Hey Kamal, look at your stats.
"Returns" is one of the last things they consider, and for good reason. So while no contest winner gets 800% returns... your algo doesn't really either. THe returns graph can be misleading.
Look at the amount of leverage you're using. For the Q Open you need to keep that under 1.0 Yours goes up to 200x, and goes way negative (I don't even know what that means). In the real world that's impossible. This would point to a problem with your code. Make sure there are no open orders when you place new orders for a stock, otherwise you might be double ordering -- a common pitfall caused by the clunky nature of Q's ordering functions. Keeping leverage under control is an art when you're dealing with shorts. This is just something you have to get good at -- paying attention to leverage and writing clever helpers to manage it, or use Q's built-in order optimizer.
They want something with a Beta of close to 0. Anybody can get larger returns by increasing beta (by using extra leverage). But increased beta means increased risk, and they don't need an algorithm in order to accomplish that -- they can get that for way cheaper.
They want something with positive Alpha. Alpha is basically the non-correlated gains. So market goes up, you make gains, market goes down, you still make gains -- that's the ideal algorithm, generating alpha despite whatever direction the market is moving. Negative alpha means you're not even keeping pace with the market (after you factor in leverage and such). Means for the amount of money you risked, you got significantly less returns for it than you would have by just investing that same amount in SPY.
Maxdrawdown should not exceed -0.10, ideally not more than -0.05. You're hitting -5.23, or 523% losses, so you lost all your money and then 4x more than that in debt. Realistically there's no way you would get out of that hole. You'd be bankrupt. Q does not want to invest in algos that make them bankrupt.
Sharpe and sortino ratios are good to keep an eye on. 0.01 is terrible. I read somewhere on this forum recently that large money managers never get 2.0 sharpe. So shoot for above 1.0.
Beyond stats, there are a lot of other issues the consider. A solid algo will continue to deliver consistent results out-of-sample. This is surprisingly hard, because it's really hard to differentiate between an economic hypothesis built on observed, reliable patterns and an economic hypothesis overfit to observed historical patterns. It's a super grey area. So it takes a lot to yourself to avoid introducing biases into your code that will inflate your backtest in a way that won't repeat out-of-sample.