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Beckett Bull Bear Algo

Stocks · Started Sep 2026

Trades Own Strategy

hypothetical · Cumul. Return
9.3%
Max Drawdown
1.9%
Trades
1
Win Trades
100.0%
Profit Factor
—
Win Months
—
Subscribe $150/mo

About this strategy

Beckett ATR RSI Algo is a rules-based swing-trading algorithm backtested from February 16, 2010 through August 14, 2026. Across the 15 full calendar years from 2011 through 2025, the Algo produced a profit in 14 of 15 years. Its edge does not come from winning every trade: approximately 40% of trades lose money, but historically the strategy gained about $1.30 for every $1.00 lost. Over many trades, that favorable payoff structure produced powerful compounding. All results shown are TradingView backtest results and do not represent actual trading performance.

This is a high-growth, high-volatility strategy designed to be evaluated over one year or longer—not week by week or month by month. Short-term results can be choppy and misleading. Roughly 30% of months were negative in the backtest, so investors need to be comfortable allowing the strategy's statistical edge to play out over a long series of trades rather than abandoning it during an inevitable losing period.

WHY THE STRATEGY IS INTERESTING

From 2011 through 2025, the Beckett ATR RSI Algo produced 51.89% compound annual growth, compared with 14.07% for the S&P 500 and 18.57% for the NASDAQ-100.

The strategy's annual-return Sortino ratio was 4.01, compared with 3.11 for the S&P 500 and 2.46 for the NASDAQ-100.

Sortino measures the amount of return generated relative to harmful downside volatility. A higher score is better. In simple terms, the Beckett ATR RSI Algo historically generated more return for each unit of downside risk than either benchmark.

That does not mean the strategy had smaller swings. It means the returns historically compensated investors more generously for the downside risk they experienced.

WHY IT CAN WORK EVEN WITH LOSING TRADES

A trading strategy does not need to win all the time.

The Beckett ATR RSI Algo historically lost on approximately 40% of its trades, but the average relationship between gains and losses was favorable: roughly $1.30 gained for every $1.00 lost.

That payoff advantage, repeated across hundreds of trades, is what drives the long-term result.

This means an investor can experience losing trades, losing weeks, and losing months while the longer-term strategy is still functioning as designed.

WHO THIS STRATEGY IS FOR

The Beckett ATR RSI Algo is designed for investors who:

can evaluate performance over one year or longer;
understand that losing trades and negative months are normal;
can tolerate substantial fluctuations in account value;
do not require short-term capital stability; and
can stay with a rules-based strategy during difficult periods rather than abandoning it because of short-term performance.

This is not a strategy for someone who needs a smooth ride.

THE CASE FOR CAUTION

The Beckett ATR RSI Algo can experience severe volatility and large drawdowns.

The clearest historical example occurred in 2022, when the strategy lost 68.47% during the calendar year.

A decline of 68.47% requires approximately a 217% gain just to return to breakeven.

The backtest subsequently produced a 264.53% gain in 2023, more than recovering the prior year's loss. There is no assurance, however, that a future recovery would occur as quickly or be as large.

For this reason, the strategy is unsuitable for investors who may need to withdraw capital during a major drawdown, who are likely to liquidate during periods of severe volatility, or who cannot tolerate a substantial temporary decline in account value.

BOTTOM LINE

The Beckett ATR RSI Algo is built around a simple statistical idea:

You do not need to win every trade if your gains are consistently larger than your losses.

Historically, approximately 40% of trades lost money, yet the strategy gained roughly $1.30 for every $1.00 lost, helping it produce profitable calendar years in 14 of 15 years during the backtest.

Its 51.89% compound annual growth and 4.01 Sortino ratio were substantially higher than the corresponding results for the S&P 500 and NASDAQ-100 over the tested period.

The trade-off is significant volatility.

This strategy is therefore best suited to a patient investor with a one-year-or-longer investment horizon who is comfortable accepting substantial short-term volatility in pursuit of higher long-term growth.

