Sociopath Community

Today I made 120 dollars trading natural gas!  https://energymarkettrader.blogspot.com/2023/02/21723-energy-market-trading-journal.html 

Good day for me :)

Trading with leverage ? Very brave.

AliceInWonderland said: 

LiYang said:

I don't understand why you would say forecasting doesn't matter. that's kinda the goal, to forecast the price so you can buy or sell.

Forecasting accuracy diverges from expected payoff because market events are thick tailed. 

 

There is a difference between a forecast and its accuracy and your exposure to the market. You do not derive returns from the expectation of your forecast but instead the expectation of your payoff function. 

 

You be wrong all the time and still have positive expected payoff if the cost of being wrong is low, this is known as convexity of payoff. In turn you can have near absolute accuracy in your forecasts and still experience ruin because the cost of being wrong is high and your payoff function has a negative expected return. 

 

The example below shows how bet size over favorable conditions, that is accurate forecasting, maps to ruin. 

 

The curves represent payoff given a bet size over odds of that payoff, where the red dots are the most optimal bets. Notice that even under the most favorable odds you expect ruin (lose everything) if you go beyond optimal betting. The blue linear curve with an optimality of 1 shows ruin is impossible because the probability of there not being a payoff is 0, but that is impossible in real world markets. 

 

But I agree, any randomness will need to be identified and filtered out.

We do not want to filter out randomness, we want to make optimal decisions under highly random conditions. 

One of the fundamental problems with forecasts is that they do filter out randomness which in turn obscures your decision making and leads you to ruin. 

Forecasting errors are necessarily thin-tailed while market events and therefore payoffs are thick-tailed. As such,  forecasts do not allow you to consider events that lead to ruin. Essentially, a highly accurate forecast cannot account for one extremely costly payoff that makes you go bust. Despite that event being rare, it will completely destroy any gains you made prior to that event taking place.   

puppygirl said:

um ok thanks i think >.>

 You're welcome. 

edit :

Source equations for the code are from one of talebs moocs, 

 You're making a lot of claims here with no references. Sorry, fake news wikipedia won't cut it. Where is the evidence of your claims?

puppygirl said: 

Today I made 120 dollars trading natural gas!  https://energymarkettrader.blogspot.com/2023/02/21723-energy-market-trading-journal.html 

Good day for me :)

 I'll be impressed when it goes to $1200 a day.

What leading indicators do you think you should look at? What causes demand for gas to go up? What causes supply of gas to go down?

120 dollars for a day is still pretty good, but the real test is the amount per month.

Liyang said:

You're making a lot of claims here with no references. Sorry, fake news wikipedia won't cut it. Where is the evidence of your claims?

 Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications

I use this book as a reference a lot.

Read 2.2.22-2.2.23, 2.2.25, 3.4, 3.10-3.12, 11.1-11.3, and 12

You will find references for these sections in the back of the book, check their if you want to see empirical studies and proofs of derivations. 

I can't find a PDF online but Modelling Extremal Events by Embrechts, Kluppelberg, and Mikosch is an awesome book about the limitations of normal forecasting and more specifically how to make more robust ones a long with better alternatives. This book should be the bread and butter of finance but for some reason its not. More of a derivations book. 

If you want something interesting to read about that you can just google, look into the shortcomings of VAR forecasts and how they played into the 07-08 financial crises. 

AliceInWonderland said: 

Liyang said:

You're making a lot of claims here with no references. Sorry, fake news wikipedia won't cut it. Where is the evidence of your claims?

 Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications

I use this book as a reference a lot.

Read 2.2.22-2.2.23, 2.2.25, 3.4, 3.10-3.12, 11.1-11.3, and 12

You will find references for these sections in the back of the book, check their if you want to see empirical studies and proofs of derivations. 

I can't find a PDF online but Modelling Extremal Events by Embrechts, Kluppelberg, and Mikosch is an awesome book about the limitations of normal forecasting and more specifically how to make more robust ones a long with better alternatives. This book should be the bread and butter of finance but for some reason its not. More of a derivations book. 

If you want something interesting to read about that you can just google, look into the shortcomings of VAR forecasts and how they played into the 07-08 financial crises. 

 I'll check it out.

WILL THIS BE THE FINAL PEACE

puppygirl said: 

Today I made 120 dollars trading natural gas!  https://energymarkettrader.blogspot.com/2023/02/21723-energy-market-trading-journal.html 

Good day for me :)

 https://www.eia.gov/outlooks/steo/#:~:text=Natural%20gas%20prices.,Energy%20Outlook%20(STEO)%20forecast.

found this site.

