Napoleon Crypto Asset Manager is developing an artificial intelligence-based allocation tool to optimize investment performance across its primary strategies for various asset classes including equities, rates, commodities, and cryptocurrencies. The tool will utilize a proprietary database of financial indicators and market states derived from raw market data through processing techniques. A supervised machine learning framework will be used to train algorithms on past periods to predict optimal allocations across strategies going forward, which will be backtested across all periods to evaluate performance out of sample. The goal is to find the configuration that delivers the best risk-adjusted returns through exhaustive testing of different algorithms and parameters.
This document summarizes an intraday event study using news data and market reaction profiles. Key points:
- It uses a unique real-time news feed classified with an extensive taxonomy to build intraday market reaction profiles for Russell 1000 stocks around news events.
- It defines abnormal returns, volatility, and volume around news events and finds distinct responses in these measures around news arrivals.
- By ranking profiles based on post-event returns, volatility, and event frequency, it aims to characterize market reactions and identify the most profitable trading strategies.
- Sentiment analysis and other news analytics like relevance and novelty are found to help filter significant effects and predict future price trends.
This document discusses algorithmic trading and summarizes various techniques that can be used to develop and optimize automated trading systems including:
- Using technical analysis signals like MACD, RSI, and %R to characterize market state along with sentiment analysis of news data.
- Employing algorithms like genetic algorithms, neural networks, and random forests to improve trading strategies.
- Predicting order book evolution by analyzing customized market data from Reuters including order book depth and price/volume information over time.
- Applying machine learning techniques like regression trees, bagging, and genetic programming to build and optimize trading algorithms.
IRJET- Stock Market Prediction using Machine Learning TechniquesIRJET Journal
This document discusses using machine learning techniques to predict stock market prices. It proposes building a machine learning model that uses historical stock data to predict future stock prices. The model would go through preprocessing, processing, and regression analysis of the dataset to make predictions. Predicting stock market movements accurately is challenging, but this model aims to generate results using machine learning and deep learning algorithms on the dataset to help investors make trading decisions.
AlgoB – Cryptocurrency price prediction system using LSTMIRJET Journal
This document describes a cryptocurrency price prediction system called AlgoB that uses an LSTM neural network model. The system was developed by four students to predict cryptocurrency prices with high accuracy. It takes historical price data as input and can predict future prices. The system uses libraries like NumPy, Pandas, TensorFlow and Matplotlib. It achieves 80% prediction accuracy, outperforming regression and tree models. The LSTM model is trained on price data and evaluates predictions against real prices. This helps traders understand market movements and identify good times to buy and sell cryptocurrencies.
This talk provides a critical view on employing machine learning / deep learning methods in algorithmic trading. We highlight the particular challenges that we meet in this domain along with approaches to tackle some of these challenges in practice. Even though experience has shown that algorithmic trading using advanced machine learning can be successful, the crucial issue remains that predictive patterns utilizing market inefficiencies quickly become void as soon as competing market participants use them too. The conclusion is that the crucial advantage is – and has always been – to know more and to be faster than competitors.
Our Speaker: Dr. Ulrich Bodenhofer
MSc (applied math, Johannes Kepler University, Linz, Austria, 1996)
PhD (applied math, Johannes Kepler University, Linz, Austria, 1998)
Since June 2018: Chief Artificial Intelligence Officer at QUOMATIC.AI (Linz, Austria)
This project aims to provide accurate and reliable predictions for stock prices using the power of LSTM (Long Short-Term Memory) and ARIMA (AutoRegressive Integrated Moving Average) models. By analyzing historical stock data and leveraging the capabilities of these advanced forecasting models, we help investors and traders make informed decisions and optimize their investment strategies.
The project workflow begins with gathering comprehensive historical stock price data, including open, high, low, and closing prices, as well as trading volumes and other relevant features. This data is then preprocessed to handle missing values, outliers, and any other inconsistencies that may impact the accuracy of the predictions.
For time series analysis and forecasting, we employ the LSTM model, a variant of recurrent neural networks (RNNs) known for their ability to capture long-term dependencies in sequential data. LSTM models have proven to be highly effective in capturing the complex patterns and trends present in stock price data. By training the LSTM model on historical stock data, we can predict future stock prices with a high degree of accuracy.
In addition to LSTM, we utilize the ARIMA model, a widely used statistical method for time series forecasting. ARIMA models capture the autoregressive, moving average, and integrated components of a time series, allowing us to capture both short-term and long-term trends in stock prices. By incorporating the ARIMA model into our prediction pipeline, we further enhance the accuracy and reliability of our forecasts.
To evaluate the performance of our models, we use appropriate evaluation metrics such as mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). These metrics provide insights into the effectiveness of our models and help us fine-tune the parameters for optimal performance.
The Stock Price Prediction project using LSTM and ARIMA models represents our commitment to leveraging advanced machine learning and statistical techniques to provide valuable insights in the financial domain. By accurately forecasting stock prices, we empower investors and traders to make data-driven decisions, mitigate risks, and optimize their investment strategies. This project showcases our expertise in time series analysis, deep learning, and statistical modeling, and our dedication to delivering solutions that drive tangible business outcomes in the financial sector.
This document discusses using data mining techniques and machine learning algorithms to predict stock market prices based on historical stock data and financial news articles. It proposes a novel method using an artificial neural network trained on numerical representations of stock quotes, news keywords, and technical indicators to forecast closing prices. The method involves preprocessing news articles, extracting key phrases, representing the data numerically, and training a neural network with backpropagation to predict stock market indices.
This document summarizes an intraday event study using news data and market reaction profiles. Key points:
- It uses a unique real-time news feed classified with an extensive taxonomy to build intraday market reaction profiles for Russell 1000 stocks around news events.
