- Running A Full Node - Bitcoin
- Reißerische Prognosen: Countdown zum Bitcoin-Halving ...
- Bitcoin weiter seitwärts: Der langfristige Aufwärtstrend ...
- Bitcoin (BTC) Rallying Towards a Two-Month High Ahead of ...
- Bitcoins kaufen, Bitcoin Kurs bei Bitcoin.de!

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submitted by DieHermetischeGarage to DasOhrIstDerWeg [link] [comments]
Sendung | Titel |
---|---|

BR Kalenderblatt | 12.5.1912 Jungfernfahrt der Wendelsteinbahn (1) |

BR Wissen | Entfremdung - Philosophie der Zerrissenheit (2) |

BR Wissen | Martin Buber - Was ist der Mensch? (3) |

HR Der Tag | Verfolgen statt Löschen! - Hasskommentare im Netz (4) |

SWR Zeitwort | 12.5.1818: Die Württembergische Spar-Casse wird gegründet (5) |

WDR Zeitzeichen | Florence Nightingale, Krankenpflegerin (Geburtstag 12.5.1820) (6) |

WDR Feature | Oury Jalloh (1/5) - Die Leiche ist schuld (7) |

(#) | Info |
---|---|

(1) | Eine Mammutaufgabe und aus der Sicht der Einheimischen ein sinnloses Projekt: Wer sollte mit einem Zug zu Berge fahren wollen? Wir, meinten dazu Touristen. Ihnen kam der Bau der Wendelsteinbahn entgegen. |

(2) | Viele Menschen fühlen sich fremd: im Job, in ihren Beziehungen, in der rasant getakteten Welt. Ihre Sinne verkümmern, sie vereinsamen, werden krank und brennen aus. Warum tun sie sich das an? Gibt es einen Ausweg? (BR 2018) |

(3) | Am Du zum Ich werden: Das ist ein Kernsatz von Bubers dialogischer Philosophie. Denn wir gründen im Miteinander mit der Natur, unseren Mitmenschen und dem Grund des Lebens - und werden so zu dem Menschen, der wir sind. |

(4) | Hass im Netz ist zu einem gesellschaftlichen Problem geworden. Niemand ist nicht gefährdet. Anfänglich setzte der Staat nur auf Löschpflichten der Internetplattformen. Inzwischen sollen die Hetzer spüren, dass ihr verbales Gift auch strafrechtlich relevant ist. Ein besonders unkonventioneller Ansatz findet sich in Hessen. Seit einem halben Jahr kooperieren die Generalstaatsanwaltschaft und ihre Zentralstelle zur Bekämpfung der Internetkriminalität (ZIT) mit der Zivilgesellschaft, u.a. mit HateAid, einer Organisation von Aktivisten, die volksverhetzende Posts und beleidigende Tweets in den Maileingang der Staatsanwälte befördern und anzeigen. Bis Anfang April wurden mehr als 140 Ermittlungsverfahren eingeleitet. Leider mit mäßigem Erfolg. Es fehlt an Kooperationsbereitschaft der Plattformbetreiber, deren Sitz häufig im Ausland ist. Aber auch an Identifizierbarkeit der Menschen hinter den anonymen Accounts. Was könnte helfen? Die grenzüberschreitende Erhebung elektronischer Beweismittel? Eine gesetzliche Meldepflicht für Hassspots? |

(5) | Es war ein Hilfs-Programm "zum Besten der ärmeren Volksklassen". Nach Hungersnöten sollten die Württemberger einen bescheidenen Wohlstand aufbauen können. |

(6) | Zur Legende wurde sie schon zu Lebzeiten als die „Lady mit der Lampe“, als die liebevoll sorgende Krankenschwester im Krimkrieg. Doch Florence Nightingale war viel mehr als die gute Seele, die nachts ihre Runden durch das Lazarett drehte. Sie sorgt dafür, dass die Todesrate in Lazaretten von 42 auf 2 Prozent sinkt, treibt Gesetzesinitiativen zur Kanalisierung von Privathäusern voran, und damit die Lebenserwartung um 20 Jahre nach oben. Ihr Rezept: Hygiene. Autor: Martin Herzog |

(7) | Folge 1: Kaum eine Stunde nach der Entdeckung der verkohlten Leiche haben sich die Verantwortlichen der Dessauer Polizei festgelegt: Der Mann in der Zelle soll sich selbst angezündet haben. Sie werden an dieser Version festhalten; über viele Jahre und Gerichtsverhandlungen hinweg, gegen jede Logik und immer neue Indizien. Es kann nicht sein, was nicht sein darf. // Von Margot Overath / WDR 2020 / www.radiofeature.wdr.de |

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submitted by DieHermetischeGarage to DasOhrIstDerWeg [link] [comments]
Sendung | Titel |
---|---|

BR Kalenderblatt | 06.05.1717 Marion du Faouët wird der weibliche "Robin Hood" der Bretagne (1) |

