Lab 1000 - BTC
Auto-registered from TradingView. Trading BTCUSD on 125m timeframe via Alpaca.
Period
Jan 2022 – Aug 2026
History
4.5 years
Trades
865
Start capital
$100,000
Total return
+601.6%
$100,000
starting capital
Profit factor
1.39
Money won for every 1 lost. Above 1.5 is solid.
Win rate
51%
445 winning and 420 losing trades.
Max drawdown
54.2%
Largest fall from a peak. Your worst-case dip.
Sharpe ratio
1.12
Return per unit of volatility. Above 1 is good.
Trades
865
Over 4.5 years of history.
Overview
This strategy at a glance
What the numbers above mean in practice, in plain English.
At a glance
How it wins
Wins 51% of its trades. The average winner (+1.69%) is 1.32× the size of the average loser (-1.29%), which is what keeps the edge positive.
Reward for the risk
Profit factor of 1.39: for every 1 unit lost it earned 1.39. Sharpe 1.12 and Sortino 1.65 measure the return you get per unit of overall and downside volatility.
What the worst looks like
Deepest drop from a peak was 54.2%. The longest stretch spent below a prior high was 168 days, and the longest losing streak was 11 trades in a row.
How steady it is
Profitable in 63% of months and 100% of calendar years. Best month +93.3%, worst month -45.1% of the account.
How often it trades
865 closed trades over 4.5 years, about 192 a year, holding for 12 bars on average.
Growth
How the account grew
The equity curve is the strategy's track record in one picture: a steady upward slope means gains compounded consistently, while sharp dips show the losing periods you would have lived through.
Cumulative return
Cumulative return on a $100,000 starting capital, trade by trade. It finished at +601.6%.
Underwater plot
How far the account sat below its previous high at any moment. Shallow and short-lived is what you want.
Return by year
Profit or loss in each calendar year. Look for green across the board rather than one big year carrying the rest.
Consistency
Month by month
Strategies that only make money in a few lucky months are fragile. The heatmap shows every month's return so you can see how often, and how evenly, this one pays.
Monthly returns heatmap
Each cell is the sum of trade returns closed that month, in %. Greener is better, redder is worse.
| Year | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | Year |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022 | +15.5 | -14.9 | -9.6 | +3.1 | +29.8 | +24.1 | +14.1 | +2.2 | +1.0 | +4.4 | -2.8 | -2.4 | +64.4% |
| 2023 | -5.0 | -9.5 | +14.0 | +2.2 | +1.6 | +9.2 | -5.7 | -2.2 | -1.6 | +11.7 | +7.6 | +7.7 | +30.0% |
| 2024 | -9.8 | +22.5 | +30.7 | -8.1 | +5.2 | +4.4 | +11.8 | -15.6 | +5.4 | -4.9 | +24.8 | -8.2 | +58.4% |
| 2025 | +4.7 | -1.2 | +5.3 | +2.0 | +5.4 | -6.2 | +3.0 | +1.4 | +1.8 | -0.3 | +15.2 | -2.1 | +29.1% |
| 2026 | +5.3 | +12.0 | -3.8 | +17.5 | +2.9 | +8.4 | -9.1 | -1.4 | · | · | · | · | +31.7% |
Monthly return
Result of each month as a % of the account. 63% of months were profitable.
Risk
What the bad times looked like
Every strategy has losing stretches. These are the ten deepest ones in the backtest: when they started, how long they lasted and how long it took to climb back.
| # | Started | Recovered | Depth | Days |
|---|---|---|---|---|
| 1 | 2024-11-28 | 2025-03-04 | -17.1% | 97 |
| 2 | 2022-02-03 | 2022-05-14 | -54.2% | 100 |
| 3 | 2025-07-16 | 2025-10-09 | -11.9% | 86 |
| 4 | 2024-03-20 | 2024-06-14 | -15.6% | 87 |
| 5 | 2024-07-29 | 2024-11-15 | -13.8% | 109 |
| 6 | 2025-03-09 | 2025-05-14 | -11.0% | 66 |
| 7 | 2025-12-01 | 2026-01-04 | -8.9% | 34 |
| 8 | 2025-05-26 | 2025-07-16 | -9.2% | 51 |
| 9 | 2026-06-09 | — | -6.1% | 61 |
| 10 | 2026-02-08 | 2026-04-14 | -6.0% | 65 |
All metrics
Every statistic, explained
The complete set, each with a one-line meaning so nothing is a mystery.
Performance
Total return
Net result of all closed trades, as a % of the account.
+601.6%
Profit factor
Gross profit divided by gross loss.
1.39
Payoff ratio
Average win relative to average loss.
1.32
Average return per trade
Typical result of a single trade.
+0.25%
Gross profit / loss
Total won and total lost before netting, as a % of the account.
+2159.8% / -1558.2%
Risk-adjusted return
Sharpe ratio
Return per unit of total volatility, annualised.
1.12
Sortino ratio
Return per unit of downside volatility only.
1.65
Calmar ratio
Annual return divided by the worst drawdown.
0.68
Omega ratio
Probability-weighted gains versus losses.
1.39
Kelly criterion
Theoretical optimal bet size. Most traders use a fraction of it.
14.3%
Drawdown & volatility
Max drawdown
Largest peak-to-trough fall.
54.2%
Longest drawdown
Longest time spent below a previous high.
168 days
Average drawdown
Typical depth and length of a dip.
22.1% · 41 days
Recovery factor
Net profit relative to the worst drawdown.
7.60
Volatility (annualised)
How much returns swing over a year.
42.1%
Extremes & streaks
Largest win
+30.70%
Largest loss
-12.12%
Best / worst month
+93.3% / -45.1%
Best / worst year
+166.7% / +49.2%
Longest win / loss streak
Plan position size so the losing streak is survivable.
8 / 11 trades
Skew / kurtosis
Positive skew means more large wins than large losses.
3.91 / 30.37
How to read these results
Figures come from a backtest of closed trades on BTCUSD, with every figure shown as a percentage of the $100,000 starting capital the backtest was run on. Backtests show how a strategy would have behaved on past data, not what it will do next. Past performance does not guarantee future results; size positions so the worst drawdown above is something you can sit through.
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