METHODOLOGY AND IMPORTANT LIMITATIONS

Results are derived from a TradingView Strategy Tester backtest covering 952 trades beginning February 16, 2010.

All results are backtested and do not represent actual trading performance. Backtests have inherent limitations, benefit from hindsight, and may not reflect actual market execution.

The backtest assumes zero commissions. Real-world commissions, spreads, slippage, execution differences, taxes, and other costs may reduce returns.

Past performance and backtested performance do not guarantee future results.

Statistics

Overview

Strategy began9/14/2026
Suggested Minimum Capital$15,000
Age14 days
C2 Rank932
Rank at C2 %Top 6.8%
Rank ##125
What it tradesStocks
# Trades1
# Profitable1
% Profitable100.0%
Avg trade duration5.0 days
Max peak-to-valley drawdown1.9%
drawdown periodSept 22, 2026 - Sept 23, 2026
Avg win$10,899
Avg loss$0

Ratios

W:L ratio—
Sharpe Ratio—
Sortino Ratio—
Calmar Ratio—

CORRELATION STATISTICS

Return Percent SP500 (cumu) during strategy life1.1%
Return of Strat Pcnt - Return of SP500 Pcnt (cumu)8.4%

Return Statistics

Ann Return (w trading costs)526.7%
Return Pcnt (Compound or Annual, age-based, NFA compliant)0.1%
Return Pcnt Since TOS Status989.0%
Ann Return (Compnd, No Fees)1155.5%

Slump

Current Slump as Pcnt Equity1.9%
Current Slump, time of slump as pcnt of strategy life0.4%

Instruments

Percent Trades Forex0.0%
Percent Trades Futures0.0%
Percent Trades Options0.0%
Short Options - Percent Covered100.0%
Percent Trades Stocks1.0%

Risk of Ruin (Monte-Carlo)

Chance of 10% account loss0.0%
Chance of 20% account loss0.0%
Chance of 30% account loss0.0%
Chance of 40% account loss0.0%
Chance of 50% account loss0.0%
Chance of 60% account loss (Monte Carlo)0.0%
Chance of 70% account loss (Monte Carlo)0.0%
Chance of 80% account loss (Monte Carlo)0.0%
Chance of 90% account loss (Monte Carlo)0.0%

Automation

Percentage Signals Automated50.0%

Popularity

Popularity (Today)0
Popularity (Last 6 weeks)659
C2 Score932
Popularity (7 days, Percentile 1000 scale)720

Trading Style

Any stock shorts? 0/10

Trades-Own-System Certification

Trades Own System?187701
TOS percent1.0%

Win / Loss

Avg Loss$0
Avg Win$10,899
# Winners1
Sum Trade PL (losers)$0
Sum Trade PL (winners)$10,899
Num Months Winners1
# Losers0
% Winners100.0%

Dividends

Dividends Received in Model Acct0

Age

Num Months filled monthly returns table1

Frequency

Avg Position Time (mins)7146.58
Avg Position Time (hrs)119.11
Avg Trade Length5
Last Trade Ago6

Leverage

Daily leverage (average)2.89
Daily leverage (max)3.03

Maximum Adverse Excursion (MAE)

Hold-and-Hope Ratio16.33

DRAW DOWN STATISTICS

Strat Max DD how much worse than SP500 max DD during strat life?-406799456
Max Equity Drawdown (num days)1
Last 4 Months - Pcnt Negative0.0%

Trading record

Placed 1 trades in real-life brokerage accounts.

SymbolSideQtyOpenedClosedP/L
TQQQ long1362.2Sep 18, 2026Sep 23, 2026$10,872

Past results are not necessarily indicative of future results.

These results are based on simulated or hypothetical performance results that have certain inherent limitations. Unlike the results shown in an actual performance record, these results do not represent actual trading. Also, because these trades have not actually been executed, these results may have under-or over-compensated for the impact, if any, of certain market factors, such as lack of liquidity. Simulated or hypothetical trading programs in general are also subject to the fact that they are designed with the benefit of hindsight. No representation is being made that any account will or is likely to achieve profits or losses similar to these being shown.