50/200 SMA crosslongBacktest Results @ SPY • 10 Minutes
The Moving Average Crossover strategy uses two moving averages of different periods to generate buy and sell signals. It appoximates the idea of a trending market by using 2 SMAs, one short fast SMA(50) and another slow longer SMA(200). It buys whenever a short SMA(50) crosses up a long SMA(200), thereby implying that the direction of the market has changed. It sells once a short SMA(50) crosses down a long SMA(200). These are fairly long MAs, which means that this strategy is naturalyl meant to capture bigger moves, and thereby might not be a good fit for short time frames. But assumptions like that do not mean anything, because we've backested it! See youself.
Equity Curve
Backtest covers 12.6 months of SPY • 10 Minutes (SPDR S&P 500) data, from June 28, 2024 to July 11, 2025.
Equity curve is the strategy's performance over time. You should compare it to the asset's Buy & Hold performance. In general, you want the blue area to be well above the gray area.
Drawdown is how much losses (realized or unrealized) the strategy has had if compared to the highest equity peak. Compare this to the asset's drawdown to see whether your strategy does a decent job of isolating you from downside volatility. In general, the red area must be well within the gray area.
So, we have backtested 50/200 SMA cross on 12.6 months of SPY • 10 Minutes candles. This backtest resulted in 29 positions, with the average win rate of 41% and reward-risk ratio of 1.89. If you assume that 1.89 reward-to-risk ratio holds, you need a minimum win rate of 34.6 to be profitable. So you're looking good so far. However, 29 positions is a small sample size, so take the results with a huge grain of salt. The key metrics are as follows:
- Total Return: Total Return: 6.60% vs 14.30% for the asset
- Max Drawdown: Max Drawdown: -13.40% vs -20.70% for the asset
- Exposure: Exposure: 59.90% time in the market
- Win Rate: Win Rate: 41.0%, vs 34.6% minimum
- Reward/Risk Ratio: Reward/Risk Ratio: 1.89
With that exposure in mind, you can tell that for 60% time-in-market, you get 46.15% of the asset upside potential, and 64.73% of the asset downside potential.
50/200 SMA cross: enter a position when
All of the following: # "Papa" 10min Simple Moving Average (50, 0, close) Crosses ↗ 10min Simple Moving Average (200, 0, close)
50/200 SMA cross: exit a position when
All of the following: # "India" 10min Simple Moving Average (50, 0, close) Crosses ↘ 10min Simple Moving Average (200, 0, close)
50/200 SMA cross @ SPY • 10 Minutes (6.6%) backtest results explained by Alex C, Mike, Sarah
Alex C
AuthorThe backtest results show some concerning patterns for this classic SMA crossover strategy. The Win Rate of 41% with Risk/Reward of 1.89 looks mathematically viable, but I'm not convinced about its real-world applicability.
The strategy underperforms buy & hold significantly (6.6% vs 14.3%) while still exposing you to significant drawdowns (-13.4%). What worries me most is the low trading frequency - only 29 trades over 12.6 months means we don't have enough statistical significance to trust these numbers. The average trade duration of 205 candles also suggests the strategy is quite slow to react to market changes, which explains the underperformance in volatile periods.
I must say though, the risk metrics are not completely terrible - Beta of 0.34 shows good market independence, and the drawdown is better than the overall market (-13.4% vs -20.7%). But with such low Sharpe (0.36) and Sortino (0.47) ratios, I would not recommend trading this without significant modifications. Perhaps adding momentum filters or optimizing the SMA periods could improve the results, but in its current form, the strategy needs more work to be viable.
Mike
AuthorYo fam, let me break down this 50/200 SMA cross strategy on SPY! 🚀
Looking at these numbers, it's giving me some mixed vibes tbh. The strategy's got a decent risk/reward ratio of 1.89 and that win rate leeway is absolutely bonkers at 4065% above the minimum - that's some serious cushioning! 💪 Plus, those average wins are nearly double the size of average losses, which is pretty sweet.
But here's the thing bros - we're only catching about 46% of the market's total gains (6.6% vs 14.3% buy & hold), and that 41% win rate could definitely be better. The max drawdown of -13.4% is actually not too shabby compared to the asset's -20.7%, so we're managing risk decent enough. Market exposure at 59.9% means we're staying safe during choppy times, which I dig. 🏄♂️
Overall, this strategy isn't exactly YOLO material, but it could be a solid base to build on. Maybe add some momentum indicators or volume confirmation to boost that win rate? I'd probably throw a small portion of my Wendy's checks at this while I keep tweaking it. Just remember, past performance doesn't guarantee future tendies! 🍗
Sarah
AuthorMadre mía, this is one of the most mediocre strategies I have seen in my career! Let me tell you why this is basically throwing money into garbage.
First, your win rate is pathetic - only 41%! Even though your Risk/Reward ratio of 1.89 technically makes this mathematically viable, you're basically hoping to catch big winners while accepting frequent losses. This is mentally exhausting and most traders would break down psychologically before seeing any real profits.
The performance is embarassing - 6.6% net profit when buy & hold gave 14.3%? ¡Qué desastre! You're basically paying commission fees and spending time to make HALF of what you could make by simply buying and forgetting. The Sharpe ratio of 0.36 is terrible - you're taking on risk without adequate compensation.
The only somewhat decent thing here is your Win Rate Leeway being positive, but honestly, that's like being proud of getting a D- instead of failing completely. The strategy produces very few trades (only 29 in over a year!) which means your sample size is too small to be statistically meaningful.
¡Por favor! Do yourself a favor and either stick to buy & hold or develop something that actually adds value instead of destroying it. This strategy belongs in the trash bin.
