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Their expiration is often accompanied by large losses. Put the money under the pillow? Take it into the bank? Give it to a hedge funds? Which gives us a slightly bad conscience , since those options are widely understood as a scheme to separate naive traders from their money.
And their brokers make indeed no good impression at first look. Some are regulated in Cyprus under a fake address, others are not regulated at all. They spread fabricated stories about huge profits with robots or EAs.
They are said to manipulate their price curves for preventing you from winning. And if you still do, some refuse to pay out , and eventually disappear without a trace but with your money. Are binary options nothing but scam?
Or do they offer a hidden opportunity that even their brokers are often not aware of? Deep Blue was the first computer that won a chess world championship. That was , and it took 20 years until another program, AlphaGo , could defeat the best human Go player.
Deep Blue was a model based system with hardwired chess rules. AlphaGo is a data-mining system, a deep neural network trained with thousands of Go games. Not improved hardware, but a breakthrough in software was essential for the step from beating top Chess players to beating top Go players. This method does not care about market mechanisms. It just scans price curves or other data sources for predictive patterns.
In fact the most popular — and surprisingly profitable — data mining method works without any fancy neural networks or support vector machines. This is the third part of the Build Better Strategies series. As almost anything, you can do trading strategies in at least two different ways: We begin with the ideal development process , broken down to 10 steps.
We all need some broker connection for the algorithm to receive price quotes and place trades. Seemingly a simple task. Trading systems come in two flavors: This article deals with model based strategies. Even when the basic algorithms are not complex, properly developing them has its difficulties and pitfalls otherwise anyone would be doing it. A significant market inefficiency gives a system only a relatively small edge.
Any little mistake can turn a winning strategy into a losing one. And you will not necessarily notice this in the backtest. The more data you use for testing or training your strategy, the less bias will affect the test result and the more accurate will be the training. Even shorter when you must put aside some part for out-of-sample tests.
Extending the test or training period far into the past is not always a solution. The markets of the s or s were very different from today, so their price data can cause misleading results. But there is little information about how to get to such a system in the first place. The described strategies often seem to have appeared out of thin air. Does a trading system require some sort of epiphany? Or is there a systematic approach to developing it?
The first part deals with the two main methods of strategy development, with market hypotheses and with a Swiss Franc case study. All tests produced impressive results. So you started it live. Situations are all too familiar to any algo trader. Carry on in cold blood, or pull the brakes in panic?
Several reasons can cause a strategy to lose money right from the start. It can be already expired since the market inefficiency disappeared. Or the system is worthless and the test falsified by some bias that survived all reality checks.
In this article I propose an algorithm for deciding very early whether or not to abandon a system in such a situation. You already have an idea to be converted to an algorithm. You do not know to read or write code. So you hire a contract coder. Just start the script and wait for the money to roll in.