scholarly journals High-Frequency Cryptocurrency Trading Strategy using Tweet Sentiment Analysis

2021 ◽  
Author(s):  
Zhijun Chen
Keyword(s):  
Prediction Model ◽  
Sentiment Analysis ◽  
Portfolio Optimization ◽  
High Frequency ◽  
Optimization Procedure ◽  
Trading Strategy ◽  
Portfolio Allocation ◽  
Auxiliary Data ◽  
Cart Model

Sentiments are extracted from tweets with the hashtag of cryptocurrencies to predict the price and sentiment prediction model generates the parameters for optimization procedure to make decision and re-allocate the portfolio in the further step. Moreover, after the process of prediction, the evaluation, which is conducted with RMSE, MAE and R2, select the KNN and CART model for the prediction of Bitcoin and Ethereum respectively. During the process of portfolio optimization, this project is trying to use predictive prescription to robust the uncertainty and meanwhile take full advantages of auxiliary data such as sentiments. For the outcome of optimization, the portfolio allocation and returns fluctuate acutely as the illustration of figure.

scholarly journals An Analytical Prediction Model of Time Diversity Performance for Earth-Space Fade Mitigation

2008 ◽  
Vol 2008 ◽  
pp. 1-5 ◽  
Author(s):  
Pantelis-Daniel M. Arapoglou ◽  
Athanasios D. Panagopoulos ◽  
Panayotis G. Cottis
Keyword(s):  
Prediction Model ◽  
High Frequency ◽  
Rain Attenuation ◽  
Analytical Prediction ◽  
Time Dynamics ◽  
Time Diversity ◽  
Satellite Applications ◽  
Basic Parameters ◽  
Cost Efficient ◽  
General Prediction

Time diversity (TD) has recently attracted attention as a promising and cost-efficient solution for high-frequency broadcast satellite applications. The present work proposes a general prediction model for the application of TD by approximating the time dynamics of rain attenuation through the use of the joint lognormal distribution. The proposed method is tested against experimental data and its performance is investigated with respect to the basic parameters of a satellite link.

scholarly journals A special high-frequency soft-switched DC/DC power supply for GCT gate driver

2021 ◽  
Author(s):  
Jahangir Afsharian
Keyword(s):  
Power Supply ◽  
High Frequency ◽  
Optimization Procedure ◽  
Power Supplies ◽  
Zero Voltage Switching ◽  
General Power ◽  
Gate Driver ◽  
Isolation Transformer ◽  
Experimental Use ◽  
Gate Drivers

This thesis is devoted to the development of a novel parallel isolated power supply (PIPS) for the gate driver of integrated Gate Commutated Thyristors (GCT). The proposed PIPS is essentially a special high frequency soft switched DC/DC converter, integrating six parallel isolated power supplies in one module where each power supply generates a regulated dc supply for the GCT gate driver. In commercial GCT power supplies, a high-voltage isolation transformer is indispensable but highly inefficient in terms of cost and size, which can be significantly improved by the optimized transformer. In all, this design strives to achieve a general power supply for powering up the gate drivers of all types of GCT devices in all MV applications with minimal changes in configuration. In this thesis, the configuration of PIPS is presented and its operating principle is elaborated. The transformer optimization procedure satisfying the voltage isolation requirement of GCT gate drivers is extensively discussed. The performance of PIPS, including the front end DC/DC converter, zero voltage switching phase-shift full bridge (ZVS-PS-FB) converter, and the optimization of the transformer, is verified by simulations and experiments where a 360W laboratory prototype is built for the experimental use.

scholarly journals A Complete VADER-Based Sentiment Analysis of Bitcoin (BTC) Tweets during the Era of COVID-19

2020 ◽  
Vol 4 (4) ◽  
pp. 33
Author(s):  
Toni Pano ◽  
Rasha Kashef
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Prediction Model ◽  
Sentiment Analysis ◽  
Significant Role ◽  
Prediction Models ◽  
Financial Sector ◽  
Research Gap ◽  
Text Preprocessing ◽  
The Impact

