A real time clustering and SVM based price-volatility prediction for optimal trading strategy

2014 ◽  
Vol 131 ◽  
pp. 419-426 ◽  
Author(s):  
Subhabrata Choudhury ◽  
Subhajyoti Ghosh ◽  
Arnab Bhattacharya ◽  
Kiran Jude Fernandes ◽  
Manoj Kumar Tiwari
2019 ◽  
Vol 75 (1) ◽  
pp. 183-213
Author(s):  
Christian Gambardella ◽  
Michael Pahle ◽  
Wolf-Peter Schill

AbstractWe analyze the gross welfare gains from real-time retail pricing in electricity markets where carbon taxation induces investment in variable renewable technologies. Applying a stylized numerical electricity market model, we find a U-shaped association between carbon taxation and gross welfare gains. The benefits of introducing real-time pricing can accordingly be relatively low at relatively high carbon taxes and vice versa. The non-monotonous change in welfare gains can be explained by corresponding changes in the inefficiency arising from “under-consumption” during low-price periods rather than by changes in wholesale price volatility. Our results may cast doubt on the efficiency of ongoing roll-outs of advanced meters in many electricity markets, since net benefits might only materialize at relatively high carbon tax levels and renewable supply shares.


2016 ◽  
Vol 19 (08) ◽  
pp. 1650055 ◽  
Author(s):  
M. ALESSANDRA CRISAFI ◽  
ANDREA MACRINA

We consider an optimal trading problem over a finite period of time during which an investor has access to both a standard exchange and a dark pool. We take the exchange to be an order-driven market and propose a continuous-time setup for the best bid price and the market spread, both modeled by Lévy processes. Effects on the best bid price arising from the arrival of limit buy orders at more favorable prices, the incoming market sell orders potentially walking the book, and deriving from the cancellations of limit sell orders at the best ask price are incorporated in the proposed price dynamics. A permanent impact that occurs when ‘lit’ pool trades cannot be avoided is built in, and an instantaneous impact that models the slippage, to which all lit exchange trades are subject, is also considered. We assume that the trading price in the dark pool is the mid-price and that no fees are due for posting orders. We allow for partial trade executions in the dark pool, and we find the optimal trading strategy in both venues. Since the mid-price is taken from the exchange, the dynamics of the limit order book also affects the optimal allocation of shares in the dark pool. We propose a general objective function and we show that, subject to suitable technical conditions, the value function can be characterized by the unique continuous viscosity solution to the associated partial integro-differential equation. We present two explicit examples of the price and the spread models, derive the associated optimal trading strategy numerically. We discuss the various degrees of the agent's risk aversion and further show that roundtrips are not necessarily beneficial.


2013 ◽  
Vol 16 (1) ◽  
pp. 1-32 ◽  
Author(s):  
Anna A. Obizhaeva ◽  
Jiang Wang

2012 ◽  
Vol 433-440 ◽  
pp. 1677-1682
Author(s):  
Heng Kai Hu ◽  
Cheng Jian Wei ◽  
Qing Hua Chen ◽  
Guang Yang

One of the key advantages of the smart grid is its capability to manage the electricity trading automatically between homes and electricity grids to cope with the inherent real-time dynamic in electricity demand and supply. In this context, this paper presents some advantages and capabilities offered by intelligent agents for trading of electricity in the microgrid. A market-based mechanism and agent trading strategy are developed and studied. Based on the Continuous Double Auction model, the proposed mechanism can deal with unforeseen demand or increased supply capacity in real time. A new trading strategy that can achieve higher market efficiency than previously published well known strategy is also illustrated. The importance of intelligent agents for the distributed control and autonomous operation for the smart grid is demonstrated.


2014 ◽  
Vol 631-632 ◽  
pp. 62-65
Author(s):  
Ru Zhen Yan ◽  
Ping Li ◽  
Yong Zeng

Financial markets has witnessed an explosion of algorithmic trading strategy which can help traders especially involved in high-frequency trading efficiently reduce invisible transaction cost. The VWAP strategy usually used by traders can only decrease the cost of price impact by breaking block order into small pieces. However, the behavior of such order splitting may result in inevitable opportunity cost as well as price appreciation. This paper establishes a new algorithmic trading strategy to minimize total transaction costs including price impact, opportunity cost and price appreciation. The results show that the total transaction cost of this optimal trading strategy is lower than VWAP strategy.


Sign in / Sign up

Export Citation Format

Share Document