- It defines abnormal returns, volatility, and volume around news events and finds distinct responses in these measures around news arrivals.
- By ranking profiles based on post-event returns, volatility, and event frequency, it aims to characterize market reactions and identify the most profitable trading strategies.
- Sentiment analysis and other news analytics like relevance and novelty are found to help filter significant effects and predict future price trends.
This document discusses algorithmic trading and summarizes various techniques that can be used to develop and optimize automated trading systems including:
- Using technical analysis signals like MACD, RSI, and %R to characterize market state along with sentiment analysis of news data.
- Employing algorithms like genetic algorithms, neural networks, and random forests to improve trading strategies.
- Predicting order book evolution by analyzing customized market data from Reuters including order book depth and price/volume information over time.
- Applying machine learning techniques like regression trees, bagging, and genetic programming to build and optimize trading algorithms.
IRJET- Stock Market Prediction using Machine Learning TechniquesIRJET Journal
This document discusses using machine learning techniques to predict stock market prices. It proposes building a machine learning model that uses historical stock data to predict future stock prices. The model would go through preprocessing, processing, and regression analysis of the dataset to make predictions. Predicting stock market movements accurately is challenging, but this model aims to generate results using machine learning and deep learning algorithms on the dataset to help investors make trading decisions.
AlgoB – Cryptocurrency price prediction system using LSTMIRJET Journal
This document describes a cryptocurrency price prediction system called AlgoB that uses an LSTM neural network model. The system was developed by four students to predict cryptocurrency prices with high accuracy. It takes historical price data as input and can predict future prices. The system uses libraries like NumPy, Pandas, TensorFlow and Matplotlib. It achieves 80% prediction accuracy, outperforming regression and tree models. The LSTM model is trained on price data and evaluates predictions against real prices. This helps traders understand market movements and identify good times to buy and sell cryptocurrencies.
This talk provides a critical view on employing machine learning / deep learning methods in algorithmic trading. We highlight the particular challenges that we meet in this domain along with approaches to tackle some of these challenges in practice. Even though experience has shown that algorithmic trading using advanced machine learning can be successful, the crucial issue remains that predictive patterns utilizing market inefficiencies quickly become void as soon as competing market participants use them too. The conclusion is that the crucial advantage is – and has always been – to know more and to be faster than competitors.
Our Speaker: Dr. Ulrich Bodenhofer
MSc (applied math, Johannes Kepler University, Linz, Austria, 1996)
PhD (applied math, Johannes Kepler University, Linz, Austria, 1998)
Since June 2018: Chief Artificial Intelligence Officer at QUOMATIC.AI (Linz, Austria)
This project aims to provide accurate and reliable predictions for stock prices using the power of LSTM (Long Short-Term Memory) and ARIMA (AutoRegressive Integrated Moving Average) models. By analyzing historical stock data and leveraging the capabilities of these advanced forecasting models, we help investors and traders make informed decisions and optimize their investment strategies.
The project workflow begins with gathering comprehensive historical stock price data, including open, high, low, and closing prices, as well as trading volumes and other relevant features. This data is then preprocessed to handle missing values, outliers, and any other inconsistencies that may impact the accuracy of the predictions.
For time series analysis and forecasting, we employ the LSTM model, a variant of recurrent neural networks (RNNs) known for their ability to capture long-term dependencies in sequential data. LSTM models have proven to be highly effective in capturing the complex patterns and trends present in stock price data. By training the LSTM model on historical stock data, we can predict future stock prices with a high degree of accuracy.
In addition to LSTM, we utilize the ARIMA model, a widely used statistical method for time series forecasting. ARIMA models capture the autoregressive, moving average, and integrated components of a time series, allowing us to capture both short-term and long-term trends in stock prices. By incorporating the ARIMA model into our prediction pipeline, we further enhance the accuracy and reliability of our forecasts.
To evaluate the performance of our models, we use appropriate evaluation metrics such as mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). These metrics provide insights into the effectiveness of our models and help us fine-tune the parameters for optimal performance.
The Stock Price Prediction project using LSTM and ARIMA models represents our commitment to leveraging advanced machine learning and statistical techniques to provide valuable insights in the financial domain. By accurately forecasting stock prices, we empower investors and traders to make data-driven decisions, mitigate risks, and optimize their investment strategies. This project showcases our expertise in time series analysis, deep learning, and statistical modeling, and our dedication to delivering solutions that drive tangible business outcomes in the financial sector.
This document discusses using data mining techniques and machine learning algorithms to predict stock market prices based on historical stock data and financial news articles. It proposes a novel method using an artificial neural network trained on numerical representations of stock quotes, news keywords, and technical indicators to forecast closing prices. The method involves preprocessing news articles, extracting key phrases, representing the data numerically, and training a neural network with backpropagation to predict stock market indices.
The Analysis of Share Market using Random Forest & SVMIRJET Journal
The document discusses using machine learning algorithms like random forest and support vector machines (SVM) to predict stock market movement and values. Specifically, it aims to develop a more accurate technique for forecasting stock behavior by applying these algorithms to preprocessed historical stock data from Yahoo Finance. Random forest and SVM will be used to generate precise predictions. The goal is to build an effective machine learning model that can provide real-world solutions for issues faced by stockholders and market organizations.
ELASTIC PROPERTY EVALUATION OF FIBRE REINFORCED GEOPOLYMER COMPOSITE USING SU...IRJET Journal
This document discusses using machine learning algorithms like random forest and support vector machines to predict stock market prices more accurately. It analyzes using these algorithms on historical stock price data from Yahoo Finance to train models. Specifically, it trains random forest and SVM models on 80% of the data and tests them on the remaining 20% to predict future stock prices. The goal is to develop a more effective technique for stock price forecasting using artificial intelligence methods.