During the COVID-19 pandemic, many research studies have been conducted to examine the impact of the outbreak on the financial sector, especially on cryptocurrencies. Social media, such as Twitter, plays a significant role as a meaningful indicator in forecasting the Bitcoin (BTC) prices. However, there is a research gap in determining the optimal preprocessing strategy in BTC tweets to develop an accurate machine learning prediction model for bitcoin prices. This paper develops different text preprocessing strategies for correlating the sentiment scores of Twitter text with Bitcoin prices during the COVID-19 pandemic. We explore the effect of different preprocessing functions, features, and time lengths of data on the correlation results. Out of 13 strategies, we discover that splitting sentences, removing Twitter-specific tags, or their combination generally improve the correlation of sentiment scores and volume polarity scores with Bitcoin prices. The prices only correlate well with sentiment scores over shorter timespans. Selecting the optimum preprocessing strategy would prompt machine learning prediction models to achieve better accuracy as compared to the actual prices.

scholarly journals Grid Trading System Robot (GTSbot): A Novel Mathematical Algorithm for trading FX Market

2019 ◽  
Vol 9 (9) ◽  
pp. 1796 ◽  
Author(s):  
Rundo ◽  
Trenta ◽  
di Stallo ◽  
Battiato
Keyword(s):  
High Frequency ◽  
High Performance ◽  
Foreign Exchange Market ◽  
Financial Time Series ◽  
Trading Strategy ◽  
High Frequency Trading ◽  
Trading System ◽  
Trading Systems ◽  
Time Series Forecast ◽  
Financial Transactions

Grid algorithmic trading has become quite popular among traders because it shows several advantages with respect to similar approaches. Basically, a grid trading strategy is a method that seeks to make profit on the market movements of the underlying financial instrument by positioning buy and sell orders properly time-spaced (grid distance). The main advantage of the grid trading strategy is the financial sustainability of the algorithm because it provides a robust way to mediate losses in financial transactions even though this also means very complicated trades management algorithm. For these reasons, grid trading is certainly one of the best approaches to be used in high frequency trading (HFT) strategies. Due to the high level of unpredictability of the financial markets, many investment funds and institutional traders are opting for the HFT (high frequency trading) systems, which allow them to obtain high performance due to the large number of financial transactions executed in the short-term timeframe. The combination of HFT strategies with the use of machine learning methods for the financial time series forecast, has significantly improved the capability and overall performance of the modern automated trading systems. Taking this into account, the authors propose an automatic HFT grid trading system that operates in the FOREX (foreign exchange) market. The performance of the proposed algorithm together with the reduced drawdown confirmed the effectiveness and robustness of the proposed approach.

scholarly journals Evaluating Various Portfolio Optimization Strategies using High-Dimensional Covariance Matrix Estimators

2020 ◽  
Vol 9 (6) ◽  
pp. 64-67
Keyword(s):  
Portfolio Optimization ◽  
Covariance Matrix ◽  
Global Minimum ◽  
Evaluation Studies ◽  
Minimum Variance ◽  
Portfolio Allocation ◽  
New Strategy ◽  
Rotationally Invariant ◽  
Diversified Portfolio ◽  
Allocation Strategies

We compare the performance of multiple covariance matrix estimators for the purpose of portfolio optimization. This evaluation studies the ability of estimators like Sample Based Estimator (SCE), Ledoit-Wolf Estimator (LWE), and Rotationally Invariant Estimators (RIE) to estimate covariance matrix and their competency in fulfilling the objectives of various portfolio allocation strategies. In this paper, we have captured the effectiveness of strategies such as Global Minimum Variance (GMVP) and Most-Diversified Portfolio (MDP) to produce optimal portfolios. Additionally, we also propose a new strategy inspired from MDP: Most-Diversified Portfolio (MMDP), that enables diversification upon minimizing risk. Empirical evaluations show that by and large, MMDP furnishes the maximum returns. LWE are relatively more robust than SCE and RIE but RIE performs better under certain conditions.

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