FAQs


Is my code safe?

Yes, we encrypt your code so that no one can look at it. You remain the owner of your intellectual property. Neither we nor our investors can look at your code.
 

Is Quantiacs a hedge fund?

No, we are a marketplace for user generated trading algorithms. We connect trading systems with external capital from institutional investors.
 

Should I develop a system for futures or stocks?

The big advantage of stocks is that they are beginner-friendly and easier to grasp (you simply buy a tiny piece of a company). Futures are the more powerful financial instruments. They are a derivative and provide huge liquidity. Liquidity can become an issue if your algo is making trades that have a significant size. Assume that your algo wants to invest $3M in stock X. You usually can not buy them at once which successively makes every other chunk of X-stocks more expensive. Your algorithm impacts the market price by eating through the order book. The lower the liquidity the bigger your market impact is and the higher the price you end up paying to get stock X.
 

Can I lose money with my system?

No, there is no downside risk for you. If your system performs well you will make money. It is the loss of the investor if your system has negative performance.
 

Do you offer data for high frequency trading?

No, our institutional clients usually invest several million dollars in one single strategy. Most HFT strategies do not scale well enough to handle that. Higher investments create higher market impact and come with higher slippage (= costs of trading). The strategies don’t remain successful as long and stable as daily trading strategies.
 

What do I get as a developing quant?

Hedge funds are typically compensated with 20% from the profits they make, called performance fee. Since this is an industry typical standard we also charge our institutional clients 20% of the performance and share this money 50/50 with the developing Quant.
 

How do I get paid?

We sent our Quants quarterly checks, in some cases monthly. The performance fee is calculated with the so called ‘Daily High Watermark’ method. This basically means that when your strategy reaches a new high, you earn a portion of that difference between the most recent high and the new high (the highest ‘watermark’). You get to keep the money even if your algo loses after this high.
 

What is slippage?

Slippage is the difference between the price at which you expected or placed your order and the price at which your order was actually filled. The following factors contribute to the slippage. The liquidity of the market: Higher liquidity results in lower slippage. In very liquid markets your positions are filled almost immediately. In an illiquid market, the order execution could cost significant time, in this time the price might move against you. You will notice that the impact of slippage on your trading system depends on how frequently you trade and how much return each trade generates. If you trade often and have trades with smaller returns per trade, slippage will be an issue. If you don’t change the size of your exposure often, slippage will be almost irrelevant for your results. These factors contribute to slippage:

  • Your trading volume: The more shares you want to buy, the longer the order execution takes. The longer the order execution takes, the further the fill price might be.
  • The bid-ask spread: This is the difference in the price quoted for an immediate buy (ask) and the price quoted for an immediate sale (bid). To get an order filled, you usually have to cross the spread. This is typically on the far side for you. If you want to sell 100 shares of Stock X you need to find a buyer for them. If there is one in the orderbook you’ll find him at the other side of the spread. Large bid/ask spreads lead to a high slippage.
  • The volatility of the market: For the sake of simplicity, let’s define volatility as the average change of price per unit of time. Thus, if the volatility is high, it’s evident that slippage will be higher in volatile markets since prices tend to move more while your order is executed.

How do I write a trading algorithm?

Quantiacs provides a free an open source toolbox and up to 25 years of historical market data for 44 Futures and the S&P500 stocks. You can use this data to test your trading hypothesis, which is called backtesting. The toolbox helps you to conveniently run these tests. It returns important indicators like the Sharpe Ratio, the equity curve and so on.
There are basically no limits to what you can try out. Coding and testing a trading algorithm is actual research in trying to capture effects that you can trade.
 

What happens if I have a good trading algorithm?

If you decide to make money with your trading algorithm you can upload it to our platform. This is done in a secure manner that protects your code and intellectual property (IP). Neither we nor any investors will look at your code and you remain the owner of the IP. Please find the details of our terms here.
Once your algorithm is uploaded to our platform we will start to simulate it on live data. This means that we can see how your algo would have performed on new data. By simulating it on unknown data we build its live track record. This is important to convince investors of its performance.
Quantiacs has longstanding relationships to the hedge fund industry and other institutional investors. Our institutional clients are interested in good investment opportunities. To ensure that the ‘black box’ really works they are usually looking for live track records of three months or longer.
 

How much money can I earn with my trading algorithm?

This depends on how well your algorithm performs and how much capital it gets. Hedge funds are typically compensated with 20% from the profits they make, called a performance fee. Since this is an industry standard we also charge our institutional clients this 20% performance fee. We share this money 50/50 with you - the system developer. 
 

What if I'm having installation problems in Python?

Taking the following steps can make sure you don't encounter any problems during installation.

  1. Install Anaconda Python 2.7 version
    • If it fails on Mac, follow the instructions for the command-line installer
  2. Open command prompt (Windows) or terminal (Mac), type conda and press enter
    • If you get an error go back and try reinstalling Anaconda
  3. Type pip install quantiacsToolbox and press enter
  4. Still having problems? Post your error in the Forums and someone will help you out

Do you have another question? No problem: Email your question to ask@quantiacs.com