UNRAVELING THE POWER OF QUANTOPIAN ALGORITHMS IN FINANCIAL MARKETSRiya Sen
In today’s fast-paced financial world, staying ahead of the curve is a must for traders and investors. With the advent of technology, the use of algorithmic trading strategies has become increasingly popular. Among the many platforms that cater to algorithmic trading, Quantopian stands out as a powerful tool. In this blog post, we will delve into the world of Quantopian algorithms, exploring what they are, how they work, and their significance in financial markets.
IRJET- Stock Market Prediction using Machine LearningIRJET Journal
This document discusses using machine learning techniques to predict stock market movements. Specifically, it uses a Support Vector Machine (SVM) algorithm with a Radial Basis Function (RBF) kernel to predict stock prices. It describes collecting stock price data, selecting features like price volatility and momentum, training the SVM model on historical data, and generating predictions of future stock prices. The results show the SVM model was able to accurately predict the movements of IBM stock prices based on historical data.
In Stock Market Prediction, the aim is to predict the future value of the financial stocks of a company. The recent trend in stock market prediction technologies is the use of machine learning which makes predictions based on the values of current stock market indices by training on their previous values. Machine learning itself employs different models to make prediction easier and authentic.
Classification of quantitative trading strategies webinar pptQuantInsti
There exist thousands of academic research papers written on trading strategies. Learn what these academics found out and how we can use their knowledge in the trading world.
The webinar covers:
- Overview of research in a field of quantitative trading
- Taxonomy of quantitative trading strategies
- Where to look for unique alpha
- Examples of lesser-known trading strategies
- Common issues in quant research
Learn more about our EPAT™ course here: https://www.quantinsti.com/epat/
Most Useful links
Join EPAT – Executive Programme in Algorithmic Trading: https://goo.gl/3Oyf2B
Visit us at: https://www.quantinsti.com/
Like us on Facebook: https://www.facebook.com/quantinsti/
Follow us on Twitter: https://twitter.com/QuantInsti
Access the webinar recording here: http://ow.ly/1YwO30dz5FD
Know more about EPAT™ by QuantInsti™ at http://www.quantinsti.com/epat/
This document discusses data mining methods and implementation of predictive data mining architecture for stock market prediction. It describes predicting unknown data values using classification, regression, and time series analysis. Two types of predictions discussed are stock market and environmental predictions. Stock market prediction aims to determine future company stock prices, while various parameters like return on investment are analyzed. The document also covers data mining techniques like descriptive and predictive mining, algorithms, and performance evaluation metrics like correct profitable trade signals and annual return on investment.
Performance Comparisons among Machine Learning Algorithms based on the Stock ...IRJET Journal
This document compares the performance of various machine learning algorithms for predicting stock market performance based on stock market data and news data. It applies algorithms like linear regression, random forest, decision tree, K-nearest neighbors, logistic regression, linear discriminant analysis, XGBoost classifier, and Gaussian naive Bayes to datasets containing stock market values, news articles, and Reddit posts. It evaluates the algorithms based on metrics like accuracy, recall, precision and F1 score. The results suggest that linear discriminant analysis achieved the best performance at predicting stock market values based on the given datasets and evaluation metrics.
IRJET - Stock Market Analysis and PredictionIRJET Journal
This document discusses using machine learning algorithms to analyze stock market data and predict future stock prices. It proposes collecting historical stock price and Twitter sentiment data and using recurrent neural networks and long short-term memory models to analyze the data and generate predictions and visualizations. The models would allow investors to make informed decisions about buying and selling stocks to potentially achieve returns on their investments.
Chris Nagao is seeking a role that allows him to continue learning about new markets while leveraging his analytical skills and market knowledge to help manage fixed income portfolios. He has over 8 years of experience in the financial services industry. Currently, he is an Associate Portfolio Manager at Charles Schwab Investment Management where he manages municipal money and bond funds totaling $28.1 billion in assets under management. Previously, as a Senior Applications Engineer also at Charles Schwab Investment Management, he engineered various portfolio management tools and analytics platforms. He holds a Bachelor's degree in Computer Science.
The document summarizes an investment fund called ACOS Arbitrage Fund that uses quantitative strategies for long/short equity investing. It describes the team's experience and investment philosophy, which combines quantitative equity market neutral with capital structure arbitrage and options analysis. The process involves dynamic universe selection, an equity capital arbitrage model, optimization, trade execution, and risk management. Research and development is emphasized to continuously improve the models. Backtesting shows promising results of enhanced returns when incorporating credit and options data.
Quant Foundry Labs - Low Probability DefaultsDavidkerrkelly
The Quant Foundry Labs division was approached to improve models for predicting low probability sovereign defaults. They developed a machine learning model that uses a large dataset of economic, financial, and governance indicators to predict sovereign credit ratings. The model was trained and tested on historical data, demonstrating improved accuracy over traditional statistical techniques. Explanatory tools also provide transparency into the model's predictions. The results represent an improvement in predicting low probability default events, which can help with regulatory requirements and risk management.
Data-Driven Approach to Stock Market Prediction and Sentiment AnalysisIRJET Journal
This document discusses a data-driven approach to stock market prediction and sentiment analysis. It proposes combining recurrent neural networks with long short-term memory (RNN-LSTM) to predict stock prices based on historical data, and using support vector machines (SVM) to analyze sentiment from news headlines and predict how it may affect stock trends. The paper reviews several related works applying machine learning techniques like RNN, LSTM, and SVM to stock prediction and sentiment analysis. It aims to improve prediction accuracy by combining both historical data analysis and sentiment analysis of news articles.
