Triton Quantitative Trading
Quant
Date Joined
13th May, 2024
Bootcamp progress
0% Complete
Achievements
Algorithm Sharing
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2026-02-25 06:17:32
Live Trading
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2026-02-20 03:08:15
Python Programming
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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 (46)
View More349.623Net Profit
61.586PSR
1.046Sharpe Ratio
0.162Alpha
0.9Beta
35.059CAR
27.2Drawdown
-2.31Loss Rate
24Parameters
1Security Types
1256Tradeable Dates
209Trades
0.24Treynor Ratio
2.06Win Rate
2737.393Net Profit
98.628PSR
2.141Sharpe Ratio
0.564Alpha
0.683Beta
95.199CAR
25.5Drawdown
-1.46Loss Rate
69Parameters
1Security Types
1255Tradeable Dates
198Trades
0.889Treynor Ratio
5.1Win Rate
195.691Net Profit
74.118PSR
1.02Sharpe Ratio
0.109Alpha
0.306Beta
24.203CAR
16.5Drawdown
-1.25Loss Rate
21Parameters
1Security Types
1255Tradeable Dates
405Trades
0.421Treynor Ratio
0.95Win Rate
92.425Net Profit
21.712PSR
0.458Sharpe Ratio
0Alpha
0.998Beta
13.989CAR
24.4Drawdown
0Loss Rate
5Parameters
1Security Types
1255Tradeable Dates
1Trades
0.065Treynor Ratio
0Win Rate
9.37Net Profit
14.888PSR
0.006Sharpe Ratio
-0.002Alpha
-0.202Beta
4.593CAR
16.1Drawdown
-0.4Loss Rate
24Parameters
2Security Types
501Tradeable Dates
115Trades
-0.003Treynor Ratio
0.45Win Rate
Community
View MoreTriton submitted the research Volatility-Targeted QQQ–TLT Rotation with a Multi-Scale CNN-LSTM Forecast
Abstract
We present an intraday equity strategy that reduces market ambiguity by forecasting short-horizon volatility with a CNN-LSTM, then using the forecast as a regime filter. Using 15-minute OHLC windows, three parallel convolutional branches (kernel sizes 3, 5, and 11) learn multi-scale price patterns, which are concatenated and passed to an LSTM to model volatility persistence and clustering. A final dense head outputs a forward realized-volatility estimate that governs risk-on/risk-off positioning, with an 8-bar cooldown after exits to avoid whipsaws. In backtests from Feb 1 to Jun 1, 2022, the model exited SPY during turbulence and rotated into SH, returning 3.09% during the rate-hike selloff.
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.
349.623Net Profit
61.586PSR
1.046Sharpe Ratio
0.162Alpha
0.9Beta
35.059CAR
27.2Drawdown
-2.31Loss Rate
24Parameters
1Security Types
1256Tradeable Dates
209Trades
0.24Treynor Ratio
2.06Win Rate
2737.393Net Profit
98.628PSR
2.141Sharpe Ratio
0.564Alpha
0.683Beta
95.199CAR
25.5Drawdown
-1.46Loss Rate
69Parameters
1Security Types
1255Tradeable Dates
198Trades
0.889Treynor Ratio
5.1Win Rate
195.691Net Profit
74.118PSR
1.02Sharpe Ratio
0.109Alpha
0.306Beta
24.203CAR
16.5Drawdown
-1.25Loss Rate
21Parameters
1Security Types
1255Tradeable Dates
405Trades
0.421Treynor Ratio
0.95Win Rate
92.425Net Profit
21.712PSR
0.458Sharpe Ratio
0Alpha
0.998Beta
13.989CAR
24.4Drawdown
0Loss Rate
5Parameters
