Parallax Python SDK (strategic-mandate-parallax)

Numerical Insight Controls Execution — Official Python SDK for the Parallax quantitative valuation and Monte Carlo simulation platform by Strategic Mandate GmbH.

Installation

pip install strategic-mandate-parallax
# With full quantitative finance & visualization stack (pandas, SciPy, scikit-learn, Matplotlib):
pip install "strategic-mandate-parallax[quant]"

In Python, import the package as parallax:

import parallax
from parallax import Engine, Model, Config, Simulation, Valuation, Asset, Capex, Revenue
from parallax import viz

Architecture & Commercial Licensing (parallax-license)

Like commercial solver packages (gurobipy, blpapi), strategic-mandate-parallax on PyPI is a pure-Python thin SDK (py3-none-any.whl) and does not bundle the proprietary simulation engine binary.

• Without an Engine Runtime / License: You can freely install strategic-mandate-parallax in any environment (CI, cloud notebooks, developer laptops) to construct, inspect, type-check, and serialize models (parallax.model) and render institutional exhibits (parallax.viz).
• With a Licensed Engine Runtime (_parallax_native + license.pxl): parallax.Engine() dynamically loads the native binary runtime from ~/.parallax/runtime/ (or PARALLAX_RUNTIME_DIR / your Strategic Mandate installation) and unlocks sub-millisecond in-process Monte Carlo execution with zero-copy IEEE-754 binary memory views into numpy and pandas.

Activating Your License & Runtime

# 1. Inspect current license and native runtime status
parallax-license status

# 2. Install your cryptographic license file (.pxl)
parallax-license install-license ~/Downloads/license.pxl

# 3. Install the native engine runtime binary for your platform
parallax-license install-runtime ~/Downloads/_parallax_native.cpython-314-darwin.so

Key Capabilities

1. Typed Python Component Model (parallax.model): 92 strongly-typed component classes (Model, Config, Simulation, Valuation, Asset, Capex, Revenue, Expense, DebtFacility, EquityIssuance, GlobalVariable, DistConfig, Scenario, Trigger, TrpEvent, etc.) for programmatic and batch model generation.
2. In-Process Native Engine (parallax.Engine): Executes stochastic simulations in-process with multi-threaded parallel sampling.
3. Zero-Copy Binary Transfer to NumPy, pandas & scikit-learn: Reads IEEE-754 float64 scalar sample vectors (eng.scalar_array) and (n_paths, n_periods) time-series matrices (eng.timeseries_matrix) directly into numpy.ndarray, pandas.DataFrame, and scikit-learn (X, y) datasets (eng.sklearn_dataset).
4. Financial Statements & Schedules as Pandas DataFrames (parallax.statements): Extract Income Statement / P&L (eng.income_statement_df(), eng.pnl_df()), Balance Sheet (eng.balance_sheet_df()), Cash Flow Statement (eng.cash_flow_df()), 3-Statement Bundles (eng.financial_statements()), Debt Schedules (eng.debt_schedule_df()), Cash Waterfalls (eng.cash_waterfall_df()), Working Capital (eng.working_capital_df()), Financial Ratios (eng.ratios_df()), and Sanity Checks (eng.sanity_checks_df()) — supporting both full deterministic base-case tables (statistic="base") and Monte Carlo quantile/moment or single-path statements (statistic="p50", "p5", "mean", or path_id=0).
5. Model & Catalog Introspection (parallax.introspection): Inspect any project via interactive treeviews (model.tree() — Unicode box-drawing in terminals, collapsible Strategic Mandate HTML in Jupyter), settings tables (model.settings_df()), global variables (model.globals_df()), component inventories (model.components_df(), model.capex_df(), model.financing_df(), model.operations_df()), deep component inspection (model.inspect("Brewery_Loan")), and live engine catalog DataFrames (eng.catalog_view(), eng.mc_catalog_df(), eng.templates_df(), eng.distributions_df(), eng.processes_df(), eng.functions_df(), eng.metrics_df()).
6. Interactive Help & AI Coding Agent Guide (parallax.help_system): Built-in discovery (parallax.help(), eng.help("gbm"), parallax.help(DebtFacility), parallax.help(search="dscr")) and token-efficient LLM context generation (parallax.agent_guide(model=m, engine=eng) / m.agent_context()).
7. Strategic Mandate Corporate Visualization Suite (parallax.viz): High-DPI quantitative exhibits (plot_fan_manifold, plot_npv_distribution, plot_scenario_ranges, plot_surrogate_attribution, plot_dscr_covenant, plot_pv_waterfall) and institutional pandas exhibit tables (sm_exhibit_table) with inline SVG sparklines and quantile range bars.

Quick Example: Introspection, Financial Statements & Help

import parallax
from parallax import Engine, Model

with Engine() as eng:
    # 1. Load a template and inspect its hierarchical tree & settings
    m = Model.from_dict(eng.template_get("simple_brewery")["model"])
    print(m.tree())                      # Unicode tree in terminal; interactive HTML in Jupyter
    settings_df = m.settings_df()        # Model-level simulation, config, valuation & tax settings
    globals_df  = m.globals_df()         # Stochastic distributions, processes & constants
    loan_spec   = m.inspect("Brewery_Loan")

    # 2. Run Monte Carlo simulation and extract financial statements as DataFrames
    eng.run(m)
    pnl_base = eng.income_statement_df(drop_zero_rows=True)
    pnl_p50  = eng.income_statement_df(statistic="p50")
    bs_base  = eng.balance_sheet_df(drop_zero_rows=True)
    cf_base  = eng.cash_flow_df(drop_zero_rows=True)
    stmts    = eng.financial_statements()  # .pnl, .balance_sheet, .cash_flow

    # 3. Interactive help & AI agent context
    parallax.help("statements")
    parallax.help(search="dscr")
    agent_md = parallax.agent_guide(model=m, engine=eng)

License

Copyright © 2024–2026 Strategic Mandate GmbH. All rights reserved.
See LICENSE.txt for terms. Commercial licensing: https://nice.strategicmandate.com