"

Notable Forecast Changes 2023 2024
The current STEO forecast was released February 7.
The previous STEO forecast was released January 10.
Natural gas price at Henry Hub (current) (dollars per MMBtu) $3.40 $4.04
Previous forecast $4.90 $4.80
Percentage change -30.5% -15.8%

Natural gas prices. We forecast that the Henry Hub natural gas spot price will average $3.40 per million British thermal units (MMBtu) in 2023, down almost 50% from last year and about 30% from our January Short-Term Energy Outlook (STEO) forecast. We revised our outlook for Henry Hub prices as a result of significantly warmer-than-normal weather in January that led to less-than-normal consumption of natural gas for space heating and pushed inventories above the five-year average.

Natural gas storage. As a result of less-than-normal natural gas consumption in January, natural gas inventories ended the month above their five-year (2018–2022) average. We now expect inventories will close the withdrawal season at the end of March at more than 1.8 trillion cubic feet, 16% more than the five-year average.

U.S. heating degree days (current) 4,083 4,201
Previous forecast 4,158 4,265
Percentage change -1.8% -1.5%

"

Simple supply and demand.

Less demand for gas drives prices down. Also less demand causes the supply to go up.

Looks like weather and electricity generation is on their list of leading indicators for gas usage demand.

Data
Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1997 3.45 2.15 1.89 2.03 2.25 2.20 2.19 2.49 2.88 3.07 3.01 2.35
1998 2.09 2.23 2.24 2.43 2.14 2.17 2.17 1.85 2.02 1.91 2.12 1.72
1999 1.85 1.77 1.79 2.15 2.26 2.30 2.31 2.80 2.55 2.73 2.37 2.36
2000 2.42 2.66 2.79 3.04 3.59 4.29 3.99 4.43 5.06 5.02 5.52 8.90
2001 8.17 5.61 5.23 5.19 4.19 3.72 3.11 2.97 2.19 2.46 2.34 2.30
2002 2.32 2.32 3.03 3.43 3.50 3.26 2.99 3.09 3.55 4.13 4.04 4.74
2003 5.43 7.71 5.93 5.26 5.81 5.82 5.03 4.99 4.62 4.63 4.47 6.13
2004 6.14 5.37 5.39 5.71 6.33 6.27 5.93 5.41 5.15 6.35 6.17 6.58
2005 6.15 6.14 6.96 7.16 6.47 7.18 7.63 9.53 11.75 13.42 10.30 13.05
2006 8.69 7.54 6.89 7.16 6.25 6.21 6.17 7.14 4.90 5.85 7.41 6.73
2007 6.55 8.00 7.11 7.60 7.64 7.35 6.22 6.22 6.08 6.74 7.10 7.11
2008 7.99 8.54 9.41 10.18 11.27 12.69 11.09 8.26 7.67 6.74 6.68 5.82
2009 5.24 4.52 3.96 3.50 3.83 3.80 3.38 3.14 2.99 4.01 3.66 5.35
2010 5.83 5.32 4.29 4.03 4.14 4.80 4.63 4.32 3.89 3.43 3.71 4.25
2011 4.49 4.09 3.97 4.24 4.31 4.54 4.42 4.06 3.90 3.57 3.24 3.17
2012 2.67 2.51 2.17 1.95 2.43 2.46 2.95 2.84 2.85 3.32 3.54 3.34
2013 3.33 3.33 3.81 4.17 4.04 3.83 3.62 3.43 3.62 3.68 3.64 4.24
2014 4.71 6.00 4.90 4.66 4.58 4.59 4.05 3.91 3.92 3.78 4.12 3.48
2015 2.99 2.87 2.83 2.61 2.85 2.78 2.84 2.77 2.66 2.34 2.09 1.93
2016 2.28 1.99 1.73 1.92 1.92 2.59 2.82 2.82 2.99 2.98 2.55 3.59
2017 3.30 2.85 2.88 3.10 3.15 2.98 2.98 2.90 2.98 2.88 3.01 2.82
2018 3.87 2.67 2.69 2.80 2.80 2.97 2.83 2.96 3.00 3.28 4.09 4.04
2019 3.11 2.69 2.95 2.65 2.64 2.40 2.37 2.22 2.56 2.33 2.65 2.22
2020 2.02 1.91 1.79 1.74 1.75 1.63 1.77 2.30 1.92 2.39 2.61 2.59
2021 2.71 5.35 2.62 2.66 2.91 3.26 3.84 4.07 5.16 5.51 5.05 3.76
2022 4.38 4.69 4.90 6.60 8.14 7.70 7.28 8.81 7.88 5.66 5.45 5.53
2023 3.27

ya I've been using the API From eia.gov to gather all the Nat Gas storage storage levels into a single "battery icon" which I hope can display on my nwe app I'm making.