Forward–Backward SDEs in Algorithmic Trading: BSDEs, Deep BSDE Solvers, and Risk-Sensitive Control With Python (Computational Mathematics Library) - Image 1

£59.82

Majosta Forward–Backward SDEs in Algorithmic Trading: BSDEs, Deep BSDE Solvers, and Risk-Sensitive Control With Python (Computational Mathematics Library)

Name: Forward-Backward SDEs in Algorithmic Trading: BSDEs, Deep BSDE Solvers, and Risk-Sensitive Control With Python (Computational Mathematics Library)
Brand: Majosta
Price: 59.82 GBP
Availability: InStock

E-business

Price History & Forecast

Last 5 days • 5 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 5 days • 1 price levels

Days at Price

Price Analysis

Most common price: £60 (5 days, 100.0%)

Price range: £60 - £60

Price levels: 1 different prices over 5 days

Description

Harness modern stochastic control and deep learning to design, price, and manage algorithmic trading strategies in continuous time. This book takes you from first principles of stochastic price dynamics through fully coupled forward backward stochastic differential equations and on to state of the art deep BSDE solvers, always in the concrete setting of execution, market making, and portfolio problems. Every chapter is accompanied by complete Python code that reproduces all numerical examples, so you can move directly from theory to implementation. Starting from trading PnL modeled as stochastic differential equations, you will see how execution, inventory, and risk constraints naturally lead to BSDE and FBSDE formulations. You will learn how risk neutral, risk averse, and risk sensitive objectives are embedded in generators, how they connect to nonlinear expectations and entropic risk, and how they change optimal trading behavior in realistic market impact models. On the computational side, the book shows how to discretize and solve BSDEs with Monte Carlo and regression, then scales up to high dimensional settings with deep BSDE solvers implemented in Python. You will build full workflows that simulate forward dynamics, propagate backward value and costate processes, and train neural networks to approximate optimal controls in problems that are out of reach for classical PDE methods. You will learn how to: Model prices, inventory, and execution costs with controlled SDEs tailored to high frequency and institutional trading Formulate optimal execution, market making, and portfolio problems as BSDE or FBSDE systems Incorporate permanent, transient, and nonlinear market impact into continuous time trading models Encode risk neutral, mean variance, and risk sensitive objectives using quadratic and nonlinear BSDE generators Apply regression based Monte Carlo methods to solve discretized BSDEs in Python Use deep BSDE solvers for high dimensional control, including multi asset execution and multi venue trading Design risk sensitive strategies with dynamic risk constraints and robust formulations that account for model uncertainty Calibrate SDE and BSDE parameters to real market data and adapt models online in changing conditions Each chapter includes: A carefully chosen trading or risk management problem Step by step derivation of the associated BSDE or FBSDE Discussion of existence, uniqueness, and qualitative behavior of solutions A complete, well commented Python implementation with simulation and numerical results The result is a self contained path from mathematical foundations to production ready prototypes. Whether you are a quant researcher, a practitioner responsible for algorithmic execution and risk, or an advanced student in financial engineering, this book gives you a rigorous framework and executable code to develop and test sophisticated continuous time strategies driven by forward backward SDEs.

Product Specifications

Brand: Majosta
Format: paperback
ASIN: B0G6TD2JBL
Category: Books > Subjects > Business, Finance & Law > E-Commerce > E-business
Domain: Amazon UK
Release Date: 13 December 2025
Listed Since: 13 December 2025

Barcode

No barcode data available

Majosta Forward–Backward SDEs in Algorithmic Trading: BSDEs, Deep BSDE Solvers, and Risk-Sensitive Control With Python (Computational Mathematics Library)

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Quantitative Algorithm Design for FX Markets: Building High-Frequency Trading Systems With Python (The Artificial Edge: Quantitative Trading Strategies with Python)

Market Making Algorithms: 33 Comprehensive Powerful Algorithms With Full Python Code

Statistical Arbitrage in Algorithmic Trading: Stationarity, Cointegration, and Mean-Reverting Stochastic Processes With Python (Computational Mathematics Library)

Stochastic Finance with Python: Design Financial Models from Probabilistic Perspective

Diffusion Models & Score-Based Generative Modeling: Theorems, Proofs, and Python Implementations (Computational Mathematics Library)

Algorithmic Trading Hands-On Approach Using Python

Python for Algorithmic Trading Cookbook: Recipes for designing, building, and deploying algorithmic trading strategies with Python

Financial Modeling: A Backward Stochastic Differential Equations Perspective (Springer Finance)

Machine Learning for Algorithmic Trading - Second Edition: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python

Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python

Ultimate Quant Job Interview Questions Workbook: Brief Crash Courses and Real Interview Questions taking you from Beginner to Wall Street Offers (The ... Quantitative Trading Strategies with Python)

Quantitative Trading Strategies Using Python: Technical Analysis, Statistical Testing, and Machine Learning

High-Frequency Trading and Order Book Dynamics (The Artificial Edge: Quantitative Trading Strategies with Python)

Computation and Simulation for Finance: An Introduction with Python (Springer Undergraduate Texts in Mathematics and Technology)

Options Pricing with AI (The Artificial Edge: Quantitative Trading Strategies with Python)

Day Trade With AI: Theoretical foundations for discretionary, algorithmic, and diversified trading with AI

Algorithmic Pattern Recognition in Day Trading (The Artificial Edge: Quantitative Trading Strategies with Python)

Quantitative Analysis, Derivatives Modeling, and Trading Strategies: In the Presence of Counterparty Credit Risk for the Fixed-Income Market

Hands-On Machine Learning for Algorithmic Trading: Design and implement investment strategies based on smart algorithms that learn from data using Python

Quantitative Risk Management Using Python: An Essential Guide for Managing Market, Credit, and Model Risk

Analytical Finance: Volume I: The Mathematics of Equity Derivatives, Markets, Risk and Valuation: 1

Pricing and Trading Interest Rate Derivatives: A Practical Guide to Swaps

Quant_Py: Python for Quantitative Finance

Malliavin Calculus in Finance: Theory and Practice (Chapman and Hall/CRC Financial Mathematics Series)