DIX and GEX indicators

Two specific indicators, the Dix and the Gex, have been born from the investigations of SqueezeMetrics. They serve to monitor the behavior of short orders in the Dark Pools, which gives us an idea of the bullish sentiment of the real market:


These indicators are the way to get our analysis to incorporate the indications obtained in high frequency trading. For me they are a complement.

They are not easy to follow and extract conclusions right away, but sometimes they help. Specially to try to identify divergences. Below an example between GEX and S&P.

Mi primera onda de Elliot

Mi primera onda de Elliot que finalmente no cumple una de las tres normas de Elliot:

Las tres normas

  • 1ª norma: En un ciclo completo la onda 2 no puede retroceder a la 1 en su totalidad.
  • 2ª norma: En un ciclo completo la onda 3 nunca será la de menor recorrido entre las impulsivas (1 y 5).
  • 3ª norma: En un ciclo completo la onda 4 nunca tendrá como mínimo de cierre un precio inferior al del máximo de cierre marcado en la onda 1.

En la gráfica superior correspondiente al S&P de 2019.

  • Norma 1ª -> se cumple
  • Norma 2ª -> se cumple
  • Norma 3ª -> NO se cumple

Sigo aprendiendo sobre ondas de Elliot y sobre los detalles que hay que tener en cuenta en los comportamientos del mercado. Ahora el foco es:

  • Diferenciación de la compra por mano fuerte y compradores pequeños.
  • Identificación de volúmenes.

US exchange regulations, limit to show indexes

I have receipt today this message from Trading view:

As per exchange regulations, S&P and Dow Jones indexes can be shown ONLY to users who are NOT logged in, or who are logged in and have paid for a real-time package. We cannot show them to all registered users. We do not understand this policy, but must follow it regardless. So, if you want to see real-time S&P and Dow Jones indexes, either log out of your account, or purchase the package through your User Settings.

This is interesting, you can be anonymous or pay for it…

I need to find what’s going on, this is very weird.


I was yesterday night navigating onto the different charts and data that are available in this web: https://tradingeconomics.com

The purpose is to define a set of macro indicators that enable me to contrast macro trends into a sector, so I can advance in general terms the trend of the sector or identify a divergence.

For instance, the manufacturing sector has a lot of dependency on:

  • Consumer expenditure.
  • Energy price (electricity, gas, Brent barrel…)
  • and Raw material prices.

Here I can check all this. The difficulty is going to define the right indicators to check, and simplify the amount of data to be reviewed.

S&P Predictions

The beginning

On August 3rd 2018 I wrote about some behaviors of S&P. These behaviors and my desire to develop reports to understand trends at monthly level took me to draw this figure the same day:

How things happened in the calendar

At the end of August I took the decision that I was going to sell the majority of my positions. I did it.

In September I was astonished with the defiance to gravity of the market and a little bit pissed-off with the trend.

Now in October, specially in the second half of the month, I have seen how this has been evolving. The Q3 closing reports seemed to be the flutter of the butterfly that changed the trend.

The feeling

It’s just a graphic, it’s just a figure, it’s just a coincidence, but I’m happy about all I learned to be able to draw it.

Update November 23rd 2018

Update December 22nd 2018

Update January 2nd 2019

Update March 14th 2019

Update April 3rd 2019

Update on September 30th 2019

At the end,I was wrong, the S-H-S figure was not drawn. I was expecting a fall in May and it did not happen.

Fortunately to me, I did 2 big moves in the right moment and 1 move in the wrong moment:

The right ones:

  • August 2018: sell.
  • January 2019: buy.

The wrong one:

  • April 2019: sell

The impact of ICOs on cryptos’ value

I have had some conversations related to this during the last months.

The topic

So many companies/start-ups are opening ICO processes to fund their blockchain business. They create a business plan with a budget to fund it, the currency is in dollars/euros or other FIAT currency.

The majority enable the investors to fund the project with crypto-currency, mainly bitcoins or ethers. (It would be ironic that a company based on the block-chain will not trust the 2 main block-chain coins, right?).

The point is that the money they have raised is to fund the project that is based on real cost needs: salaries, equipment, other services. The majority of the services are paid in FIAT, so you are obliged to sell these coins in the market and then use the money for your business plan.

The questions I ask my self are

  • How many of these “sells” are provoking the market to bear?
  • Is there a real impact on the market?

Let’s do some math

I have used the data from https://www.coindesk.com/ico-tracker/ to do some basic math of the situation, and this is the result.

Let’s assume:

  • Take only in consideration the number of ICOs from 2018, as the ones for 2017 are already cashed in FIAT.
  • that the companies only enable to fund with Bitcoin and Etherium (data taken from https://coinmarketcap.com/ on August/2018).
  • Let’s assume 2 scenarios:
    • Scenario 1: 30% of the collected money is done in crypto-currency.
    • Scenario 2: 70% of the collected money is done in crypto-currency.

The math would be:

The result

Ok, the analysis is very simplified, and there could be a lot of bias on it.

But at rough estimate it looks like the impact is very low even if a high percentage of the collected funds comes from crypto-currencies.

On the other hand it’s a good bunch of dollars, isn’t it?

Quantitative trading on cryptocurrency market Q4

This is the third chapter of a learning process that started last September.

Fourth Quarter

The fourth step is defined for the next 3 months, where the main goal is to retake the back-testing and work more adjusted to a given analysis. I will also continue trading manually so I continue learning on the market momentum.

Following the V2MOM model:

  • Vision: Have a strategy running in crypto currency market running not with a period of 2 – 3 hours, but some days (stop operating at 3m).
  • Values: have fun, learn a lot, build a team with Dani, do practices and more practices.
  • Method: learn about trading basis, do backtesting with Quantopian on stocks or Forex (analyze the results in deep).
  • Obstacles: Time.
  • Measures:
    • Make short/long decisions based on 3 hour.
    • Perform backtesting with Tradingview and document the results and findings.
    • Improve and document the “mode operations” and “mode backtesting”.

Death line = September 2018

Results (October 1st, 2018)

  • Time to be accountable, let’s go…
  • I was able to perform a lot of trading during July and part of August.
  • During July I was able to earn in the total period a 33% of the money I moved. The market was plain and I was able to take profit without major rirsks.
  • The evolution of the market (bear) during August made me to be stuck inside, so almost all money was trapped.
  • I was not able to work on Quantopian.
  • I was able to apply the knowledge on stocks, and I did some shorts and long positions during August and September.
  • I have refined my mode of operations and analysis in Stock.
  • I improved the mode of operations and the mode of backtesting.

For next period, I will focus on other challenge: PgMP certification.