Stochastics, Volume Rate of Change, Relative Strength to Index, Bid/Ask Volume ratio, Floor Trader Pivots and Institutional buying/selling are 6 of our most useful indicators for running the ITRS algorithms to identify risk and growth factors related to index trend reversals. We use a total of 33 indicators that each generate a critical factor.
STOCK MARKET PREDICTION USING MACHINE LEARNING IN PYTHONIRJET Journal
This document discusses using machine learning techniques to predict stock market prices. Specifically, it evaluates using support vector machines, random forests, and regression models. It finds that support vector regression with an RBF kernel performed best compared to other models at accurately predicting stock prices based on historical data. The paper also reviews several related works applying machine learning methods like neural networks and support vector machines to financial time series data for stock prediction.
A Deep Guide To Crypto Exchange DevelopmentITIO Innovex
. It's essentially a ready made crypto exchange development that you can customize with your logo, name, and potentially some features to create a unique user experience. Visit us at: https://itio.in/services/crypto-exchange-development
20 Simple Questions from Exactpro for Your Enjoyment This Holiday SeasonIosif Itkin
Warmest wishes for a happy holiday season and a wonderful New Year!
We look forward to our continued collaboration in 2020. Thank you for your support.
Dynamical smart liquidity on decentralized exchanges for lucrative market makingStefan Duprey
This document discusses liquidity provision on Uniswap V3 and proposes algorithms for actively managing liquidity positions. It introduces the concept of concentrated liquidity on Uniswap V3 which allows liquidity providers to specify a price range. It discusses different configurations for liquidity provision including long, short, and market neutral approaches. It then presents algorithms for dynamically exiting liquidity positions based on trend signals, modeling historical fees, and determining optimal price range bounds while minimizing costs like impermanent loss and transaction fees.
This document discusses two strategies for generating yield from bitcoin holdings:
1. A low frequency strategy that sells in-the-money covered calls with long durations of over 6 months, generating a large premium but capping upside gains.
2. A middle frequency strategy that rolls call options weekly with strikes unlikely to be reached, compounding the premium over time.
It also discusses optimizing call option maturities and strikes to guarantee a target yield percentage while allowing for varying degrees of upside potential.
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The Analysis of Share Market using Random Forest & SVMIRJET Journal
The document discusses using machine learning algorithms like random forest and support vector machines (SVM) to predict stock market movement and values. Specifically, it aims to develop a more accurate technique for forecasting stock behavior by applying these algorithms to preprocessed historical stock data from Yahoo Finance. Random forest and SVM will be used to generate precise predictions. The goal is to build an effective machine learning model that can provide real-world solutions for issues faced by stockholders and market organizations.
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This document discusses using machine learning algorithms like random forest and support vector machines to predict stock market prices more accurately. It analyzes using these algorithms on historical stock price data from Yahoo Finance to train models. Specifically, it trains random forest and SVM models on 80% of the data and tests them on the remaining 20% to predict future stock prices. The goal is to develop a more effective technique for stock price forecasting using artificial intelligence methods.
UNRAVELING THE POWER OF QUANTOPIAN ALGORITHMS IN FINANCIAL MARKETSRiya Sen
In today’s fast-paced financial world, staying ahead of the curve is a must for traders and investors. With the advent of technology, the use of algorithmic trading strategies has become increasingly popular. Among the many platforms that cater to algorithmic trading, Quantopian stands out as a powerful tool. In this blog post, we will delve into the world of Quantopian algorithms, exploring what they are, how they work, and their significance in financial markets.
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This document discusses using machine learning techniques to predict stock market movements. Specifically, it uses a Support Vector Machine (SVM) algorithm with a Radial Basis Function (RBF) kernel to predict stock prices. It describes collecting stock price data, selecting features like price volatility and momentum, training the SVM model on historical data, and generating predictions of future stock prices. The results show the SVM model was able to accurately predict the movements of IBM stock prices based on historical data.
In Stock Market Prediction, the aim is to predict the future value of the financial stocks of a company. The recent trend in stock market prediction technologies is the use of machine learning which makes predictions based on the values of current stock market indices by training on their previous values. Machine learning itself employs different models to make prediction easier and authentic.
Classification of quantitative trading strategies webinar pptQuantInsti
There exist thousands of academic research papers written on trading strategies. Learn what these academics found out and how we can use their knowledge in the trading world.
The webinar covers:
- Overview of research in a field of quantitative trading
- Taxonomy of quantitative trading strategies
- Where to look for unique alpha
- Examples of lesser-known trading strategies
- Common issues in quant research
Learn more about our EPAT™ course here: https://www.quantinsti.com/epat/
Most Useful links
Join EPAT – Executive Programme in Algorithmic Trading: https://goo.gl/3Oyf2B
Visit us at: https://www.quantinsti.com/
Like us on Facebook: https://www.facebook.com/quantinsti/
Follow us on Twitter: https://twitter.com/QuantInsti
Access the webinar recording here: http://ow.ly/1YwO30dz5FD
Know more about EPAT™ by QuantInsti™ at http://www.quantinsti.com/epat/
This document discusses data mining methods and implementation of predictive data mining architecture for stock market prediction. It describes predicting unknown data values using classification, regression, and time series analysis. Two types of predictions discussed are stock market and environmental predictions. Stock market prediction aims to determine future company stock prices, while various parameters like return on investment are analyzed. The document also covers data mining techniques like descriptive and predictive mining, algorithms, and performance evaluation metrics like correct profitable trade signals and annual return on investment.