1Security Types
1255Tradeable Dates
1Trades
0.065Treynor Ratio
0Win Rate
9.37Net Profit
14.888PSR
0.006Sharpe Ratio
-0.002Alpha
-0.202Beta
4.593CAR
16.1Drawdown
-0.4Loss Rate
24Parameters
2Security Types
501Tradeable Dates
115Trades
-0.003Treynor Ratio
0.45Win Rate
9.413Net Profit
14.899PSR
0.008Sharpe Ratio
-0.001Alpha
-0.203Beta
4.614CAR
16.1Drawdown
-0.41Loss Rate
24Parameters
2Security Types
501Tradeable Dates
115Trades
-0.004Treynor Ratio
0.45Win Rate
1436.161Net Profit
9.647PSR
0.643Sharpe Ratio
0.043Alpha
0.725Beta
15.111CAR
35Drawdown
-0.09Loss Rate
89Parameters
1Security Types
4879Tradeable Dates
5660Trades
0.126Treynor Ratio
0.19Win Rate
89.51Net Profit
20.469PSR
0.442Sharpe Ratio
-0.001Alpha
0.999Beta
13.633CAR
24.5Drawdown
0Loss Rate
5Parameters
1Security Types
1254Tradeable Dates
1Trades
0.062Treynor Ratio
0Win Rate
20.377Net Profit
8.721PSR
0.095Sharpe Ratio
-0.014Alpha
0.995Beta
6.373CAR
30.6Drawdown
-0.12Loss Rate
91Parameters
1Security Types
753Tradeable Dates
1150Trades
0.015Treynor Ratio
0.19Win Rate
-9.22Net Profit
5.516PSR
-0.295Sharpe Ratio
-0.02Alpha
0.995Beta
-7.012CAR
30.8Drawdown
-0.16Loss Rate
91Parameters
1Security Types
335Tradeable Dates
502Trades
-0.059Treynor Ratio
0.11Win Rate
-1.425Net Profit
20.835PSR
-0.137Sharpe Ratio
-0.224Alpha
0.844Beta
-3.843CAR
9.3Drawdown
-0.09Loss Rate
91Parameters
1Security Types
92Tradeable Dates
90Trades
-0.023Treynor Ratio
0.1Win Rate
101.79Net Profit
87.953PSR
2.025Sharpe Ratio
0.03Alpha
0.952Beta
49.268CAR
12.5Drawdown
-0.08Loss Rate
91Parameters
1Security Types
443Tradeable Dates
350Trades
0.351Treynor Ratio
0.54Win Rate
39.753Net Profit
72.106PSR
1.941Sharpe Ratio
0.38Alpha
0.566Beta
65.112CAR
16.8Drawdown
-0.17Loss Rate
91Parameters
1Security Types
169Tradeable Dates
166Trades
0.791Treynor Ratio
0.22Win Rate
79.543Net Profit
19.386PSR
0.609Sharpe Ratio
0.026Alpha
1.094Beta
12.413CAR
22.9Drawdown
-0.04Loss Rate
91Parameters
1Security Types
1258Tradeable Dates
592Trades
0.074Treynor Ratio
0.28Win Rate
55.53Net Profit
46.368PSR
0.956Sharpe Ratio
0.063Alpha
0.719Beta
18.007CAR
15.1Drawdown
-0.05Loss Rate
91Parameters
1Security Types
673Tradeable Dates
1061Trades
0.171Treynor Ratio
0.14Win Rate
-3.241Net Profit
22.485PSR
-0.413Sharpe Ratio
0.019Alpha
1.104Beta
-14.696CAR
12.6Drawdown
-0.14Loss Rate
91Parameters
1Security Types
53Tradeable Dates
5Trades
-0.082Treynor Ratio
0Win Rate
4.021Net Profit
65.06PSR
2.511Sharpe Ratio
0.029Alpha
1.01Beta
59.867CAR
5.9Drawdown
-0.05Loss Rate
91Parameters
1Security Types
23Tradeable Dates
40Trades
0.398Treynor Ratio
0.03Win Rate
-2.286Net Profit
9.149PSR
-2.102Sharpe Ratio
-0.204Alpha
0.495Beta
-17.219CAR
3.3Drawdown
-0.05Loss Rate
91Parameters
1Security Types
30Tradeable Dates
56Trades
-0.263Treynor Ratio
0.03Win Rate
1.452Net Profit
40.671PSR
0.432Sharpe Ratio
0.283Alpha
0.703Beta
12.21CAR
9.6Drawdown
-0.09Loss Rate
91Parameters
1Security Types
33Tradeable Dates
39Trades
0.163Treynor Ratio
0.24Win Rate
