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Quantitative Developer Intern at QuantConnect | Masters of Financial Mathematics at UChicago

We are pioneering the radical future for open-source quant finance. QuantConnect is the world's largest quant community, empowering 220,000 quants with a framework, data, and infrastructure for their investments.

104.967Net Profit

0.266Sharpe Ratio

0.06Alpha

0.387Beta

3.366CAR

83.3Drawdown

67Loss Rate

0Parameters

1Security Types

-0.0905Sortino Ratio

7914Tradeable Dates

753Trades

0.238Treynor Ratio

33Win Rate

104.967Net Profit

0.266Sharpe Ratio

0.06Alpha

0.387Beta

3.366CAR

83.3Drawdown

67Loss Rate

0Parameters

1Security Types

-0.0905Sortino Ratio

7914Tradeable Dates

753Trades

0.238Treynor Ratio

33Win Rate

0Parameters

1Security Types

7914Tradeable Dates

0Parameters

1Security Types

7914Tradeable Dates

0Net Profit

0Sharpe Ratio

0Alpha

0Beta

0CAR

0Drawdown

0Loss Rate

2Parameters

1Security Types

0Sortino Ratio

127Tradeable Dates

0Trades

0Treynor Ratio

0Win Rate

Alethea submitted the research Improved Momentum Strategy On Commodities Futures

Abstract: This tutorial discusses an improved momentum strategy called TSMOM-CF that addresses weaknesses in traditional time-series momentum strategies. TSMOM-CF incorporates trend strength, uses an efficient volatility estimator, and adds a dynamic leverage mechanism to improve performance. The traditional strategy's oversimplified trading signal, inefficient volatility estimator, and fixed portfolio allocation mechanism are overcome in TSMOM-CF. The paper "Demystifying Time-Series Momentum Strategies: Volatility Estimators, Trading Rules and Pairwise Correlations" by Nick Baltas and Robert Kosowski serves as the basis for the implementation. TSMOM-CF is compared to the basic momentum strategy in the Momentum Effect in Commodities Futures strategy library. The tutorial provides a detailed explanation of the modifications and their impact on performance.

Alethea submitted the research Commodities Futures Trend Following

Abstract: This tutorial explores the implementation of a trend following strategy on commodities futures based on a 2014 paper titled "Two Centuries Of Trend Following". The paper highlights the existence of trends in financial markets, which contradicts the efficient market hypothesis. The strategy involves buying when prices go up and selling when prices go down. The paper extends the backtest period of trend following strategies to two centuries and demonstrates statistically significant excess returns on commodities, currencies, stock indices, and bonds. The tutorial focuses on implementing the strategy on a well-balanced commodities pool consisting of 7 representative contracts. The results of the backtest period show a lower Sharpe ratio compared to SPY's Sharpe ratio, indicating potential limitations of the trend following strategy in this context.

Alethea left a comment in the discussion Noob: Struggling to build Lean with MonoDevelop

