Algorithmic trading

About: Algorithmic trading is a research topic. Over the lifetime, 6718 publications have been published within this topic receiving 162209 citations. The topic is also known as: algotrading & Algorithmic trading.

A Closed-Form Execution Strategy to Target Volume Weighted Average Price

Abstract: We provide two explicit closed-form optimal execution strategies to target VWAP. We do this under very general assumptions about the stochastic process followed by the volume traded in the market, and, unlike earlier studies, we account for permanent price impact stemming from order-flow of the agent and all other traders. One of the strategies consists of TWAP adjusted upward by a fraction of instantaneous order-flow and adjusted downward by the average order-flow that is expected over the remaining life of the strategy. The other strategy consists of the Almgren-Chriss execution strategy adjusted by the expected volume and net order-flow during the remaining life of the strategy. We calibrate model parameters to five stocks traded in Nasdaq (FARO, SMH, NTAP, ORCL, INTC) and use simulations to show that the strategies target VWAP very closely and on average outperform the target by between 0.10 and 8 basis points.

Knowledge Graph-based Event Embedding Framework for Financial Quantitative Investments

Abstract: Event representative learning aims to embed news events into continuous space vectors for capturing syntactic and semantic information from text corpus, which is benefit to event-driven quantitative investments. However, the financial market reaction of events is also influenced by the lead-lag effect, which is driven by internal relationships. Therefore, in this paper, we present a knowledge graph-based event embedding framework for quantitative investments. In particular, we first extract structured events from raw texts, and construct the knowledge graph with the mentioned entities and relations simultaneously. Then, we leverage a joint model to merge the knowledge graph information into the objective function of an event embedding learning model. The learned representations are fed as inputs of downstream quantitative trading methods. Extensive experiments on real-world dataset demonstrate the effectiveness of the event embeddings learned from financial news and knowledge graphs. We also deploy the framework for quantitative algorithm trading. The accumulated portfolio return contributed by our method significantly outperforms other baselines.

Method and system for intelligent automated security trading via the internet

Abstract: A method and system for intelligent automated security trading via the Internet, which provides an Automated Trading Service Center (ATSC), offering a Home Page Service and managing at least one Virtual Fund Manager (VFM) syst. The ATSC is a wireless e-commerce service center providing automated trading services to investors anytime anywhere. By accessing the HPS, the investor can interact with ATSC to develop a custom investment strategy based personal VFM system to automatically trade electronic trading based securities. The VFM is a real time running program incorporated by four engines: the Quote Processing Engine, the Decision Making Engine, the Order Execution Engine, and the Order Processing Engine. Under commands of ATSC server, the VFM can periodically retrieve quotation from the security exchange, monitor the market fluctuation by performing intensive calculations to detect the desired Buy/Sell timing as predetermined by investor's proprietary investment strategy algorithms, and ultimately execute the transaction automatically.