Performance Comparisons among Machine Learning Algorithms based on the Stock ...IRJET Journal
This document compares the performance of various machine learning algorithms for predicting stock market performance based on stock market data and news data. It applies algorithms like linear regression, random forest, decision tree, K-nearest neighbors, logistic regression, linear discriminant analysis, XGBoost classifier, and Gaussian naive Bayes to datasets containing stock market values, news articles, and Reddit posts. It evaluates the algorithms based on metrics like accuracy, recall, precision and F1 score. The results suggest that linear discriminant analysis achieved the best performance at predicting stock market values based on the given datasets and evaluation metrics.
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This document discusses using machine learning algorithms to analyze stock market data and predict future stock prices. It proposes collecting historical stock price and Twitter sentiment data and using recurrent neural networks and long short-term memory models to analyze the data and generate predictions and visualizations. The models would allow investors to make informed decisions about buying and selling stocks to potentially achieve returns on their investments.
Chris Nagao is seeking a role that allows him to continue learning about new markets while leveraging his analytical skills and market knowledge to help manage fixed income portfolios. He has over 8 years of experience in the financial services industry. Currently, he is an Associate Portfolio Manager at Charles Schwab Investment Management where he manages municipal money and bond funds totaling $28.1 billion in assets under management. Previously, as a Senior Applications Engineer also at Charles Schwab Investment Management, he engineered various portfolio management tools and analytics platforms. He holds a Bachelor's degree in Computer Science.
The document summarizes an investment fund called ACOS Arbitrage Fund that uses quantitative strategies for long/short equity investing. It describes the team's experience and investment philosophy, which combines quantitative equity market neutral with capital structure arbitrage and options analysis. The process involves dynamic universe selection, an equity capital arbitrage model, optimization, trade execution, and risk management. Research and development is emphasized to continuously improve the models. Backtesting shows promising results of enhanced returns when incorporating credit and options data.
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The Quant Foundry Labs division was approached to improve models for predicting low probability sovereign defaults. They developed a machine learning model that uses a large dataset of economic, financial, and governance indicators to predict sovereign credit ratings. The model was trained and tested on historical data, demonstrating improved accuracy over traditional statistical techniques. Explanatory tools also provide transparency into the model's predictions. The results represent an improvement in predicting low probability default events, which can help with regulatory requirements and risk management.
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[4:55 p.m.] Bryan Oates
OJPs are becoming a critical resource for policy-makers and researchers who study the labour market. LMIC continues to work with Vicinity Jobs’ data on OJPs, which can be explored in our Canadian Job Trends Dashboard. Valuable insights have been gained through our analysis of OJP data, including LMIC research lead
Suzanne Spiteri’s recent report on improving the quality and accessibility of job postings to reduce employment barriers for neurodivergent people.
Decoding job postings: Improving accessibility for neurodivergent job seekers
Improving the quality and accessibility of job postings is one way to reduce employment barriers for neurodivergent people.
Economic Risk Factor Update: June 2024 [SlideShare]Commonwealth
May’s reports showed signs of continued economic growth, said Sam Millette, director, fixed income, in his latest Economic Risk Factor Update.
For more market updates, subscribe to The Independent Market Observer at https://blog.commonwealth.com/independent-market-observer.
Optimizing Net Interest Margin (NIM) in the Financial Sector (With Examples).pdfshruti1menon2
NIM is calculated as the difference between interest income earned and interest expenses paid, divided by interest-earning assets.
Importance: NIM serves as a critical measure of a financial institution's profitability and operational efficiency. It reflects how effectively the institution is utilizing its interest-earning assets to generate income while managing interest costs.
OJP data from firms like Vicinity Jobs have emerged as a complement to traditional sources of labour demand data, such as the Job Vacancy and Wages Survey (JVWS). Ibrahim Abuallail, PhD Candidate, University of Ottawa, presented research relating to bias in OJPs and a proposed approach to effectively adjust OJP data to complement existing official data (such as from the JVWS) and improve the measurement of labour demand.
The Impact of Generative AI and 4th Industrial RevolutionPaolo Maresca
This infographic explores the transformative power of Generative AI, a key driver of the 4th Industrial Revolution. Discover how Generative AI is revolutionizing industries, accelerating innovation, and shaping the future of work.
"Does Foreign Direct Investment Negatively Affect Preservation of Culture in the Global South? Case Studies in Thailand and Cambodia."
Do elements of globalization, such as Foreign Direct Investment (FDI), negatively affect the ability of countries in the Global South to preserve their culture? This research aims to answer this question by employing a cross-sectional comparative case study analysis utilizing methods of difference. Thailand and Cambodia are compared as they are in the same region and have a similar culture. The metric of difference between Thailand and Cambodia is their ability to preserve their culture. This ability is operationalized by their respective attitudes towards FDI; Thailand imposes stringent regulations and limitations on FDI while Cambodia does not hesitate to accept most FDI and imposes fewer limitations. The evidence from this study suggests that FDI from globally influential countries with high gross domestic products (GDPs) (e.g. China, U.S.) challenges the ability of countries with lower GDPs (e.g. Cambodia) to protect their culture. Furthermore, the ability, or lack thereof, of the receiving countries to protect their culture is amplified by the existence and implementation of restrictive FDI policies imposed by their governments.
My study abroad in Bali, Indonesia, inspired this research topic as I noticed how globalization is changing the culture of its people. I learned their language and way of life which helped me understand the beauty and importance of cultural preservation. I believe we could all benefit from learning new perspectives as they could help us ideate solutions to contemporary issues and empathize with others.
Falcon stands out as a top-tier P2P Invoice Discounting platform in India, bridging esteemed blue-chip companies and eager investors. Our goal is to transform the investment landscape in India by establishing a comprehensive destination for borrowers and investors with diverse profiles and needs, all while minimizing risk. What sets Falcon apart is the elimination of intermediaries such as commercial banks and depository institutions, allowing investors to enjoy higher yields.
