Gurumeher Sawhney
Software Engineer; Quant Enthusiast
Date Joined
13th April, 2017
Bootcamp progress
8% Complete
Achievements
Algorithm Sharing
Share an algorithm with the community by making your algorithm public.
2018-06-07 02:17:47
Notebook Sharing
Share a notebook with others.
2018-06-20 18:44:39
Chatter Box
Leave at least 100 comments on community posts.
2018-07-18 14:06:49
Backtester
Run at least 100 backtests on QuantConnect's cloud.
2018-06-27 22:00:03
Live Trading
Deploy a live trading algorithm using one of QuantConnect's supported brokerages.
2018-06-27 14:11:52
Cloner
Clone at least 100 algorithms from the community.
2019-01-07 16:46:11
Popular
Receive at least 10 upvotes from different users in the community.
2018-07-14 00:32:59
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Activity on QuantConnect
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Public Backtests (95)
View More0Net Profit
0Sharpe Ratio
0Alpha
0Beta
0CAR
0Drawdown
0Loss Rate
12Parameters
1Security Types
0Sortino Ratio
2967Tradeable Dates
0Trades
0Treynor Ratio
0Win Rate
0Net Profit
0Sharpe Ratio
0Alpha
0Beta
0CAR
0Drawdown
0Loss Rate
0Parameters
1Security Types
0Sortino Ratio
5Tradeable Dates
0Trades
0Treynor Ratio
0Win Rate
0Net Profit
0Sharpe Ratio
0Alpha
0Beta
0CAR
0Drawdown
0Loss Rate
1Security Types
0Sortino Ratio
5Tradeable Dates
0Trades
0Treynor Ratio
0Win Rate
-7.972Net Profit
-1.957Sharpe Ratio
-0.333Alpha
-4.997Beta
-36.966CAR
10.1Drawdown
0Loss Rate
1Security Types
0Sortino Ratio
45Tradeable Dates
1Trades
0.086Treynor Ratio
0Win Rate
-2.832Net Profit
-0.522Sharpe Ratio
-0.019Alpha
0.7Beta
-0.954CAR
4.7Drawdown
100Loss Rate
1Security Types
0Sortino Ratio
927Tradeable Dates
2Trades
-0.011Treynor Ratio
0Win Rate
Community
View MoreGurumeher submitted the research Paired Switching
Abstract
Abstract: Paired switching is an investment strategy that involves periodically switching positions between two assets that are negatively correlated. The goal is to improve the performance of a portfolio by capitalizing on the relative performance of the two assets. This strategy assumes that a mixed portfolio of the two assets would result in lower returns compared to holding the assets individually. The method involves analyzing the historical prices of the two assets on a quarterly basis and buying the asset that has a higher return during the period. The position is held for one quarter before repeating the analysis. This abstract summarizes the concept of paired switching and its methodology.
Gurumeher submitted the research Accrual Anomaly
Abstract
The accrual anomaly strategy aims to determine if a company's earnings are based on real cash inflow or questionable accounting practices. Companies with lower levels of accruals are believed to have more certain earnings and should earn higher market returns. This strategy involves taking a long position in low accrual companies and a short position in high accrual companies. However, implementing this strategy requires fundamental data from the current and past year, making it currently impractical for live trading. The method involves selecting an investment universe with stocks that have fundamental data and analyzing the annual changes in balance sheet data. The portfolio is rebalanced annually in June.
Gurumeher submitted the research Asset Growth Effect
Abstract
The asset growth effect is a phenomenon where stocks with low asset growth outperform stocks with high asset growth. This effect is believed to be driven by two main factors. First, as firms grow, the risk associated with their assets decreases, as assets-in-place replace the need for future investments. Second, there is a market mispricing of growing businesses, as past gains are extrapolated into future growth for high asset growth companies. This strategy involves taking a long position in low asset growth companies and a short position in high asset growth companies. It requires fundamental data from the current and past year for analysis. The strategy is implemented by creating an investment universe of non-financial U.S. stocks and rebalancing the portfolio annually.