Hi DaveGilbert,

Alethea left a comment in the discussion Collaboration

Hi Joe,

104.967Net Profit

0.266Sharpe Ratio

0.06Alpha

0.387Beta

3.366CAR

83.3Drawdown

67Loss Rate

0Parameters

1Security Types

-0.0905Sortino Ratio

7914Tradeable Dates

753Trades

0.238Treynor Ratio

33Win Rate

104.967Net Profit

0.266Sharpe Ratio

0.06Alpha

0.387Beta

3.366CAR

83.3Drawdown

67Loss Rate

0Parameters

1Security Types

-0.0905Sortino Ratio

7914Tradeable Dates

753Trades

0.238Treynor Ratio

33Win Rate

0Parameters

1Security Types

7914Tradeable Dates

0Parameters

1Security Types

7914Tradeable Dates

0Net Profit

0Sharpe Ratio

0Alpha

0Beta

0CAR

0Drawdown

0Loss Rate

2Parameters

1Security Types

0Sortino Ratio

127Tradeable Dates

0Trades

0Treynor Ratio

0Win Rate

38.46Net Profit

0.321Sharpe Ratio

0.032Alpha

0Beta

2.827CAR

31Drawdown

46Loss Rate

0Parameters

1Security Types

0.0577Sortino Ratio

4262Tradeable Dates

2149Trades

-98.982Treynor Ratio

54Win Rate

82.864Net Profit

0.62Sharpe Ratio

0.06Alpha

0.004Beta

5.916CAR

28.5Drawdown

43Loss Rate

0Parameters

1Security Types

0.1187Sortino Ratio

3835Tradeable Dates

1875Trades

13.988Treynor Ratio

57Win Rate

0Parameters

1Security Types

5Tradeable Dates

-8.098Net Profit

-8.611Sharpe Ratio

-3.068Alpha

-0.606Beta

-95.414CAR

10.5Drawdown

0Loss Rate

0Parameters

1Security Types

0Sortino Ratio

5Tradeable Dates

1Trades

3.828Treynor Ratio

0Win Rate

-8.098Net Profit

-8.611Sharpe Ratio

-3.068Alpha

-0.606Beta

-95.414CAR

10.5Drawdown

0Loss Rate

0Parameters

1Security Types

0Sortino Ratio

5Tradeable Dates

1Trades

3.828Treynor Ratio

0Win Rate

3.824Net Profit

1.902Sharpe Ratio

0.611Alpha

-0.367Beta

53.431CAR

8.1Drawdown

50Loss Rate

12Parameters

1Security Types

0.4383Sortino Ratio

22Tradeable Dates

12Trades

-0.966Treynor Ratio

50Win Rate

0Net Profit

0Sharpe Ratio

0Alpha

0Beta

0CAR

0Drawdown

0Loss Rate

7Parameters

1Security Types

0Sortino Ratio

300Tradeable Dates

0Trades

0Treynor Ratio

0Win Rate

0Net Profit

0Sharpe Ratio

0Alpha

0Beta

0CAR

0Drawdown

0Loss Rate

4Parameters

1Security Types

0Sortino Ratio

0Tradeable Dates

0Trades

0Treynor Ratio

0Win Rate

0Net Profit

0Sharpe Ratio

0Alpha

0Beta

0CAR

0Drawdown

0Loss Rate

1Parameters

1Security Types

0Sortino Ratio

9Tradeable Dates

0Trades

0Treynor Ratio

0Win Rate

0Net Profit

0Sharpe Ratio

0Alpha

0Beta

0CAR

0Drawdown

0Loss Rate

1Parameters

1Security Types

0Sortino Ratio

9Tradeable Dates

0Trades

0Treynor Ratio

0Win Rate

0Net Profit

0Sharpe Ratio

0Alpha

0Beta

0CAR

0Drawdown

0Loss Rate

0Parameters

1Security Types

0Sortino Ratio

0Tradeable Dates

0Trades

0Treynor Ratio

0Win Rate

41.78Net Profit

0.529Sharpe Ratio

0.222Alpha

-4.826Beta

10.485CAR

24.8Drawdown

41Loss Rate

6Parameters

1Security Types

0.3219Sortino Ratio

0Tradeable Dates

68Trades

-0.026Treynor Ratio

59Win Rate

41.78Net Profit

0.529Sharpe Ratio

0.222Alpha

-4.826Beta

10.485CAR

24.8Drawdown

41Loss Rate

6Parameters

1Security Types

0.3219Sortino Ratio

0Tradeable Dates

68Trades

-0.026Treynor Ratio

59Win Rate

41.78Net Profit

0.529Sharpe Ratio

0.222Alpha

-4.826Beta

10.485CAR

24.8Drawdown

41Loss Rate

6Parameters

1Security Types

0.3219Sortino Ratio

0Tradeable Dates

68Trades

-0.026Treynor Ratio

59Win Rate

Alethea submitted the research Improved Momentum Strategy On Commodities Futures

Abstract: This tutorial discusses an improved momentum strategy called TSMOM-CF that addresses weaknesses in traditional time-series momentum strategies. TSMOM-CF incorporates trend strength, uses an efficient volatility estimator, and adds a dynamic leverage mechanism to improve performance. The traditional strategy's oversimplified trading signal, inefficient volatility estimator, and fixed portfolio allocation mechanism are overcome in TSMOM-CF. The paper "Demystifying Time-Series Momentum Strategies: Volatility Estimators, Trading Rules and Pairwise Correlations" by Nick Baltas and Robert Kosowski serves as the basis for the implementation. TSMOM-CF is compared to the basic momentum strategy in the Momentum Effect in Commodities Futures strategy library. The tutorial provides a detailed explanation of the modifications and their impact on performance.

Alethea submitted the research Commodities Futures Trend Following

Abstract: This tutorial explores the implementation of a trend following strategy on commodities futures based on a 2014 paper titled "Two Centuries Of Trend Following". The paper highlights the existence of trends in financial markets, which contradicts the efficient market hypothesis. The strategy involves buying when prices go up and selling when prices go down. The paper extends the backtest period of trend following strategies to two centuries and demonstrates statistically significant excess returns on commodities, currencies, stock indices, and bonds. The tutorial focuses on implementing the strategy on a well-balanced commodities pool consisting of 7 representative contracts. The results of the backtest period show a lower Sharpe ratio compared to SPY's Sharpe ratio, indicating potential limitations of the trend following strategy in this context.

Alethea left a comment in the discussion Business days till expiration

Hi Gil,

Alethea left a comment in the discussion Noob: Struggling to build Lean with MonoDevelop

Hi DaveGilbert,

Alethea left a comment in the discussion Collaboration

Hi Joe,

Alethea left a comment in the discussion 'ForexHolding' object has no attribute 'TotalMargin'

Hi Brent,

Alethea left a comment in the discussion Extending Backtesting Data Range

Hi Frost,

Alethea left a comment in the discussion Extending Historical Data

Hi Frost,

Alethea submitted the research Price Earnings Anomaly

The Price to Earnings (P/E) ratio is commonly used by investors to determine the valuation of a company's stock. This discussion explores the Price Earnings Anomaly and its impact on portfolio performance. The strategy involves selecting the 10 stocks with the lowest P/E ratio at the beginning of each year and investing an equal amount of capital in each stock. The portfolio significantly outperforms the benchmark, S&P 500, during the three and a half year backtest period. The strategy also considers the size factor, where small market capitalization stocks are found to outperform large market capitalization stocks. This discussion provides insight into the potential benefits of incorporating the P/E ratio and size factor into investment strategies.

Alethea left a comment in the discussion Business days till expiration

Hi Gil,

4 years ago