Trading Algorithms

Calculating Profits and Losses for Algorithmic Trading Strategies: A Short Guide ArXiv ID: 2411.14068 “View on arXiv” Authors: Unknown Abstract We present a series of equations that track the total realized and unrealized profits and losses at any time, incorporating the spread. The resulting formalism is ideally suited to evaluate the performance of trading model algorithms. Keywords: realized profit/loss, unrealized profit/loss, spread, trading algorithms, performance evaluation, Trading Strategies Complexity vs Empirical Score Math Complexity: 3.5/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper presents a series of algebraic equations to formalize profit and loss calculations, which is moderately math-intensive but lacks the deep stochastic calculus or advanced statistics often seen in quant finance research. Empirically, it is a theoretical guide with illustrative examples but no backtested performance, real-world datasets, or implementation code. flowchart TD A["Research Goal: Develop<br>algorithms to track<br>realized & unrealized PnL"] --> B["Key Methodology: Mathematical Formalism"] B --> C["Data/Inputs: Trades, Prices, Spread"] C --> D["Computational Process:<br>Equations for PnL Calculation"] D --> E["Key Findings: Robust<br>Performance Evaluation"]

Optimizing Broker Performance Evaluation through Intraday Modeling of Execution Cost ArXiv ID: 2405.18936 “View on arXiv” Authors: Unknown Abstract Minimizing execution costs for large orders is a fundamental challenge in finance. Firms often depend on brokers to manage their trades due to limited internal resources for optimizing trading strategies. This paper presents a methodology for evaluating the effectiveness of broker execution algorithms using trading data. We focus on two primary cost components: a linear cost that quantifies short-term execution quality and a quadratic cost associated with the price impact of trades. Using a model with transient price impact, we derive analytical formulas for estimating these costs. Furthermore, we enhance estimation accuracy by introducing novel methods such as weighting price changes based on their expected impact content. Our results demonstrate substantial improvements in estimating both linear and impact costs, providing a robust and efficient framework for selecting the most cost-effective brokers. ...

Exploiting Distributional Value Functions for Financial Market Valuation, Enhanced Feature Creation and Improvement of Trading Algorithms ArXiv ID: 2405.11686 “View on arXiv” Authors: Unknown Abstract While research of reinforcement learning applied to financial markets predominantly concentrates on finding optimal behaviours, it is worth to realize that the reinforcement learning returns $G_t$ and state value functions themselves are of interest and play a pivotal role in the evaluation of assets. Instead of focussing on the more complex task of finding optimal decision rules, this paper studies and applies the power of distributional state value functions in the context of financial market valuation and machine learning based trading algorithms. Accurate and trustworthy estimates of the distributions of $G_t$ provide a competitive edge leading to better informed decisions and more optimal behaviour. Herein, ideas from predictive knowledge and deep reinforcement learning are combined to introduce a novel family of models called CDG-Model, resulting in a highly flexible framework and intuitive approach with minimal assumptions regarding underlying distributions. The models allow seamless integration of typical financial modelling pitfalls like transaction costs, slippage and other possible costs or benefits into the model calculation. They can be applied to any kind of trading strategy or asset class. The frameworks introduced provide concrete business value through their potential in market valuation of single assets and portfolios, in the comparison of strategies as well as in the improvement of market timing. They can positively impact the performance and enhance the learning process of existing or new trading algorithms. They are of interest from a scientific point-of-view and open up multiple areas of future research. Initial implementations and tests were performed on real market data. While the results are promising, applying a robust statistical framework to evaluate the models in general remains a challenge and further investigations are needed. ...