Quantitative credit trading strategy

For example, this product link here. John Matok 1 1 1 bronze badge. Leakage and bias in XGBoost trading strategy I apologize for my persistence, i'm on a course of study and doubts increase every day. My goal is "just" to code a profitable forex trading strategy with machine learning.

Algorithmic trading

I'm trying to Federico Juvara 11 2 2 bronze badges. ABK 9 9 bronze badges. How is the total return of an alpha strategy being calculated during backtesting? I am using a quant simulation platform and I have chosen a formulaic alpha to be used. Now the platform is backtesting and displaying the total return of the alpha strategy over 12 years. The trading Backtesting Period Effect I am backtesting a stock trading strategy.

I tested it over two time periods: and and compared the results against a buy and hold strategy. To be clear, I only changed backtesting Stat 3 3 bronze badges. Optimize Bollinger Bands Strategy I was proving a very simple strategy with Bollinger Bands for a intraday timeframe 1 minute that buy on lower band and sell in a higher band Very common strategy , but in backtesting in E-Mini SP Caeta 2 2 bronze badges. Sharpe Ratio Graphed Over Time I looked and could not find a suitable answer to my question already, so: What is the best way to calculate the Sharpe Ratio over time, given I have about a decade's worth of 1-minute candlesticks?

Paul McElroy 1 1 bronze badge. For example, in the JPMorgan paper p. Qwerty 5 5 bronze badges. Quantifying Bollinger Band squeeze I'm interested in experimenting with Bollinger Band squeezes to see if a strategy can come of it. A simple definition is a narrowing of the bands like the example below. Really, only the standard SuperCodeBrah 1 1 silver badge 8 8 bronze badges. What's the intuition behind factor grouping?

From the book "Finding Alpha", written by a popular quant fund WorldQuant, explains many techniques about quantitative investing but intentionally omits many of the caveats and applications Can you point me to any resources about a possible framework to analyse and possibly quantify model uncertainty and -robustness associated with quantitative investment models? As an example, there MGL 2 2 silver badges 8 8 bronze badges. Machine learning algorithms that generate trading models literature?

Is there any academic literature on machine learning algorithms that are able to generate functioning trading models? Would this even be feasible at all, now or in the future?

Could you point me to Yass44 11 2 2 bronze badges. Can I use the Sharpe Ratio as an objective function in algorithmic trading? MK23 21 1 1 bronze badge. Can somebody explain and give examples of "signals" in quant investing?

Quant Funds Like RenTech and Cubist Are Building Credit-Focused Teams

What are those? What does this word mean? Are momentum returns negatively skewed?

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In the academic literature, I found that momentum returns are negatively skewed e. Daniel and Moskowitz, As far as I understand, this usually happens when the "past losers" rebound Eaglez 63 4 4 bronze badges. He demonstrates two With an eye toward ride-sharing, Aptiv developed its own autonomous driving capabilities, testing cars in Las Vegas, Nevada, through a partnership with Lyft, a US ride-sharing company.

Building Quant Equity Strategies in Python

After some discussion, the credit analyst and investment-grade credit portfolio manager decided to underweight autos, overruling the factor industry weight recommendations. They implemented this underweight through companies like Aptiv and Fiat Chrysler Automobiles, which they believed were riding the seismic secular trends better than most. Based on these combined variables, the algorithm predicts the future relative performance of our six style factors spanning the value, momentum and quality categories.

For historical context, four months later, in October , the Dow Jones Industrial Average stock index peaked at over 14, points. As economic conditions worsened the following year, the algorithm incrementally increased exposure to Quality factors while decreasing exposure to Equity Momentum and Value. Instead of a static buy-and-hold approach, our gradient-boosting algorithm shifts factor exposures dynamically to better match overarching macroeconomic environments.

The basic concept driving value factors is that cheap bonds i. Our data science team uses three distinct factor calculations that fall under the Value umbrella. The second two factors measure credit ratings too, but then layer return volatility into their risk assessments while controlling for industry cyclicality and spread duration. The Return Volatility factor uses month excess return volatility to measure risks, while the Spread Volatility factor measures three-month spread change for its volatility measure.

Corporate bonds from publicly listed issuers with strong recent equity performance tend to perform well since the bonds are senior to equities in the capital structure. Corporate bonds with especially low probabilities of default can outperform higher yielding credit during credit downturns. Outspoken quants who championed the arrival of factor-based strategies are challenging the status quo—daring active managers to prove their worth. Many active heavyweights are more than ready thrilled in fact to meet this challenge, with some getting their arms around big data and machine learning techniques to sharpen their edge.

By incorporating data science alongside human insights, a simpler two-dimensional process of top-down and bottom-up analysis has morphed into four-dimensional chess that incorporates fundamental research and quantitative science. In the end, the most important skill sets in fixed income remain the ability of trained professionals to explain the underlying economic mechanisms that drive market regimes and the signals that data science can track and analyze. The future of fixed income has already arrived—it lies in successfully marrying quantitative science with fundamental based active management.

Franklin Templeton Fixed Income Group has engineered a seamless active quant approach—where portfolio managers, analysts, traders and data scientists work as one team to create a synergistic loop between quantitative and fundamental analysis.

Thematic insights

We believe marrying our data science and fundamental expertise gives us the insights and competitive edge to navigate challenging investment environments and serve our clients better. Source: Baker, S. Source: J. Bender, R. Briand, D. Melas, and R. Baz, M. Devarajan, M. Hajo, and R. Source: M.

Kolanovic and R. Morgan, May Source: Fama, E. Source: Lee, J. Brownlee, J. Source: S. Gu, B. Kelly and D. There can be no assurance that any model, whether algorithmic, traditional, or otherwise, can predict return. The Sharpe ratio is the average return earned in excess of the risk-free rate per unit of volatility or total risk.

There can be no assurance that adopting this optimization process will have any impact on investment outcomes, or that it will result in profits or that it will minimize losses. Patrick Klein, Ph. Portfolio Manager, Multi-Sector Strategies. Pururav Thoutireddy, Ph. Quantitative Research Analyst. Sonal Desai, Ph. Chief Investment Officer, Portfolio Manager. Franklin Templeton has been among the first to actively invest in many sectors of the fixed income markets as they have evolved—covering corporate credit, mortgage-based securities, asset-backed securities and municipal bonds since the s, international fixed income since the s and bank loans since the early s.

Being part of an established investment group at Franklin Templeton gives the portfolio managers access to experts across different areas of the fixed income market, helping them to diversify opportunities and risks across multiple sectors. Our global reach through Franklin Templeton Investments provides access to additional research, trading, and risk management resources. Portfolio managers have opportunities to exchange insights with other investment groups, and collaborate with an independent risk team that regularly examines risk analytics to help identify and address areas of excessive risk exposure within our portfolios.

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It does not constitute legal or tax advice. The views expressed are those of the investment manager and the comments, opinions and analyses are rendered as at publication date and may change without notice. The information provided in this material is not intended as a complete analysis of every material fact regarding any country, region or market.

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