symbol to sector mappings
+  [PR166](https://github.com/quantopian/pyfolio/pull/166)
+* Ability to make cones with multiple shades stdev regions
+  [PR168](https://github.com/quantopian/pyfolio/pull/168)
+* Slippage sweep: See how an algorithm performs with various levels of slippage
+  [PR170](https://github.com/quantopian/pyfolio/pull/170)
+* Stochastic volatility model in Bayesian tear sheet
+  [PR174](https://github.com/quantopian/pyfolio/pull/174)
+* Ability to suppress display of position information
+  [PR177](https://github.com/quantopian/pyfolio/pull/177)
+
+### Bug fixes
+
+* Various fixes to make pyfolio pandas 0.17 compatible
+
+## v0.2 (Oct 16, 2015)
+
+This is a major release from 0.1 that includes mainly bugfixes and refactorings
+but also some new features. We recommend that all users upgrade to this new
+version.
+
+### New features
+
+* Volatility matched cumulative returns plot
+  [PR126](https://github.com/quantopian/pyfolio/pull/126).
+* Allow for different periodicity (annualization factors) in the annual_()
+  methods [PR164](https://github.com/quantopian/pyfolio/pull/164).
+* Users can supply their own interesting periods
+  [PR163](https://github.com/quantopian/pyfolio/pull/163).
+* Ability to weight a portfolio of holdings by a metric valued
+  [PR161](https://github.com/quantopian/pyfolio/pull/161).
+
+### Bug fixes
+
+* Fix drawdown overlaps [PR150](https://github.com/quantopian/pyfolio/pull/150).
+* Monthly returns distribution should not stack by year
+  [PR162](https://github.com/quantopian/pyfolio/pull/162).
+* Fix gross leverage [PR147](https://github.com/quantopian/pyfolio/pull/147)

pyfolio/source/mkdocs.yml

@@ -0,0 +1,15 @@
+site_name: pyfolio
+repo_url: https://github.com/quantopian/pyfolio
+site_author: Quantopian Inc.
+
+pages:
+- Overview: 'index.md'
+- Releases: 'whatsnew.md'
+- Tutorial:
+  - 'Single stock': 'notebooks/single_stock_example.md'
+  - 'Zipline algorithm': 'notebooks/zipline_algo_example.md'
+  - 'Sector analysis': 'notebooks/sector_mappings_example.md'
+  - 'Round trip analysis': 'notebooks/round_trip_tear_sheet_example.md'
+  - 'Slippage analysis': 'notebooks/slippage_example.md'
+
+extra_css: [extra.css]

pyfolio/source/pyfolio/__init__.py

@@ -0,0 +1,19 @@
+from . import utils
+from . import timeseries
+from . import pos
+from . import txn
+from . import interesting_periods
+from . import capacity
+from . import round_trips
+from . import perf_attrib
+
+from .tears import *  # noqa
+from .plotting import *  # noqa
+from ._version import get_versions
+
+__version__ = get_versions()['version']
+del get_versions
+
+__all__ = ['utils', 'timeseries', 'pos', 'txn',
+           'interesting_periods', 'capacity', 'round_trips',
+           'perf_attrib']

pyfolio/source/pyfolio/_seaborn.py

@@ -0,0 +1,18 @@
+"""Wrapper module around seaborn to suppress warnings on import.
+
+This should be removed when seaborn stops raising:
+
+UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle;
+please use the latter.
+"""
+import warnings
+
+
+with warnings.catch_warnings():
+    warnings.filterwarnings(
+        'ignore',
+        'axes.color_cycle is deprecated',
+        UserWarning,
+        'matplotlib',
+    )
+    from seaborn import *  # noqa