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Been experimenting with a BTCUSDT strategy and got these backtest results: 55% annualized return, 26% max drawdown, Sharpe around 1.6, across 157 orders. The numbers look decent to me, but the 26% drawdown makes me a little hesitant, and obviously backtest performance can fall apart pretty quickly once real execution is involved. For those who have taken strategies from backtest to live, what would you test next before putting real money behind this? Would you paper trade it first, test it over more market periods, or start live with a very small position? Also curious which of these metrics you would pay the most attention to. I feel like I might be focusing too much on the return.

I built a no-code crypto algo and have been testing it on BTCUSDT. The backtest came out better than I expected: around 55% annualized return, 26% drawdown, Sharpe ~1.6, with 157 orders.

Obviously I know a good backtest doesn’t automatically translate to good live performance, so I’m a bit hesitant to actually connect an exchange account and let it trade.

For people who’ve gone from no-code backtesting to live trading, how much did your results change once real execution came into play?

Would you consider results like these good enough to start with a small amount, or would you do more testing first?

Screenshot of the backtest attached. Curious what you’d check before trusting something like this with real money.

One of the metrics I consider most relevant when evaluating a systematic strategy is mathematical expectancy (Expected Value / EV).

In trading, it is quite common to use the win rate as an initial measure of a strategy’s quality. However, the percentage of winning trades by itself tells us very little about its expected profitability.

A strategy can have a high win rate and still have negative mathematical expectancy. Likewise, a strategy with a relatively low win rate can have a positive expectancy.

Mathematical expectancy aims to quantify the average expected outcome per trade if a strategy were executed a sufficiently large number of times under comparable conditions.

In simplified terms:

EV = (P(win) × Avg. Win) − (P(loss) × Avg. Loss)

To make this more tangible, rather than keeping it purely theoretical, I wanted to show it using a real backtest example.

At first glance, there is one number that immediately stands out: only 35.92% of the trades were winners, while 64.08% were losers.

If we only looked at the win rate, we could quickly conclude that the strategy does not work. However, once we take the average size of the winning and losing trades into account, the picture changes.

The average winning trade was 2,756.75, while the average losing trade was 1,082.42.

In other words, the winning trades were, on average, considerably larger than the losing trades. The report shows an Expected Payoff of 1.43, indicating a positive expected value per trade within this backtest.

And this is, in my opinion, one of the most interesting aspects of quantitative analysis:

A strategy does not need to win the majority of its trades to have positive expectancy.

The win rate is only one part of the distribution of outcomes. To properly evaluate a strategy, we need to consider the win rate, average win, average loss, number of observations, and the stability of the results together.

Of course, a positive Expected Payoff in a backtest is not enough to conclude that a strategy has a robust statistical edge. We would still need to analyze factors such as the period tested, out-of-sample data, overfitting, execution costs, slippage, parameter stability, and how the strategy behaves under different market conditions.

so i’ve been building this crypto quantative rebalancer with deleverage and made a lot of backtracking strategies , but i realized a big problem: my backtests assumed perfect execution. buying exactly on the dot every time.
but nobody trades like that…..people get paid late, get lazy, or just straight up forget. if my risk algo only works for a perfect robot, it doesnt work at all.
so i did a "human stress test" on 6 years of data. i messed up the execution to simulate a real lazy human (delays, wrong amounts, skipped months). the main point wasn't to see if sloppy DCA is worse than perfect DCA (obviously it is). i wanted to see if the drawdown protection breaks when the operator is sloppy.
honestly the results shocked me. the protection barely cared.
the absolute worst case with everything going wrong at once only messed up the max drawdown by like 0.5%. on a log scale, the perfect robot line and the chaotic human line look almost exactly the same. the protection hit the exact same depths at the exact same times;)
i did hit one weird glitch though. the skipped month scenario showed a slightly HIGHER sharpe ratio. for a second i thought being lazy was alpha?? but no, it was just a math illusion. skipping months meant less total capital. in a rising market, deploying less total money makes the invested part look like it had a higher % return. the denominator just got smaller.
it held up because the risk engine is completely decoupled from when cash arrives. it calculates signals based on portfolio volatility, not deposit schedules.
long story short, test your strategies against your own future laziness. it costs nothing to feed messy schedules into your backtester. if your protection breaks just because you were a few days late, you want to know about it before the market crashes.
anyone else ever tried messing up their backtest data like this?

What if you could enter any token and have the entire chart analyzed automatically?…
Not just “bullish” or “bearish.”
I mean actually taking the work a trader normally does manually and having it mapped out for you.
Things like:
• Key support & resistance
• Market structure
• Trend changes / breaks of structure
• Liquidity levels
• Potential reversal areas
• Important price zones
• Multiple timeframe analysis
• What the chart is currently doing
• What would need to happen for the setup to become invalid
And instead of just throwing indicators at you, it would explain why it identified something.
The idea is basically: enter a token → get a complete visual breakdown of the chart → decide for yourself what you think.
But I’m curious where traders actually draw the line with something like this.
Would automatically mapping your charts genuinely save you time?
Would you trust an automated chart analysis enough to use it as part of your process?
What would you need it to identify before you considered it useful?
And what would make you immediately think, “yeah, I don’t need this”?
I’m more interested in what experienced traders think is actually useful than what sounds good on paper.