Gurumeher submitted the research Momentum Effect In REITs
Abstract
The momentum effect is a phenomenon that suggests that assets that have been performing well in the past are likely to continue performing well in the near future. In the case of REITs, studies have shown that REITs with strong past performance tend to outperform those with weaker performance. This strategy aims to capitalize on this momentum effect by taking long positions in REITs with the highest momentum and rebalancing the portfolio on a quarterly basis. The method involves selecting a universe of stocks based on certain criteria, such as price, fundamental data, and liquidity. The strategy is supported by research from Quantpedia on the momentum effect in REITs.
Gurumeher started the discussion RSI and Resistance Strategy
Hi,
0Net Profit
0Sharpe Ratio
0Alpha
0Beta
0CAR
0Drawdown
0Loss Rate
12Parameters
1Security Types
0Sortino Ratio
2967Tradeable Dates
0Trades
0Treynor Ratio
0Win Rate
0Net Profit
0Sharpe Ratio
0Alpha
0Beta
0CAR
0Drawdown
0Loss Rate
0Parameters
1Security Types
0Sortino Ratio
5Tradeable Dates
0Trades
0Treynor Ratio
0Win Rate
0Net Profit
0Sharpe Ratio
0Alpha
0Beta
0CAR
0Drawdown
0Loss Rate
1Security Types
0Sortino Ratio
5Tradeable Dates
0Trades
0Treynor Ratio
0Win Rate
-7.972Net Profit
-1.957Sharpe Ratio
-0.333Alpha
-4.997Beta
-36.966CAR
10.1Drawdown
0Loss Rate
1Security Types
0Sortino Ratio
45Tradeable Dates
1Trades
0.086Treynor Ratio
0Win Rate
-2.832Net Profit
-0.522Sharpe Ratio
-0.019Alpha
0.7Beta
-0.954CAR
4.7Drawdown
100Loss Rate
1Security Types
0Sortino Ratio
927Tradeable Dates
2Trades
-0.011Treynor Ratio
0Win Rate
0Net Profit
0Sharpe Ratio
0Alpha
0Beta
0CAR
0Drawdown
0Loss Rate
1Security Types
0Sortino Ratio
23Tradeable Dates
0Trades
0Treynor Ratio
0Win Rate
2.625Net Profit
0.167Sharpe Ratio
0.132Alpha
-5.634Beta
1.249CAR
20.7Drawdown
0Loss Rate
1Security Types
0Sortino Ratio
524Tradeable Dates
3Trades
-0.003Treynor Ratio
100Win Rate
117.196Net Profit
0.906Sharpe Ratio
0.037Alpha
6.649Beta
16.459CAR
17.8Drawdown
0Loss Rate
1Security Types
0Sortino Ratio
1281Tradeable Dates
1Trades
0.026Treynor Ratio
0Win Rate
-1.052Net Profit
-5.779Sharpe Ratio
0Alpha
-47.909Beta
-61.892CAR
2.4Drawdown
90Loss Rate
1Security Types
-0.019115331671902Sortino Ratio
4Tradeable Dates
268Trades
0.011Treynor Ratio
10Win Rate
0Net Profit
0Sharpe Ratio
0Alpha
0Beta
0CAR
0Drawdown
0Loss Rate
1Security Types
0Sortino Ratio
53Tradeable Dates
0Trades
0Treynor Ratio
0Win Rate
0Net Profit
0Sharpe Ratio
0Alpha
0Beta
0CAR
0Drawdown
0Loss Rate
1Security Types
0Sortino Ratio
5Tradeable Dates
0Trades
0Treynor Ratio
0Win Rate
-0.178Net Profit
-1.781Sharpe Ratio
0.308Alpha
-35.942Beta
-12.778CAR
0.8Drawdown
0Loss Rate
1Security Types
0Sortino Ratio
5Tradeable Dates
1Trades
0.002Treynor Ratio
0Win Rate
0Net Profit
0Sharpe Ratio
0Alpha
0Beta
0CAR
0Drawdown
0Loss Rate
1Security Types
0Sortino Ratio
5Tradeable Dates
0Trades
0Treynor Ratio
0Win Rate
0Net Profit
0Sharpe Ratio
0Alpha
0Beta
0CAR
0Drawdown
0Loss Rate
2Security Types
0Sortino Ratio
8Tradeable Dates
0Trades
0Treynor Ratio
0Win Rate
0Net Profit
0Sharpe Ratio
0Alpha
0Beta
0CAR
0Drawdown
0Loss Rate
2Security Types
0Sortino Ratio
8Tradeable Dates
0Trades
0Treynor Ratio