Tabular metrics of 50/200 SMA cross backtested on SPY • 10 Minutes
| Total Trades | 29 | Net Profit | 6.6% | Buy & Hold Profit | 14.3% |
| Win Rate | 41% | Reward/Risk Ratio | 1.89 | Max Drawdown | -13.4% |
| Asset Max Drawdown | -20.7% | Exposure | 59.9% | Avg Candles in Position | 205.7 |
| Sharpe Ratio | 0.36 | Sortino Ratio | 0.47 | Realized Volatility | 10.65% |
| Max Winning Streak | 2 | Avg Winning Streak | 1.3 | Max Losing Streak | 4 |
| Avg Losing Streak | 2.1 | Avg Trades per Month | 4.6 | Avg Trades per Day | 0.2 |
| Return Std Dev | 2.1 | Loss Std Dev | 1.2 | Win Std Dev | 1.2 |
| Expectancy | 0.2 | Beta | 0.34 |
All backtests for 50/200 SMA cross
| backtest | exposure | peformance vs asset | drawdown vs asset | win% | reward/risk |
|---|---|---|---|---|---|
BTCUSDT • 1 Minute | 57% | (5.4%/8.9%) 0.61x | (-2.0%/-1.9%) 1.05x | 39 | 5.4 |
EURUSD • 1 Minute | 61% | (-1.1%/-1.0%) 1.10x | (-1.2%/-1.2%) 1.00x | 20 | 1.1 |
GLD • 1 Minute | 52% | (1.7%/0.0%) Infinityx | (-2.5%/-5.5%) 0.45x | 36 | 2.5 |
NVDA • 1 Minute | 61% | (1.2%/16.7%) 0.07x | (-7.9%/-4.4%) 1.80x | 28 | 2.9 |
SPY • 1 Minute | 60% | (0.5%/4.6%) 0.11x | (-2.5%/-2.1%) 1.19x | 33 | 2.3 |
TSLA • 1 Minute | 47% | (6.6%/-10.3%) -0.64x | (-11.2%/-21.4%) 0.52x | 39 | 2.1 |
WMT • 1 Minute | 41% | (-0.5%/-5.4%) 0.09x | (-3.9%/-6.4%) 0.61x | 29 | 2.4 |
BTCUSDT • 10 Minutes | 57% | (18.2%/22.4%) 0.81x | (-8.6%/-12.1%) 0.71x | 41 | 3.1 |
EURUSD • 10 Minutes | 55% | (2.5%/5.8%) 0.43x | (-2.2%/-4.3%) 0.51x | 38 | 2.4 |
GLD • 10 Minutes | 58% | (26.1%/43.7%) 0.60x | (-5.9%/-8.3%) 0.71x | 54 | 3.1 |
NVDA • 10 Minutes | 57% | (5.0%/32.9%) 0.15x | (-33.4%/-42.8%) 0.78x | 44 | 1.4 |
SPY • 10 Minutes | 60% | (6.6%/14.3%) 0.46x | (-13.4%/-20.7%) 0.65x | 41 | 1.9 |
TSLA • 10 Minutes | 47% | (18.4%/59.0%) 0.31x | (-34.8%/-55.3%) 0.63x | 41 | 1.9 |
WMT • 10 Minutes | 60% | (37.4%/40.1%) 0.93x | (-10.3%/-23.8%) 0.43x | 62 | 1.8 |
BTCUSDT • 1 Hour | 56% | (36.4%/68.2%) 0.53x | (-31.3%/-30.6%) 1.02x | 43 | 2.2 |
EURUSD • 1 Hour | 50% | (6.9%/6.8%) 1.01x | (-5.7%/-9.0%) 0.63x | 42 | 2.4 |
GLD • 1 Hour | 60% | (35.8%/117.6%) 0.30x | (-26.6%/-22.2%) 1.20x | 36 | 3.0 |
NVDA • 1 Hour | 61% | (783.4%/3126.3%) 0.25x | (-51.4%/-68.0%) 0.76x | 52 | 3.5 |
SPY • 1 Hour | 64% | (85.7%/106.7%) 0.80x | (-19.0%/-35.1%) 0.54x | 61 | 2.3 |
TSLA • 1 Hour | 55% | (2930.2%/1395.5%) 2.10x | (-38.7%/-75.1%) 0.52x | 56 | 6.0 |
WMT • 1 Hour | 60% | (33.6%/138.2%) 0.24x | (-28.4%/-26.9%) 1.06x | 50 | 1.6 |
BTCUSDT • Daily | 59% | (638.7%/1337.5%) 0.48x | (-61.1%/-76.6%) 0.80x | 83 | 7.3 |
EURUSD • Daily | 35% | (4.8%/10.8%) 0.44x | (-12.2%/-23.3%) 0.52x | 71 | 0.7 |
GLD • Daily | 61% | (242.9%/595.1%) 0.41x | (-36.4%/-45.3%) 0.80x | 46 | 6.4 |
NVDA • Daily | 65% | (83183.3%/373678.5%) 0.22x | (-57.1%/-90.0%) 0.63x | 77 | 16.0 |
SPY • Daily | 72% | (1127.3%/1316.3%) 0.86x | (-32.5%/-56.7%) 0.57x | 87 | 3.8 |
TSLA • Daily | 57% | (3139.0%/24185.2%) 0.13x | (-65.4%/-75.0%) 0.87x | 38 | 25.3 |
WMT • Daily | 64% | (1143.9%/13022.2%) 0.09x | (-57.1%/-50.6%) 1.13x | 39 | 8.1 |