Financial quantitative strategies using artificial intelligence
1. B U I L D I N G T H E F U T U R E O F
I N V E S T I N G
N AP O L E O N
C RY P TO
N a p o l e o n X C r y p t o A s s e t M a n a g e r
Tuesday, 10 August 2021
2. Tuesday, 10 August 2021
B U I L D I N G T H E F U T U R E O F I N V E S T I N G
N A P O L E O N
C R Y P T O
D É V E L O P P E M E N T O U T I L D ’A L L O C AT I O N À
B A S E D ’ I A
3. Développement d’une plateforme sécurisée
d’investissements performants et automatisés d’actifs et de
cryptoactifs financiers
Difficulté de créer des algorithmes qui
soient capables de générer de la
performance quelles que soient les
conditions de marchés et/ou pour des
actifs différents.
Fournir de la performance
absolue à base d’algorithmes
tout en prenant en compte
des contraintes des grands
investisseurs: sous-jacents
liquides, régulation, faits
stylistiques
Définition d’une méthodologie
outillée de composition
d’algorithmes basées sur la
recherche de features
Recherche d’algorithmes de composition
d’algorithmes d’investissement
Solutions patrimoniales/Mélange crypto
aggressif
IA/econometrics
Recherche d’algorithmes d’investissement:
sélection univers avec contraintes
Construction de stratégies par univers
IA/econometrics
Recherche de la
performance optimale
via des algorithmes
d’investissement
Objectif de R&D
Verrous
Travaux
Légende
Axe de R&D
Problématiques
Implémenter des solutions
adaptatives en fonction des
carnets d’ordre à base de
techniques de découpage
d’ordres de marché. Difficile à
backtester. Heuristique et mise
en production directe
Recherche sur les algorithmes de meilleure
exécution efficace dans le monde des crypto
actifs
TWAP, advanced iceberg order, ..
IA/Reinforcement learning
Plus la fréquence est élevée (turn over), plus
c’est important
Recherche de
l’exécution optimale
dans le marché
immature des crypto-
actifs
Optimiser l’exécution
d’algorithmes sur les
marchés des cryptoactifs
en prenant en compte des
contraintes du marché dans
le développement des
algorithmes et l’égal
traitement des parties. Frais
d’exécution sont
primordiaux pour les gros
turn over, basse fréquence,
Difficulté de modéliser le cours des
cryptomonnaies à cause de l’immaturité
des marchés des crypto-monnaies
caractérisée par :
le manque de régulation, les montées et
les chutes brutales de la monnaie, le peu
de diversité d’ordres, des structures de
frais archaïques, etc…
Difficulté d’assurer la meilleure exécution
des algorithmes dans un contexte de
marché à faible liquidité
Développement d’un outil de simulation
d’évaluation multicritères des algorithmes
Vision d’ensemble
4. External Data Provider
REST API
Strategy transaction & Logs emission
Regulators
Indexes checkers
Napoleon customer
to get financial advises
NAPOLEON
Data
REST API
Values publishing
Feed prices
NAPOLEON Index publishing
REST API
R&D Napoleon Toolbox Connector
Napoleon sales force
Demonstrating
Notebooks
Napoleon
R&D server
• Database of Napoleon proprietary features
• Library of existing devised strategies
• Visualization/management notebook
Infrastructure IT R&D pour Napoléon AM
Napoleon
R&D Toolbox
• Features generation algorithms
• Standard financial algorithms
• Advanced strategies mixing algorithms
5. Un serveur de notebook pour la visualisation des résultats
Napoleon
R&D server
• Library of existing devised strategies
• Visualization/management notebook
• Analytique disponible sur le serveur via la
napoleon toolbox
• Capacité de visualisation et investigation
6. Constitution d’une base de stratégies primaires Napoléon AM
Napoléon AM a développé des stratégies primaires au cours de dizaines d’années d’expérience en tant qu’asset manager professionnel pour toutes les classes
d’actifs fiat et cryptos :
• Equity indices
• Taux/obliigations
• Commodité
• Crypto actifs
Ces stratégies sont adaptées au sous-jacent/marché et sont construites à partir de la calibration de processus stochastique et de tests statistiques, qui génèrent
un signal prévoyant l’évolution du sous-jacent. Elles peuvent se ranger en trois catégories:
• Momentum/trend following
• Mean reverting
• Buy the dip
Ces stratégies primaires sont les briques sur lesquelles notre moteur d’allocation d’intelligence artificielle va calculer des poids d’allocation optimale et définir
un indice blend de ces différentes stratégies.
Evolution from Napo signal 𝑜𝐼
𝑡
:𝐼𝑡 = 𝐼𝑡−1 ∗ (1 + 𝑜𝐼
𝑡
∗ 𝑅𝑢𝑛𝑑𝑒𝑟𝑙𝑦𝑖𝑛𝑔
𝑡
)
Napoleon AM proprietary strategy and matching signal : 𝐼𝑡, 𝑜𝐼
𝑡
7 S&P 500 Napoleon AM proprietary strategies: 𝐼1, 𝐼2, 𝐼3, 𝐼4, 𝐼5, 𝐼6, 𝐼7
7. La première étape est la découverte et collection de données non travaillées chez les fournisseurs financiers professionnels (Bloomberg,Reuters).
Cette donnée se décline dans les catégories suivantes:
• Indicateurs macro-économiques (US, Asie, Europe): manufacturiels avancés, immobiliers, inflation, chômage, création monétaire
• Indicateurs de marché : bull/bear indicateur, volatilité, métriques encours produits dérivés
• Indicateurs taux/commodités : différentes mâturités, spread
• Indicateurs de type signal : stratégies primaires Napoléon, données de marché des indices mondiaux
Cette étape consiste en une mise à jour quotidienne des indicateurs via un processus de gestion de la qualité de la donnée (harmonisation des fréquences
d’apparition, gestion de la donnée manquante).