3.537Net Profit
69.841PSR
2.149Sharpe Ratio
0.037Alpha
0.537Beta
23.711CAR
3.3Drawdown
-0.04Loss Rate
91Parameters
1Security Types
43Tradeable Dates
84Trades
0.29Treynor Ratio
0.03Win Rate
-5.942Net Profit
10.395PSR
-2.317Sharpe Ratio
-0.061Alpha
0.601Beta
-50.645CAR
7.3Drawdown
-0.07Loss Rate
91Parameters
1Security Types
21Tradeable Dates
36Trades
-0.651Treynor Ratio
0.03Win Rate
54.828Net Profit
10.262PSR
0.392Sharpe Ratio
0.05Alpha
0.468Beta
9.138CAR
26.3Drawdown
-0.1Loss Rate
91Parameters
1Security Types
1260Tradeable Dates
2073Trades
0.108Treynor Ratio
0.12Win Rate
29.534Net Profit
10.728PSR
0.204Sharpe Ratio
0.002Alpha
1.273Beta
9.003CAR
34.7Drawdown
0Loss Rate
9Parameters
1Security Types
753Tradeable Dates
1Trades
0.031Treynor Ratio
0Win Rate
-3.995Net Profit
9.059PSR
-0.083Sharpe Ratio
0.029Alpha
1.268Beta
-3.018CAR
29.8Drawdown
0Loss Rate
9Parameters
1Security Types
335Tradeable Dates
1Trades
-0.016Treynor Ratio
0Win Rate
5.907Net Profit
40.653PSR
0.702Sharpe Ratio
-0.189Alpha
1.328Beta
16.971CAR
10.8Drawdown
0Loss Rate
9Parameters
1Security Types
92Tradeable Dates
1Trades
0.1Treynor Ratio
0Win Rate
120.583Net Profit
89.913PSR
2.129Sharpe Ratio
0.037Alpha
1.089Beta
57.048CAR
12.6Drawdown
0Loss Rate
9Parameters
1Security Types
443Tradeable Dates
1Trades
0.355Treynor Ratio
0Win Rate
26.171Net Profit
45.035PSR
0.998Sharpe Ratio
0.228Alpha
0.997Beta
41.663CAR
28.9Drawdown
0Loss Rate
9Parameters
1Security Types
169Tradeable Dates
1Trades
0.347Treynor Ratio
0Win Rate
86.25Net Profit
20.363PSR
0.629Sharpe Ratio
0.028Alpha
1.165Beta
13.24CAR
22.8Drawdown
0Loss Rate
9Parameters
1Security Types
1258Tradeable Dates
1Trades
0.075Treynor Ratio
0Win Rate
49.673Net Profit
28.364PSR
0.706Sharpe Ratio
0.035Alpha
0.998Beta
16.321CAR
16Drawdown
0Loss Rate
9Parameters
1Security Types
673Tradeable Dates
1Trades
0.119Treynor Ratio
0Win Rate
86.25Net Profit
20.363PSR
0.629Sharpe Ratio
0.028Alpha
1.165Beta
13.24CAR
22.8Drawdown
0Loss Rate
9Parameters
1Security Types
1258Tradeable Dates
1Trades
0.075Treynor Ratio
0Win Rate
Triton submitted the research Volatility-Targeted QQQ–TLT Rotation with a Multi-Scale CNN-LSTM Forecast
Abstract
We present an intraday equity strategy that reduces market ambiguity by forecasting short-horizon volatility with a CNN-LSTM, then using the forecast as a regime filter. Using 15-minute OHLC windows, three parallel convolutional branches (kernel sizes 3, 5, and 11) learn multi-scale price patterns, which are concatenated and passed to an LSTM to model volatility persistence and clustering. A final dense head outputs a forward realized-volatility estimate that governs risk-on/risk-off positioning, with an 8-bar cooldown after exits to avoid whipsaws. In backtests from Feb 1 to Jun 1, 2022, the model exited SPY during turbulence and rotated into SH, returning 3.09% during the rate-hike selloff.
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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