Collective2 · investment strategy
by Collective2
From 07-2016 we use a machine learning Algo. From 14-02-2017 we trade only etf and stock
Description from Collective2
of the return and risk figures:as the source publishes them
What $10,000 would have become, from the strategy's own published history, against the S&P 500 over the same dates. The basis is labelled.
Each rule is quoted word for word from the strategy's own documents.
“When the model exits the market, it moves into cash.”
“enabling it to build a library used to screen for equities with a higher probability of outperforming”
“a volatility-based sub-model which, in exceptional situations such as the onset of COVID in early 2020, can suspend trading activity”
“The model also incorporates a stop-loss rule”
“From 14-02-2017 we trade only etf and stock”
No published holdings for this strategy.
Fitted to 252 returns (hypothetical) from 2025-10-30 to 2026-10-16. An estimate, not holdings: it can't name stocks.
· : 0.52 · Too loose a fit for a verdict; shown as a guide.
Beta to the S&P 500: 0.36 (90%: 0.27 to 0.43)
Compares the stated rules with the holdings (or, where none are published, a mix estimated from returns), using tests fixed before any results were seen.
No holdings on file, so nothing to test.
Strategies that share its AI / ML-driven label, from other sources first, strongest risk-adjusted return first.
Composer Technologies · Composer
Collective2
How much of what we look for this strategy has, and why anything is missing. Fields that don't apply to it aren't counted.
3 missing
The text the rules are quoted from, with where it came from.
We have been using a pattern-recognition machine learning model since July 2016, well before the advent of LLM-based AI. The model is continuously updated through supervised training that now includes thousands of events, enabling it to build a library used to screen for equities with a higher probability of outperforming. The model also incorporates a stop-loss rule and a volatility-based sub-model which, in exceptional situations such as the onset of COVID in early 2020, can suspend trading activity. That said, machine learning models can struggle precisely because they are trained on historical data when confronted with truly exceptional events—such as an individual bankruptcy (an idiosyncratic event) or a market-wide shock (a systemic event). When the model exits the market, it moves into cash. Before December 2025, uninvested money was simply left in the account; only from December 2025 onward will it be allocated to short-term government instruments, such as the BIL ETF. Therefore, the results from 2015 to 2025 did not benefit from any yield-bearing cash parking. On C2, cash is not remunerated.
From 07-2016 we use a machine learning Algo. From 14-02-2017 we trade only etf and stock
Every value on file, each labelled with its calculation basis and as-of date.
| Asset classes / instruments | stocks | ||
| Leaderboard views (Collective2) | new_highs |
| Subscription price per month | $50 | 2026-10-16 | |
| Suggested minimum capital | $35k | 2026-10-16 |
| Strategy age | 4,060 | 2026-10-16 |
| Alpha vs S&P 500, strategy life (hypothetical model account) | 0.01 | 2026-10-16 | |
| Beta vs S&P 500, strategy life (hypothetical model account) | 0.15 | 2026-10-16 | |
| Correlation to S&P 500, strategy life (hypothetical model account) | 0.37 | 2026-10-16 |
| Annual return compounded, strategy life (hypothetical model account) | 6.00% | ||
| Annualized return since inception (Lighthorse, daily, hypothetical) | 5.81% | 2026-10-16 | |
| Monthly returns (hypothetical model account) | show{
"2015-Dec": 0.0016200000000000001,
"2015-Nov": 0.01617,
"2015-Oct": 0.02505,
"2015-Sep": -0.032709999999999996,
"2015-YTD": 0.00919,
"2016-Apr": -0.03646,
"2016-Aug": 0.0072499999999999995,
"2016-Dec": -0.00822,
"2016-Feb": -0.00149,
"2016-Jan": -0.042249999999999996,
"2016-Jul": 0.00507,
"2016-Jun": -0.08726,
"2016-Mar": 0.03722,
"2016-May": -0.00974,
"2016-Nov": 0.010629999999999999,
"2016-Oct": 0.00021,
"2016-Sep": 0.01077,
"2016-YTD": -0.11381000000000001,
"2017-Apr": 0.0008,
"2017-Aug": -0.00253,
"2017-Dec": 0.00602,
"2017-Feb": 0.00517,
"2017-Jan": 0.0149,
"2017-Jul": 0.01525,
"2017-Jun": 0.01324,
"2017-Mar": 0.01004,