Cette étape est suivi d’un processing de ces séries financières pour en extraire une valeur financière prédictive.
La nécessité de ce premier processing naît du fait que les financiers s’accordent sur des consensus autour de la valeur indicative de calculs dérivés de ces
données brutes. Il provient d’une large expérience sur les marchés financiers en tant qu’asset managers professionnels. Des librairies open-source éprouvées
sont utilisées ici pour le calcul d’indicateurs financiers (scikit-learn, https://ta-lib.org/). Il couvre un large panel de méthodologies:
• Normalisation, z-score, désaisonnalisation, écart à la tendance, stationnarisation
• Indicateurs financiers standards (macd, rsi, %R, oscillateurs, etc…)
Une troisième étape à ce processing s’ajoute, qui se compose de méthodes type data-science pour une extraction d’indicateurs de marché avancés.
Cette étape se réalise à l’aide de tableurs avancés excel/python-panda. Les indicateurs primaires créés ci-dessus sont utilisés pour détecter des états de marché
via un apprentissage non-supervisé. Des périodes temporelles disjointes où les indicateurs primaires exhibent des comportements similaires sont extraites et
définis comme des états de marché. Ces états de marché deviennent des indicateurs à part entière :
• Détection d’états de marché, clustering non supervisé
• Création d’indicateur coupe-circuit, risk on/off
Les différentes techniques utilisés pour l’apprentissage non supervisé se rangent de la simple détection de seuil (traitement du signal fft) à du clustering
avancé d’apprentissage profond par autoencoder en passant par les clusterings standards hiérarchiques et k-mean.
Constitution de la base Napoléon propriétaire d’indicateurs
à but d’apprentissage
8. Caractéristiques de marché
• Indicateurs macro-économiques (US, Asie, Europe):
manufacturiels avancés, immobiliers, inflation,
chômage, création monétaire
• Indicateurs de marché : bull/bear indicateur,
volatilité, métriques encours produits dérivés
• Indicateurs taux/commodités : différentes mâturités,
spread , or, pétrole
• Indicateurs de type signal : stratégies primaires
Napoléon, données de marché des indices mondiaux
Constitution de la base Napoléon propriétaire d’indicateurs
à but d’apprentissage
Processing financier
• Echantillonage, qualité de la donnée
• Normalisation/stationnarisation
• Indicateur financiers (macd, rsi, …)
Processing data science
• Détection d’états de marché
• Indicateurs coupe-circuit
• Détection d’outliers
Napoleon AM
indicateurs
propriétaires
9. Création d’un framework d’apprentissage
pour la prévision d’une allocation optimale
Apprentissage supervisé pour une allocation optimale 100% systématique :
Nos algorithmes de composition bâsés sur l’intelligence artificielle font partie intégrante de l’effort de recherche de Napoleon AM. Ils
permettent d’associer ensemble des strategies déjà performantes et de bénéficier de leur decorrelation pour obtenir un “blend” avec
des caractéristiques financiières encore plus attrayantes. Notre framework d’intelligence artificielle est un framework d’apprentissage
supervisé, où la supervision vient du future à travers un critère d’utilité (Sharpe/Calmar ratio, drawdown). Cette supervision disponible
a posteriori (cadre IA d’apprentissage supervisé) permet de concevoir des algorithmes qui apprennent à prévoir des allocations
optimales sur nos strategies primaires à partir de l’état du monde représenté par notre base propriétaire d’indicateurs.
Cadre de travail de backtesting:
Lors de l’implementation d’une algorithme de modélisation supervisée, on doit définir un ensemble d’entraînement et un ensemble de
test où la prediction se réalisera. Ici nous visons à realiser un backtest sur toutes les périodes avec un algorithme évolutif prenant en
compte le caractère changeant des marchés financiers. L’algorithme de prédiction roulera donc dans le temps, où à chaque date de
rebalancement, il apprendra sur une période d’entraînement dans le passé et prédira la meilleure allocation à venir pour une période
de prediction définie jusqu’au prochain rebalancement. Les backtests présentés dans ce papier sont donc totalement “out of sample »
et sont totalement réplicables sur de vrais marchés financiers en y incorporant des frais de transaction et la prise en compte de décalage
des marchés financiers.
Tester et trouver la configuration optimale:
L’agnosticité de notre framework de backtesting en terme d’algorithme et de méta paramètres (taille de période de rebalancement,
d’entrainment, paramètres initiaux aux rebalancements) nous permet de tester exhaustivement de multiples
configurations/algorithmes et de sélectionner la meilleure configuration.
10. Apprentissage supervisé pour une allocation optimale
𝒕𝒏
1 t+pred_size
t
10
t
t-train_size
• Le framework AI développé à Napoleon AM est un framework d’apprentissage supervisé pour la prédiction de séries temporelles.
• A chaque date de rebalancement, notre algorithme prévoit l’allocation optimale(𝑤1
∗
, … , 𝑤𝑛
∗
) à partir d’indicateurs propriétaires
• L’allocation est optimale au sens d’une fonction d’utilité financière (Sharpe ratio, Calmar ratio, etc..)
• La supervisation est obtenue car les stratégies primaires sont connues dans le passé et de simples routines d’optimisation permettent de calculer
(𝒘𝟏
∗
, … , 𝒘𝒏
∗
) l’allocation optimale a posteriori.
