Poster below is kind of right that you're never going to get an in depth answer for free...
But a few key points that separate the two:
1. It's very easy to make 50% percent a year on a few hundred thousand. If you can't even do that, it's not worth even bothering to compete. It's VERY hard to do the same with a few hundred million or worse, a billion.
2. With regards to #1, the key difference is market impact. When you start trading a non-trivial percentage of a symbol's average daily volume (eg; 10%+) you start having effects on the price. A dumb strategy would be to just place market orders for the full amount. Someone will just place a cascading set of limit orders that you'll hit as soon as you wipe out existing liquidity on the book. A slightly better strategy might hide the total order. A much better strategy will place thousands of small orders of random sizes at different times across different exchanges to simulate organic market activity and attract liquidity. This order sizing is probably based on both predictive models and analysis of the full exchange feeds (that are both very monetarily and computationally expensive to use)
3. Sophisticated algorithmic trading will either try and get the market to do something (eg; place orders in such a way as to elicit a reaction from the market) or use non-market data in combination with market data to make decisions. These approaches add external entropy and allow for more theoretical alpha than reacting to lagged market signals.
But a few key points that separate the two: 1. It's very easy to make 50% percent a year on a few hundred thousand. If you can't even do that, it's not worth even bothering to compete. It's VERY hard to do the same with a few hundred million or worse, a billion.
2. With regards to #1, the key difference is market impact. When you start trading a non-trivial percentage of a symbol's average daily volume (eg; 10%+) you start having effects on the price. A dumb strategy would be to just place market orders for the full amount. Someone will just place a cascading set of limit orders that you'll hit as soon as you wipe out existing liquidity on the book. A slightly better strategy might hide the total order. A much better strategy will place thousands of small orders of random sizes at different times across different exchanges to simulate organic market activity and attract liquidity. This order sizing is probably based on both predictive models and analysis of the full exchange feeds (that are both very monetarily and computationally expensive to use)
3. Sophisticated algorithmic trading will either try and get the market to do something (eg; place orders in such a way as to elicit a reaction from the market) or use non-market data in combination with market data to make decisions. These approaches add external entropy and allow for more theoretical alpha than reacting to lagged market signals.