Darwinex UnCut

In this episode of the Darwinex podcast, Juan Colón (CEO) and Ali Saif (CDS) talk about Darwinex's Brexit strategy now that the UK will definitely get "Brexit done". They cover how it will affect:

- New EU customers

- Existing EU customers

- New and existing non-EU customers

Feedback? Questions? Please post them on the forum and we’ll respond accordingly! https://community.darwinex.com/t/our-brexit-strategy-impact-timelines-regulation/6886

Photo by brainblogger / CC BY

MetaTrader backtesting can be tricky business for algorithmic traders. Follow these best practices to engineer robust, reliable trading strategies.

In this podcast, we discuss ways to backtest trading strategies in a robust manner, and what factors to pay attention to during the entire trading strategy development process.

Our discussion centers around:

1) Data Handling

2) Parameter Selection

3) Variable Factors

Read the associated blog post here:

https://blog.darwinex.com/dos-donts-metatrader-4-backtesting/?utm_source=soundcloud&utm_medium=podcast&utm_content=backtesting-best-practices-for-algorithmic-traders

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Are you a good trader?

We'd love to have your strategy listed on our Exchange, enabling you to earn performance fees on investor profits!

More details here:

https://www.darwinex.com/?utm_source=soundcloud&utm_medium=podcast&utm_content=backtesting-best-practices-for-algorithmic-traders

1.9M in performance fees paid to date:

https://www.darwinex.com/darwinia/hall-of-fame/users?utm_source=soundcloud&utm_medium=podcast&utm_content=backtesting-best-practices-for-algorithmic-traders

In this podcast, we describe the main features and behaviours of overfit trading strategies, and the risks they pose to both traders and DARWIN investors.

We discuss:

1) Types of Overfit Trading Strategies

2) Typical Features & Behaviours

3) How Traders Can Address Overfitting

4) How Investors Can Avoid Overfit DARWINs.

Read the associated blog post here:

https://blog.darwinex.com/identify-overfit-trading-strategies/?utm_source=soundcloud&utm_medium=podcast&utm_content=how-to-identify-overfit-trading-strategies

--

Are you a good trader?

We'd love to have your strategy listed on our Exchange, enabling you to earn performance fees on investor profits!

More details here:

https://www.darwinex.com/?utm_source=soundcloud&utm_medium=podcast&utm_content=how-to-identify-overfit-trading-strategies

1.9M in performance fees paid to date:

https://www.darwinex.com/darwinia/hall-of-fame/users?utm_source=soundcloud&utm_medium=podcast&utm_content=how-to-identify-overfit-trading-strategies

Conventional trading performance measurement and backtesting via the Sharpe Ratio may create a risk-adjusted picture for traders, but isn't without its shortcomings.

In this podcast, we present an argument for why the Sharpe Ratio isn't the most optimal tool for measuring the performance of a trading strategy.

We discuss:

1) What is the Sharpe Ratio and what is it used for?

2) Where and how does the Sharpe Ratio fall short?

3) Alternatives for performance measurement available at Darwinex.

Read the associated blog post here:https://blog.darwinex.com/sharpe-ratio-strategy-performance/?utm_source=soundcloud&utm_medium=podcast&utm_content=reliable-trading-performance-measurement-sharpe-ratio-argument

--

Are you a good trader?

We'd love to have your strategy listed on our Exchange, enabling you to earn performance fees on investor profits!

More details here:

https://www.darwinex.com/?utm_source=soundcloud&utm_medium=podcast&utm_content=reliable-trading-performance-measurement-sharpe-ratio-argument

1.9M in performance fees paid to date:

https://www.darwinex.com/darwinia/hall-of-fame/users?utm_source=soundcloud&utm_medium=podcast&utm_content=reliable-trading-performance-measurement-sharpe-ratio-argument

6 months after the previous AMA session, here's the recording of the Winter 2018 Ask Me Anything session with Darwinex founders Juan & Javier Colón.