0Win Rate
0.285Net Profit
6.322Sharpe Ratio
0Alpha
17.339Beta
58.586CAR
0Drawdown
0Loss Rate
1Security Types
0Sortino Ratio
2Tradeable Dates
1Trades
0.01Treynor Ratio
0Win Rate
0Net Profit
0Sharpe Ratio
0Alpha
0Beta
0CAR
0Drawdown
0Loss Rate
1Security Types
0Sortino Ratio
1718Tradeable Dates
0Trades
0Treynor Ratio
0Win Rate
27.786Net Profit
0.588Sharpe Ratio
-0.01Alpha
1.587Beta
2.108CAR
4.9Drawdown
0Loss Rate
1Security Types
0Sortino Ratio
2957Tradeable Dates
14Trades
0.014Treynor Ratio
100Win Rate
37.037Net Profit
1.102Sharpe Ratio
1.227Alpha
-30.668Beta
58.422CAR
34.7Drawdown
0Loss Rate
1Security Types
0Sortino Ratio
171Tradeable Dates
14Trades
-0.02Treynor Ratio
100Win Rate
6.968Net Profit
0.218Sharpe Ratio
0.039Alpha
-1.548Beta
0.721CAR
7.9Drawdown
40Loss Rate
2Security Types
0.24101807955312Sortino Ratio
3421Tradeable Dates
110Trades
-0.005Treynor Ratio
60Win Rate
48.548Net Profit
0.822Sharpe Ratio
0.082Alpha
-0.208Beta
7.585CAR
13.7Drawdown
0Loss Rate
1Security Types
0Sortino Ratio
1364Tradeable Dates
11Trades
-0.372Treynor Ratio
100Win Rate
0Net Profit
0Sharpe Ratio
0Alpha
0Beta
0CAR
0Drawdown
0Loss Rate
1Security Types
0Sortino Ratio
129Tradeable Dates
0Trades
0Treynor Ratio
0Win Rate
-2.012Net Profit
-0.044Sharpe Ratio
1.025Alpha
-54.928Beta
-11.277CAR
14.7Drawdown
0Loss Rate
1Security Types
0Sortino Ratio
44Tradeable Dates
1Trades
0Treynor Ratio
0Win Rate
1Security Types
147Tradeable Dates
1.462Net Profit
0.083Sharpe Ratio
-0.027Alpha
1.674Beta
0.26CAR
9.1Drawdown
65Loss Rate
1Security Types
-0.030858117859397Sortino Ratio
1406Tradeable Dates
3015Trades
0.002Treynor Ratio
35Win Rate
0Net Profit
0Sharpe Ratio
0Alpha
0Beta
0CAR
0Drawdown
0Loss Rate
1Security Types
0Sortino Ratio
22Tradeable Dates
0Trades
0Treynor Ratio
0Win Rate
0Net Profit
0Sharpe Ratio
0Alpha
0Beta
0CAR
0Drawdown
0Loss Rate
1Security Types
0Sortino Ratio
147Tradeable Dates
0Trades
0Treynor Ratio
0Win Rate
78.55Net Profit
0.218Sharpe Ratio
0.001Alpha
1.924Beta
2.853CAR
52.4Drawdown
38Loss Rate
2Security Types
1.8133745841417Sortino Ratio
7521Tradeable Dates
16Trades
0.014Treynor Ratio
62Win Rate
293.959Net Profit
0.613Sharpe Ratio
-0.001Alpha
4.883Beta
9.183CAR
16Drawdown
8Loss Rate
2Security Types
0Sortino Ratio
5696Tradeable Dates
19Trades
0.014Treynor Ratio
92Win Rate
214.489Net Profit
0.597Sharpe Ratio
0Alpha
3.991Beta
7.618CAR
18.6Drawdown
11Loss Rate
2Security Types
0Sortino Ratio
5696Tradeable Dates
15Trades
0.014Treynor Ratio
89Win Rate
Gurumeher submitted the research Paired Switching
Abstract
Abstract: Paired switching is an investment strategy that involves periodically switching positions between two assets that are negatively correlated. The goal is to improve the performance of a portfolio by capitalizing on the relative performance of the two assets. This strategy assumes that a mixed portfolio of the two assets would result in lower returns compared to holding the assets individually. The method involves analyzing the historical prices of the two assets on a quarterly basis and buying the asset that has a higher return during the period. The position is held for one quarter before repeating the analysis. This abstract summarizes the concept of paired switching and its methodology.
