Triton Quantitative Trading
Quant
Date Joined
13th May, 2024
Bootcamp progress
0% Complete
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2026-02-25 06:17:32
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2026-02-20 03:08:15
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2024-05-13 19:14:55
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2024-05-15 20:43:59
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Activity on QuantConnect
This section highlights your contributions and engagement across the QuantConnect platform — including backtests, live trades, published research, and community involvement through comments and threads. It reflects your overall activity as part of the QuantConnect community.
Public Backtests (5)
View More150.181Net Profit
12.726PSR
0.509Sharpe Ratio
0.02Alpha
1.998Beta
20.122CAR
52.1Drawdown
-0.46Loss Rate
62Parameters
1Security Types
0Tradeable Dates
718Trades
0.077Treynor Ratio
0.73Win Rate
149.822Net Profit
12.68PSR
0.508Sharpe Ratio
0.02Alpha
1.999Beta
20.087CAR
52.2Drawdown
-0.45Loss Rate
62Parameters
1Security Types
0Tradeable Dates
719Trades
0.077Treynor Ratio
0.73Win Rate
3.093Net Profit
45.024PSR
0.782Sharpe Ratio
0.099Alpha
0.198Beta
9.656CAR
5.9Drawdown
-0.68Loss Rate
75Parameters
1Security Types
84Tradeable Dates
65Trades
0.325Treynor Ratio
0.49Win Rate
4.416Net Profit
0.848PSR
-0.361Sharpe Ratio
-0.014Alpha
-0.231Beta
0.868CAR
19.7Drawdown
-0.69Loss Rate
71Parameters
1Security Types
1255Tradeable Dates
487Trades
0.126Treynor Ratio
0.71Win Rate
239.059Net Profit
29.426PSR
0.727Sharpe Ratio
0.078Alpha
1.57Beta
27.643CAR
38Drawdown
-0.36Loss Rate
63Parameters
1Security Types
0Tradeable Dates
660Trades
0.112Treynor Ratio
0.83Win Rate
Community
View MoreTriton submitted the research LPPLS for Bubbles in Speculative Markets
Abstract
This project implements the Log-Periodic Power Law Singularity (LPPLS) model in QuantConnect to detect speculative bubbles in high-liquidity equities and anticipate crash windows. LPPLS captures the shift from near-linear growth to super-exponential acceleration with increasingly frequent volatility oscillations as prices approach a critical time \(t_c\). To improve robustness and speed, the LPPLS equation is re-parameterized so four coefficients \((A,B,C_1,C_2)\) are solved via Ordinary Least Squares, leaving only \((t_c,m,\omega)\) for nonlinear optimization. A dual-EMA regime filter is integrated to gate signals to appropriate momentum states, reducing false positives and improving deployability in modern hype-driven markets.
150.181Net Profit
12.726PSR
0.509Sharpe Ratio
0.02Alpha
1.998Beta
20.122CAR
52.1Drawdown
-0.46Loss Rate
62Parameters
1Security Types
0Tradeable Dates
718Trades
0.077Treynor Ratio
0.73Win Rate
149.822Net Profit
12.68PSR
0.508Sharpe Ratio
0.02Alpha
1.999Beta
20.087CAR
52.2Drawdown
-0.45Loss Rate
62Parameters
1Security Types
0Tradeable Dates
719Trades
0.077Treynor Ratio
0.73Win Rate
3.093Net Profit
45.024PSR
0.782Sharpe Ratio
0.099Alpha
0.198Beta
9.656CAR
5.9Drawdown
-0.68Loss Rate
75Parameters
1Security Types
84Tradeable Dates
65Trades
0.325Treynor Ratio
0.49Win Rate
4.416Net Profit
0.848PSR
-0.361Sharpe Ratio
-0.014Alpha
-0.231Beta
0.868CAR
19.7Drawdown
-0.69Loss Rate
71Parameters
1Security Types
1255Tradeable Dates
487Trades
0.126Treynor Ratio
0.71Win Rate
239.059Net Profit
29.426PSR
0.727Sharpe Ratio
0.078Alpha
1.57Beta
27.643CAR
38Drawdown
-0.36Loss Rate
63Parameters
1Security Types
0Tradeable Dates
660Trades
0.112Treynor Ratio
0.83Win Rate
Triton submitted the research LPPLS for Bubbles in Speculative Markets
Abstract
This project implements the Log-Periodic Power Law Singularity (LPPLS) model in QuantConnect to detect speculative bubbles in high-liquidity equities and anticipate crash windows. LPPLS captures the shift from near-linear growth to super-exponential acceleration with increasingly frequent volatility oscillations as prices approach a critical time \(t_c\). To improve robustness and speed, the LPPLS equation is re-parameterized so four coefficients \((A,B,C_1,C_2)\) are solved via Ordinary Least Squares, leaving only \((t_c,m,\omega)\) for nonlinear optimization. A dual-EMA regime filter is integrated to gate signals to appropriate momentum states, reducing false positives and improving deployability in modern hype-driven markets.
Open Quant League
The Open-Quant League is a quarterly competition between universities and investment clubs for the best-performing strategy. The previous quarter's code is open-sourced, and competitors must adapt to survive.
Get this certificate by participating in our Open Quant League
Open Quant League
The Open-Quant League is a quarterly competition between universities and investment clubs for the best-performing strategy. The previous quarter's code is open-sourced, and competitors must adapt to survive.
Get this certificate by participating in our Open Quant League
Open Quant League
The Open-Quant League is a quarterly competition between universities and investment clubs for the best-performing strategy. The previous quarter's code is open-sourced, and competitors must adapt to survive.
Get this certificate by participating in our Open Quant League
Open Quant League
The Open-Quant League is a quarterly competition between universities and investment clubs for the best-performing strategy. The previous quarter's code is open-sourced, and competitors must adapt to survive.
Get this certificate by participating in our Open Quant League
Open Quant League
The Open-Quant League is a quarterly competition between universities and investment clubs for the best-performing strategy. The previous quarter's code is open-sourced, and competitors must adapt to survive.
Get this certificate by participating in our Open Quant League
Open Quant League
The Open-Quant League is a quarterly competition between universities and investment clubs for the best-performing strategy. The previous quarter's code is open-sourced, and competitors must adapt to survive.
Get this certificate by participating in our Open Quant League
Open Quant League
The Open-Quant League is a quarterly competition between universities and investment clubs for the best-performing strategy. The previous quarter's code is open-sourced, and competitors must adapt to survive.
Get this certificate by participating in our Open Quant League
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