In previous episodes we talked about how liquidity aggregation works at Darwinex with every trade hitting the market, how our incentives are to always get you the best price and how we are a pure broker and not a market maker.

In this episode we talk bout the investor replica process.

After touching upon some basics (both traders and their followers/investors trade with our with FCA regulated brokerage, followers replicate, but do NOT know, underlying trades thus cannot bypass payment of 20% success fee of the trader) we discuss the following:

- Sequence of the replica process

- Divergence (difference between leader & replica price)

- Capacity of the trading strategy to get replicated bu investors (the more market liquidity you take, the more you move market price against you)

Every strategy gets to a point where it can’t take more capital. That’s when it’s reached full capacity.

In the case of news trading in retail, it's tens / hundreds of thousands before liquidity providers refuse to price either you or your broker.

Or we can go all the way up to Bridgewater Associates, Renaissance Technologies etc., given that some of the biggest asset managers stop accepting new investors. As a matter of fact, some of them have even STOPPED accepting investors altogether!

In this episode, yo'll be also able to undrstand:

- How we measure capacity.

- How we provide traders with choices when managing their DARWIN's capacity.

- How traders can make the most of the market, i.e. encounter te optimal debt vs. capital mix (how much leverage do you borrow to boost return on your own capital and how much investor capital is ideal to top things off on the above).

In this episode we talk about liquidity aggregation at our brokerage. We'll explain how the Darwinex broker operates, specifically where does the price you see and where the price you get come from when trading the assets we offer.

We'll cover liquidity aggregation for

- Exchange traded assets: Futures / DMA Stock CFDs

- OTC Assets: FX, Index CFDs, Commodity CFDs

We'll help you get the terminology right regarding:

- Last-look vs. no-last-look

- Asymmetric markets created by last-look practices

- Prime Broker

- Market making

On July 19th, 2018 we held the second Ask Me Anything (AMA) session at Darwinex. Founders Juan and Javier Colón answered to questions from Darwinex users.

On the blog you can find the list of questions and when they appear in the recording so you can jump directly to the ones that interest you.

In previous episodes we’ve talked about finding the 0.5% of needles in the haystack - they are the most valuable asset. They are the best trading data suppliers.

But then: is trading data supplied by the remaining 99.5% worthless? No, it isn’t. There is value to it.

There's an information asymmetry when it comes to data in trading:

- You know what you trade

- The House knows what you trade

- The House knows what everyone else trades

- You don’t know what everyone else trades

The individual trader is at a disadvantage to the House. He/she ignores real time crowd-sentiment. The stuff that moves the market in the short term.

What everyone would like to know.

The problem with market makers is that they use that information to benefit their shareholders. When the crowd moves sideways, they pocket the crowd’s spread. When the crowd stampedes, they hedge their risk and gift the crowd’s real time sentiment to Wall Street (to protect their shareholders).

This is why we’re not a market maker.

We are the custodian of our community’s data. Our shareholders can’t monetise that data directly without entering into a conflict of interest with customers unless we use that data AND pay our community a fair share in the profits it generates.

Listen to the episode to know the details of how we do this.

In the last episodes we talked about making traders investable, the rise of the fittest and telling the skilled traders from the lucky. But… how do you know it’s not a whole lot of BS? How to tell this apart from a marketing gimmick of the Cash for Dealers industry?

We get this a lot.

If you really think there’s good traders out there - prove it!

So we do the only thing we can do. Walk the talk. Invest our own monies in traders. This breaks our chicken & egg problem: no traders without investors, no investors without good traders.

So we’re the first investor. This attracts the first good traders which attracts better ones. It also reassures the first investors. Repeat.

How does it work? It’s the Darwinian Challenge - the rise of the fittest. It’s made up of:

- A capital allocation -> winners receive AuM

- A dividend -> winners are paid out cash every month

And we’ve paid out $ 1.4 MM to traders to date. The monthly run-rate is well in excess of EUR 50.000 per month at the moment.