Gurumeher submitted the research Accrual Anomaly
Abstract
The accrual anomaly strategy aims to determine if a company's earnings are based on real cash inflow or questionable accounting practices. Companies with lower levels of accruals are believed to have more certain earnings and should earn higher market returns. This strategy involves taking a long position in low accrual companies and a short position in high accrual companies. However, implementing this strategy requires fundamental data from the current and past year, making it currently impractical for live trading. The method involves selecting an investment universe with stocks that have fundamental data and analyzing the annual changes in balance sheet data. The portfolio is rebalanced annually in June.
Gurumeher submitted the research Asset Growth Effect
Abstract
The asset growth effect is a phenomenon where stocks with low asset growth outperform stocks with high asset growth. This effect is believed to be driven by two main factors. First, as firms grow, the risk associated with their assets decreases, as assets-in-place replace the need for future investments. Second, there is a market mispricing of growing businesses, as past gains are extrapolated into future growth for high asset growth companies. This strategy involves taking a long position in low asset growth companies and a short position in high asset growth companies. It requires fundamental data from the current and past year for analysis. The strategy is implemented by creating an investment universe of non-financial U.S. stocks and rebalancing the portfolio annually.
Gurumeher submitted the research Momentum Effect In REITs
Abstract
The momentum effect is a phenomenon that suggests that assets that have been performing well in the past are likely to continue performing well in the near future. In the case of REITs, studies have shown that REITs with strong past performance tend to outperform those with weaker performance. This strategy aims to capitalize on this momentum effect by taking long positions in REITs with the highest momentum and rebalancing the portfolio on a quarterly basis. The method involves selecting a universe of stocks based on certain criteria, such as price, fundamental data, and liquidity. The strategy is supported by research from Quantpedia on the momentum effect in REITs.
Gurumeher started the discussion RSI and Resistance Strategy
Hi,
Gurumeher started the discussion Ramadan Effect (Maybe Can Extrapolate)
Research suggests that the euphoria derived from Ramadan could influence investor behavior in...
Gurumeher started the discussion Using Live Trading for Signals/Notifications (NOT TRADES)
Recently I have been scalp trading on a brokerage that isn't supported by QuantConnect (BitSeven)...
Gurumeher left a comment in the discussion Stock universe filter with moving average
Hey Marc, take a look at the docs here. Filtering the universe by a technical indicator is possible...
Gurumeher left a comment in the discussion Bollinger Band problem
Hey Patrik, the problem was in the initialization of the BollingerBands object. The parameter k was...
Gurumeher left a comment in the discussion Consolidators and Scheduled Events
Hi Nitesh, try taking a look at the OnDataConsolidated event handler. Once the bar is created, the...
Gurumeher left a comment in the discussion RSI Strategy Query
Hi Yao, I was able to draft up a quick algorithm. You can see in the backtest below I implemented...
Gurumeher left a comment in the discussion Error in Scheduling
Hi Georgios, the error comes from the line:
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Gurumeher left a comment in the discussion How to define a security to use based on date/time?
Hi Sanjay, the self.Time property can be useful for this algorithm. The self.Time property is the...
6 years ago