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公开(公告)号:US20140188684A1
公开(公告)日:2014-07-03
申请号:US13921741
申请日:2013-06-19
IPC分类号: G06Q40/04
CPC分类号: G06Q40/04
摘要: A trading position evaluation system for evaluating trading positions that are globally optimum for a path-independent multi-asset European Contingent Claim (ECC) includes an option price determination module configured to determine a current option price matrix, a shifted option price matrix, and a normalized conditional variance matrix associated with underlying assets of the ECC at a trading time instance amongst a plurality of trading time instances obtained from a trader, based on ECC data and market data. Based on the current option price matrix, the shifted option price matrix, and the normalized conditional variance matrix, a position evaluation module evaluates a trading position in each of the underlying assets at the trading time instance that minimizes global variance of profit and loss to the trader.
摘要翻译: 用于评估对于与路径无关的多资产欧洲或有债权(ECC)全局最佳的交易头寸的交易头寸评估系统包括:期权价格确定模块,被配置为确定当前期权价格矩阵,转移期权价格矩阵和 基于ECC数据和市场数据,在从交易者获得的多个交易时间实例之间的交易时间实例中,与ECC的基础资产相关联的归一化条件方差矩阵。 基于当前期权价格矩阵,转移期权价格矩阵和归一化条件方差矩阵,位置评估模块评估交易时间实例中每个相关资产的交易头寸,将全球损益差异最小化为 商人。
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公开(公告)号:US20140074682A1
公开(公告)日:2014-03-13
申请号:US13776372
申请日:2013-02-25
IPC分类号: G06Q40/04
CPC分类号: G06Q40/04
摘要: A trading position evaluation system for evaluating trading positions that are globally optimum in a risk-neutral measure includes an option price determination module configured to determine a current option price and a shifted option price of an underlying asset of a European Contingent Claim (ECC) at a trading time instance amongst a plurality of trading time instances obtained from a trader, based on ECC data and market data. The ECC data comprises data associated with the ECC and the underlying asset of the ECC, and the market data comprises annualized volatility of the underlying asset and risk-free interest rate of market. Based on the current option price and the shifted option price, a position evaluation module evaluates a trading position at the trading time instance that minimizes global variance of profit and loss to the trader.
摘要翻译: 用于评估在风险中性测度中是全局最佳的交易头寸的交易头寸评估系统包括期权价格确定模块,其被配置为确定当前期权价格和欧洲或有债权(ECC)的标的资产的转移期权价格 基于ECC数据和市场数据,从交易者获得的多个交易时间实例中的交易时间实例。 ECC数据包括与ECC相关的数据和ECC的标的资产,市场数据包括标的资产的年化波动率和无风险的市场利率。 根据当前期权价格和期权价格变动,仓位评估模块评估交易时间实例的交易头寸,从而最大限度地减少交易者的全球损益差额。
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3.
公开(公告)号:US11556789B2
公开(公告)日:2023-01-17
申请号:US16909262
申请日:2020-06-23
摘要: This disclosure relates generally to system and method for time series prediction using a sparse recurrent mixture density network (RMDN), such as sparse LSTM-MDN and a sparse ED-MDN, for accurate forecasting of a high variability time series. The disclosed sparse RMDN has the ability to handle high-dimensional input features, capture trend shifts and high variability present in the data, and provide a confidence estimate of the forecast. A high-dimensional time series data is passed through a feedforward layer, which performs dimensionality reduction in an unsupervised manner by inducing sparsity on weights of the feedforward layer. The resultant low-dimensional time series is fed through recurrent layers to capture temporal patterns. These recurrent layers also aid in learning latent representation of the input data. Thereafter, a mixture density network (MDN) is used to model the variability and trend shifts present in the input and it also estimates the confidence of the predictions.
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公开(公告)号:US11538100B2
公开(公告)日:2022-12-27
申请号:US16827800
申请日:2020-03-24
发明人: Avinash Achar , Abhay Pratap Singh , Venkatesh Sarangan , Akshaya Natarajan , Easwara Subramanian , Sanjay Purushottam Bhat , Yogesh Bichpuriya
摘要: Sum of bid quantities (across price bands) placed by generators in energy markets have been observed to be either constant OR varying over a few finite values. Several researches have used simulated data to investigate desired aspect. However, these approaches have not been accurate in prediction. Embodiments of the present disclosure identified two sets of generators which needed specialized methods for regression (i) generators whose total bid quantity (TBQ) was constant (ii) generators whose total bid quantity varied over a few finite values only. In first category, present disclosure used a softmax output based ANN regressor to capture constant total bid quantity nature of targets and a loss function while training to capture error most meaningfully. For second category, system predicts total bid quantity (TBQ) of a generator and then predicts to allocate TBQ predicted across the various price bands which is accomplished by the softmax regression for constant TBQs.