"2017-May": 0.01248,
"2017-Nov": 0.011120000000000001,
"2017-Oct": 0.02548,
"2017-Sep": 0.0018,
"2017-YTD": 0.11955,
"2018-Apr": 0.008159999999999999,
"2018-Aug": 0.011859999999999999,
"2018-Dec": -0.00013,
"2018-Feb": -0.023450000000000002,
"2018-Jan": 0.01627,
"2018-Jul": 0.004050000000000001,
"2018-Jun": 0.00221,
"2018-Mar": -0.01565,
"2018-May": 0.00426,
"2018-Nov": 0.00611,
"2018-Oct": -0.0405,
"2018-Sep": -0.0032700000000000003,
"2018-YTD": -0.031110000000000002,
"2019-Apr": -0.00108,
"2019-Aug": 0.0064800000000000005,
"2019-Dec": 0.0147,
"2019-Feb": -0.00035999999999999997,
"2019-Jan": -0.00035999999999999997,
"2019-Jul": -0.00453,
"2019-Jun": 0.02492,
"2019-Mar": -0.00035999999999999997,
"2019-May": -0.019610000000000002,
"2019-Nov": 0.02204,
"2019-Oct": -0.00032,
"2019-Sep": 0.01065,
"2019-YTD": 0.05258,
"2020-Apr": 0.00911,
"2020-Aug": 0.00549,
"2020-Dec": 0.0076100000000000004,
"2020-Feb": 0.016810000000000002,
"2020-Jan": -0.01413,
"2020-Jul": 0.00193,
"2020-Jun": 0.01948,
"2020-Mar": -0.00034,
"2020-May": 0.028319999999999998,
"2020-Nov": 0.029910000000000003,
"2020-Oct": -0.00643,
"2020-Sep": 0.0034100000000000003,
"2020-YTD": 0.10494999999999999,
"2021-Apr": 0.014539999999999999,
"2021-Aug": 0.01571,
"2021-Dec": 0.01968,
"2021-Feb": 0.0106,
"2021-Jan": 0.00086,
"2021-Jul": 0.04893,
"2021-Jun": 0.0051600000000000005,
"2021-Mar": 0.05942,
"2021-May": 0.01088,
"2021-Nov": -0.00256,
"2021-Oct": 0.02077,
"2021-Sep": -0.03267,
"2021-YTD": 0.18195,
"2022-Apr": -0.00994,
"2022-Aug": -0.017490000000000002,
"2022-Dec": 0.00369,
"2022-Feb": -0.00872,
"2022-Jan": -0.01665,
"2022-Jul": 0.03769,
"2022-Jun": -0.0433,
"2022-Mar": 0.0070799999999999995,
"2022-May": -0.00433,
"2022-Nov": 0.0462,
"2022-Oct": 0.0121,
"2022-Sep": -0.01139,
"2022-YTD": -0.00828,
"2023-Apr": 0.0091,
"2023-Aug": -0.01585,
"2023-Dec": 0.02804,
"2023-Feb": -0.006790000000000001,
"2023-Jan": 0.0167,
"2023-Jul": 0.00986,
"2023-Jun": 0.014459999999999999,
"2023-Mar": 0.00971,
"2023-May": -0.01657,
"2023-Nov": 0.053970000000000004,
"2023-Oct": -0.03546,
"2023-Sep": -0.04559,
"2023-YTD": 0.01757,
"2024-Apr": -0.03322,
"2024-Aug": 0.014650000000000002,
"2024-Dec": -0.020419999999999997,
"2024-Feb": 0.0143,
"2024-Jan": 0.01847,
"2024-Jul": 0.04107,
"2024-Jun": -0.00365,
"2024-Mar": 0.01238,
"2024-May": 0.0239,
"2024-Nov": 0.08341,
"2024-Oct": -0.012809999999999998,
"2024-Sep": 0.00849,
"2024-YTD": 0.15121,
"2025-Apr": -0.04447,
"2025-Aug": 0.019039999999999998,
"2025-Dec": 0.0058,
"2025-Feb": 0.01678,
"2025-Jan": 0.05929,
"2025-Jul": 0.009439999999999999,
"2025-Jun": 0.0375,
"2025-Mar": -0.0462,
"2025-May": 0.02533,
"2025-Nov": 0.03929,
"2025-Oct": 0.008060000000000001,
"2025-Sep": 0.0025900000000000003,
"2025-YTD": 0.13480999999999999,
"2026-Apr": 0.01397,
"2026-Aug": 0.011850000000000001,
"2026-Feb": 0.033479999999999996,
"2026-Jan": 0.04202,
"2026-Jul": 0.03213,
"2026-Jun": 0.02411,
"2026-Mar": -0.047,
"2026-May": 0.00792,
"2026-Oct": -0.003,
"2026-Sep": -0.04063,
"2026-YTD": 0.07200000000000001
} | 2026-10-16 | |
| Return, last 365 days (Lighthorse, daily, hypothetical) | 12.58% | 2026-10-16 |
| Maximum drawdown since inception (Lighthorse, daily, hypothetical) | -14.15% | 2026-10-16 | |
| Maximum drawdown, last 365 days (Lighthorse, daily, hypothetical) | -6.42% | 2026-10-16 | |
| Maximum drawdown, strategy life (hypothetical model account) | -18.42% | 2026-10-16 | |
| Sharpe ratio since inception (Lighthorse, daily, hypothetical) | 0.42 | 2026-10-16 | |
| Sharpe ratio, last 365 days (Lighthorse, daily, hypothetical) | 0.86 | 2026-10-16 | |
| Sharpe ratio, strategy life (hypothetical model account) | 0.46 | 2026-10-16 | |
| Sortino ratio, last 365 days (Lighthorse, daily, hypothetical) | 1.31 | 2026-10-16 | |
| Sortino ratio, strategy life (hypothetical model account) | 0.64 | 2026-10-16 | |
| Volatility since inception (Lighthorse, daily, hypothetical) | 8.67% | 2026-10-16 | |
| Volatility, last 365 days (Lighthorse, daily, hypothetical) | 9.52% | 2026-10-16 |
| Average leverage | 0.59 | 2026-10-16 | |
| Maximum daily leverage | 2.10 | 2026-10-16 | |
| Number of trades | 4,625 | 2026-10-16 | |
| Percent of trades profitable | 54.94% | 2026-10-16 | |
| Profit factor | 1.30 | 2026-10-16 |
Collective2
Collective2