• L’algorithme entraîné à partir du passé permet de prédire l’allocation optimale a priori 𝒘𝟏, … , 𝒘𝒏 pour la future période de rebalancement
• Ce cadre de travail est indépendant de l’alogrithme choisi, ainsi que du sous-ensemble d’indicateurs choisis pour effectuer l’apprentissage et la prédiction
Predicted
weights
Training
days
w1, … , wn
Past
𝒕𝒏 + 𝒑𝒓𝒆𝒅_𝒔𝒊𝒛𝒆
𝒕𝒏 + 𝒕𝒓𝒂𝒊𝒏_𝒔𝒊𝒛𝒆
Optimal
weights
Training
days
(𝑤1
∗
, … , 𝑤𝑛
∗
)
Optimal
weights
Training
days
(𝑤1
∗
, … , 𝑤𝑛
∗
)
Optimal
weights
Training
days
(𝑤1
∗
, … , 𝑤𝑛
∗
)
Future
Création de données de
supervisation
• Bibliothèque de procédures d’optimisation
• Allocation optimale selon utilité (drawdown, sharpe,
calmar)
• Rendement à venir pour différents horizon
Marché
supervision
11. Environnement de Backtesting
L’indépendance de notre backtesting framework à l’algorithme de prévision et à la base d’apprentissage donne lieu à un vaste domaine de recherche pour la
sélection du meilleur algorithme et des meilleurs prédicteurs. Pour se comparer aux standards de l’industrie, nous avons dans un premier temps implémenté une
bibliothèque des principaux algorithmes d’allocation éprouvés par l’industrie financière:
• Equally Risk Contribution
• Hierarchical Risk Parity
• Maximum Diversification
Fort des évolutions de l’intelligence artificielle et des algorithmes de type apprentissage profond open-source, une partie de l’effort de recherche et
développement s’est tourné vers l’implémentation et le test d’algorithmes d’intelligence artificielle.
• Apprentissage profond perceptron
• Réseaux de neurones récurrents (LSTM : Long/short term memory)
• Forêts aléatoires boostées
La calibration des paramètres des algorithmes et du backtesting est un sujet de recherche à part entière. Nous avons mis en place une boîte à outil qui permet de
tester exhaustivement toutes les configurations possible. La configuration optimale à laquelle les efforts de recherche ont abouti est la suivante :
• A chaque date de rebalancement, l’algorithme est réentraîné sur les données les plus récentes avec une taille fixée optimale dans le passé et en prenant
comme configuration initiale les poids optimaux précédents (rebalancing standard en finance)
• Les paramètres de l’algorithme sont fixés au démarrage du backtest (méta paramètres : utilité, fréquence de rebalancement, architecture de réseau).
De nombreuses différentes configurations ont été testées et rejetées:
• Période d’entraînement dont la taille grandit avec le passé disponible (expanding training)
• Introduction d’aléa dans les valeurs de depart pour sortir des minimas locaux (type algorithme génétique)
• Recalibration des paramètres à chaque date de rebalancement (le modèle et les méta-paramètres changent dans le temps)
• Sélection de sous-ensembles de prédicteurs pour construire plusieurs modèles et faire de l’”ensembling”
Ces configurations dependent bien sûr de l’algorithme choisi et ont été fixé pour l’algorithme choisi pour le fond Dynamix de Napoléon AM.
Mais à chaque conception d’un nouvel algorithme, elles sont de nouveau toutes testées et la meilleure approche est sélectionnée.
12. Cadre de travail pour le développement
et backtesting d’algorithmes prédictifs
Napoleon
blend
indices
Napoleon
Backtesting framework
• Bibliothèque complète d’algorirthmes
• Algos financiers standards
• Equally Risk Contribution
• Hierarchical Risk Parity
• Maximum Diversification
• Algos de prédiction de series temporelles
• Deep learning perceptron
• Long/short term memory
• Forêts aléatoires boostées
• Tuning des algorithmes par recherche exhaustive
des meilleurs paramètres
• Ensembling de modèles
Napoleon
indicateurs
propriétaires
Marché
past
supervisation
13. Des résultats de comparaison entre l’intelligence artificielle et les
algorithmes financiers standards
14. Caractéristiques de marché
non travaillées
• Caractéristiques macro-économiques
• AVERAGE_usloans
• AVERAGE_usrealestate
• AVERAGE_euleadgrowth
• AVERAGE_eucredijob
• AVERAGE_japanleadinggrowth
• AVERAGE_chinagrowth
• AVERAGE_usinflation
• Caractéristiques de marché
• SCGRRAI
• AAIIBULL
• AAIIBEAR
• GSUSFCI
• VIX
• V2X
• SKEW
• PUT/Call
• NYSE High / Low
• Caractéristiques taux/commodité
• DXY
• BCOMGC
• BCOMCL
• US 2/10
• US10
• GER 2/10
• GER10
• Credit Spread US
• Credit Spread EU
• Caractéristiques prix/signaux napoléon
• Napoleon strategies
• Napoleon shortcut signals
• World wide indexes OHLC
Constitution de la base Napoléon propriétaire d’indicateurs
à but d’apprentissage
Processing financier
séries temporelles
• Echantillonage
• Stationnarisation/normalisation
• Indicateur financiers (macd,rsi, ...)
Processing avancé data science
• Apprentissage non supervisé/clustering pour
détecter phase de marché
• Transformée de Fourier/Ondelettes
• Création d’indicateur coupe-circuit
• Sélection des meilleurs prédicteurs
• Importance des prédicteurs par catégorie
Napoleon
indicateurs
propriétaires
Création de données de
supervisation
• Bibliothèque de procédures d’optimisation
• Allocation optimale selon utilité (drawdown, sharpe,
calmar)
• Rendement à venir pour différents horizon
Marché
supervisation
Création
stratégies primaires
• Trend following
• mean reverting
• buy the dip
Napoleon
proprietary
strategies