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公开(公告)号:US20140207641A1
公开(公告)日:2014-07-24
申请号:US13922015
申请日:2013-06-19
IPC分类号: G06Q40/04
CPC分类号: G06Q40/04
摘要: A trading position evaluation system for evaluating trading positions that are globally optimum for a path-dependent European Contingent Claims (ECC) includes an option price determination module configured to determine a current option price and a shifted option price of the path-dependent ECC based on ECC data and market data. The current option price and the shifted option price are determined at a trading time instance, selected from amongst a plurality of trading time instances obtained from a trader, based on at least one discrete-monitoring time instance occurring before the trading time instance. Based on the current option price and the shifted option price, a position evaluation module evaluates a trading position in an underlying asset of the path-dependent ECC at the trading time instance that minimizes global variance of profit and loss to the trader.
摘要翻译: 用于评估对于依赖于路径的欧洲或有债权(ECC)全局最佳的交易头寸的交易头寸评估系统包括:期权价格确定模块,被配置为基于所述期权价格确定模块确定当前期权价格和基于路径的ECC的期权价格 ECC数据和市场数据。 基于在交易时间实例之前发生的至少一个离散监控时间实例,当前期权价格和转移的期权价格在交易时间实例中确定,所述交易时间实例从从交易者获得的多个交易时间实例中选择。 根据当前期权价格和期权价格变动,位置评估模块评估交易时间实例中路径依赖ECC的标的资产的交易头寸,从而最大限度地减少交易者的全球损益差异。
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公开(公告)号:US12093805B2
公开(公告)日:2024-09-17
申请号:US17213333
申请日:2021-03-26
发明人: Avinash Achar , Easwara Subramanian , Sanjay Purushottam Bhat , Vignesh Lakshmanan Kangadharan Palaniradja
摘要: This disclosure relates to method and system for optimal policy learning and recommendation for distribution task using deep RL model, in applications where when the action space has a probability simplex structure. The method includes training a RL agent by defining a policy network for learning the optimal policy using a policy gradient (PG) method, where the policy network comprising an artificial neural network (ANN) with a set of outputs. A continuous action space having a continuous probability simplex structure is defined. The learning of the optimal policy is updated based on one of stochastic and deterministic PG. For stochastic PG, a Dirichlet distribution based stochastic policy parameterized by output of the ANN with an activation function at an output layer of the ANN is selected. For deterministic PG, a soft-max function is selected as activation function at the output layer of the ANN to maintain the probability simplex structure.
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7.
公开(公告)号:US11476669B2
公开(公告)日:2022-10-18
申请号:US16897842
申请日:2020-06-10
发明人: Easwara Subramanian , Avinash Achar , Yogesh Kumar Bichpuriya , Sanjay Purushottam Bhat , Akshaya Natarajan , Venkatesh Sarangan , Abhay Pratap Singh
摘要: In energy markets in which bidding process is used to sell energy, it is important that a mechanism for deciding bidding amount is in place. State of the art systems in this domain have the disadvantage that they rely on simulation data, and also they make certain assumptions, and both the factors can affect accuracy of results when the systems are deployed and are expected to handle practical scenarios. The disclosure herein generally relates to energy markets, and, more particularly, to a method and a system for Reinforcement Learning (RL) based model for generating bids. The system trains a RL agent using historical data with respect to competitor bids places and Market Clearing Prices (MCPs). The RL agent then processes real-time inputs and generates bidding recommendations.
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公开(公告)号:US20140089159A1
公开(公告)日:2014-03-27
申请号:US13776453
申请日:2013-02-25
IPC分类号: G06Q40/04
CPC分类号: G06Q40/04
摘要: A trading position evaluation system for evaluating trading positions that are locally optimum in a market measure includes an option price determination module configured to determine at a trading time instance amongst a plurality of trading time instances obtained from a trader, a scaled option price and a shifted scaled option price of an underlying asset of a European Contingent Claim (ECC) based on ECC data and market data. The ECC data comprises data associated with the ECC and the underlying asset of the ECC, and the market data comprises annualized rate of return and annualized volatility of the underlying asset, and interest rate of market. Based on the scaled option price and the shifted scaled option price, a position evaluation module evaluates a trading position at the trading time instance that minimizes local variance of profit and loss to the trader.
摘要翻译: 用于评估在市场测量中是局部最佳的交易头寸的交易头寸评估系统包括期权价格确定模块,其被配置为在交易时间上确定从交易者获得的多个交易时间实例中,定价期权价格和经转移 基于ECC数据和市场数据的欧洲或有债权(ECC)的标的资产的定价期权价格。 ECC数据包括与ECC相关的数据和ECC的标的资产,市场数据包括标的资产的年化回报率和年度波动率以及市场利率。 基于定价期权价格和期权价格变动,仓位评估模块评估交易时间实例的交易头寸,从而最大限度地减少交易者的利润和损失的局部差异。
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