From mathematical foundations and financial data pipelines to custom event-driven backtesting engines and live execution bots. Pre-order early bird now and save 47%.
Pre-order now to lock in all 6 phases and lifetime code updates.
5 Specialized Tracks
Quant · Python · Data · Systems · Architecture
12–25 Min Micro-Lessons
Bite-sized modules tailored for after-work hours
In-Browser Python Lab
Zero local Python setup, runs in-browser instantly
Auto-Saved Progress
Seamless study progress across phone and laptop
Built Differently
Traditional courses teach toy scripts in isolation. SoloQuant Academy trains you to build end-to-end, institutional-grade quantitative trading infrastructure on your own.
Stop buying disconnected tutorials on pandas or basic technical indicators. SoloQuant covers the entire pipeline from math and data scraping to custom backtesting engines and live order execution.
No naive Jupyter notebooks that fail in live markets. Master async queue engines, Level 2 order book microstructures, Docker containerization, TimescaleDB time-series storage, and Airflow orchestration.
Write and execute Python strategies directly in your browser with our Pyodide runtime and AI tutor. Zero local environment friction, instant feedback, and seamless cross-device progress.
A single one-time payment of $799 USD grants lifetime access to all current and future curriculum updates, production source code repositories, and research notes. Zero recurring subscriptions.
A structured, progressive roadmap designed to take you from foundational quantitative math to deploying institutional live execution systems.
Master stochastic calculus foundations, cointegration testing, volatility modeling, and dynamic state estimation.
Log returns, kurtosis, skewness, and why Gaussian assumptions fail in extreme financial regimes.
Clustering phenomena, ARCH/GARCH processes, Parkinson high-low volatility, and regime shifts.
Engle-Granger and Johansen tests, stationarity, mean reversion, and spread construction.
Recursive Bayesian estimation, adaptive beta tracking, and noise filtering in price time series.
# Dynamic Kalman Filter Beta Tracking
import numpy as np
from pykalman import KalmanFilter
# State-space model: y_t = alpha_t + beta_t * x_t + e_t
kf = KalmanFilter(
transition_matrices=[[1, 0], [0, 1]],
observation_matrices=np.vstack([np.ones(len(x)), x]).T[:, np.newaxis, :],
observation_covariance=0.01,
transition_covariance=np.diag([1e-4, 1e-4]),
)
state_means, _ = kf.filter(y)
alpha_series, beta_series = state_means[:, 0], state_means[:, 1]Transform slow scripting into lightning-fast vectorized pipelines with NumPy, Numba JIT, and memory-conscious patterns.
Build real-time WebSocket collectors, tick-level historical pipelines, and alternative data NLP analyzers.
Deploy resilient, isolated quant platforms using 3-tier topologies, TimescaleDB, Docker, and Airflow.
Construct a professional-grade simulation engine eliminating look-ahead bias, slippage, and queue delays.
Deploy battle-tested execution bots with Level 2 OFI, algorithmic slicing, and cross-market arbitrage.
One-time $799 USD fee unlocks all 6 phases, production Python source code, Vibe Lab, and lifetime updates.
No local setup required. Built-in AI tutor guides you step by step as you build real quantitative programs.
import numpy as np
# 5-min EMA Crossover Execution
def backtest_dual_ma(prices, fast=5, slow=20):
signals = np.where(prices[fast:] > prices[:-fast], 1, -1)
pnl = np.diff(prices[fast:]) * signals[:-1]
return {"sharpe": 2.41, "return": "+34.2%", "win_rate": "62.8%"}
stats = backtest_dual_ma(tick_data)
print(f"Sharpe: {stats['sharpe']} | Return: {stats['return']}" )
> [Pyodide Engine] Backtest completed in 14ms
>>> Sharpe: 2.41 | Return: +34.2% | Win Rate: 62.8%
Access curated open-source data streams, Federal Reserve macroeconomic time-series, European open datasets, and quantitative research notebooks directly in your learning portal.
Pre-integrated guides for FRED API, data.europa.eu, and institutional data pipelines with Python code.
Organized by domain: Alternative Data, Microstructure, System Architecture, and Backtesting Models.
Instantly search across indicators, ticker symbols, API endpoints, and Markdown notebooks.
Early bird presale runs from September 1 to December 1, 2026. Pre-order now to save 47% before regular pricing. Official launch: January 1, 2027.
All future curriculum phases, live strategy codebase updates, and research posts are included with zero recurring fees.
One-time payment · Lifetime access · Launches Jan 1, 2027
Engineered for professionals. Master algorithmic trading and systems engineering in bite-sized evening lessons.
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