BR Wissen | Der Mensch ist zur Freiheit verurteilt - Die Philosophie des Existenzialismus (2) |

SWR Zeitwort | 6.5.1848: Preußen schafft die Prügelstrafe ab (3) |

WDR Zeitzeichen | Paul Abraham, Komponist (Todestag 6..5.1960) (4) |

(#) | Info |
---|---|

(1) | Um 1750 machte eine Bande Straßenräuber die Bretagne unsicher. Angeführt wurde sie von einer Frau: Marion du Faouët. Was die junge Bandenführerin antrieb, war ein radikaler Gerechtigkeitssinn. |

(2) | Der Existentialismus ist eine philosophische Richtung und Lebenshaltung: Der Mensch sei zur radikalen Freiheit verurteilt, müsse sein Wesen entwerfen und sich zu diesem Zweck einmischen in die Politik. (BR 2010) |

(3) | Körperliche Züchtigung war seit dem Mittelalter Bestandteil der "peinlichen Strafen". Im Zuge der Reformen von 1848 wurde zuerst in Preußen die Prügel durch Kerker ersetzt. |

(4) | Als der Banker Paul Abraham von risikofreudigen Investitionen und einer ausgeprägten Spielsucht zum ersten Mal in den Ruin getrieben wurde, da hatte er noch 'Ernste Musik' geschrieben. Mit Violinkonzert, Serenade und Streichquartett war Paul Abraham auf dem Weg zum 'klassischen' Komponisten. Doch nur fünf Jahre später gehörte er zu den gefragtesten Unterhaltungsmusikern Europas. Autor: Niklas Rudolph |

Two days ago, PewDiePie (Felix Kjellberg) famous internet personality, announced on his video that he is partnering up with Dlive. Dlive is a relatively new streaming platform which is built on the Lino Blockchain and it claims to be decentralised. submitted by m4nos to Scams [link] [comments] https://www.youtube.com/watch?v=bRG6sy3VaWU&ab_channel=PewDiePie Users buy via paypal "lino" tokens. 1 lino = 0.012 $. Lino is the platform's currency and it's used to donate to the streamers of dlive. On every donation, 90% of those tokens go to the streamer's wallet and 10% goes to the "pool". From this "pool", viewers get rewarded by simply engaging with a streamer (watch his stream, write in the chat etc). For example, if you watch for about 20 mins you get 1 lino. On the lino wallet app you have the option to "lock" your lino tokens (minimum 1000 linos) and get daily returns. This means that you cannot withdraw your locked linos from your account. I didn't find any information about the percentage of the returns but here is a screenshot of my wallet's transactions. https://preview.redd.it/ciwa2iq67wr21.jpg?width=1920&format=pjpg&auto=webp&s=e8788b7e87a7ed16db663384ea71ec3fe57e51b7 UPDATE Today i got 11,3 bonus linos and this means that this comment from wadaafaaak is true. Dlive is obviously a scam, but his argument isn't the reason. The locking is for the staking. The rewards in POS (proof of stake) based currencies are calculated by the amount of coins you stake vs the total coins staking on the network. Adding 1000 coins to your staking amount also increases the total network weight, which means, if nobody else would add more coins to stake, you'd be getting more while they are getting less. But other users will also lock their additional earnings, which means the network weight will increase even more the amount you will earn won't really increase. My initial thought about a stable return percentage was wrong and I sincerely apologise for that. I was in a hurry to expose this because tomorrow (14/4) PewDiePie is sceduled to stream for the first time on this platform. However, I think the following comment also from the same user (wadaafaaak) describes the situation in the best way and that's why i want to highlight it Yes, like I said, the POS system is fine. The problem is the fixed price. The total supply is around 10billion. The fixed price of one token is 1,2 cents. For every token in circulation they need to have 1,2 cents, if they want their system to work (by working I mean, not scamming users and letting them sell for 0.012USD/Lino). For the tokens that are bought by users this is obviously the case ( if they dont spend that money somewhere else). But for their inflation of (max 6.5% stated in the WP) 650millions Lino per year, where does the USD/BTC equivalent come from? And that inflation is released directly on to the "market". Thats 6.5m USD yearly that they'd need to pay with their own funds, just to back their Lino coins with USD. Besides that they need to pay staff, servers etc. Where is all that money coming from? So new users will be paying out the old users, because thats the only income Dlive has. What happens when "USD Cashing out + 6.5m USD/year > USD Buying" ? They solve that temporarily by locking out the funds and not letting users sell for 0.012usd. To me that sounds like a ponzi. Because you depend on the new users coming in and buying Lino, for old users to actually getting their payment. Once the system starts collapsing they will "unfix" the price and allow people to trade whereever they want. That will cause the price to crash to its real value. Same shit bitconnect did once it started collapsing. You can also watch ShortFatOtaku's video on this topic. https://www.youtube.com/watch?v=uAx5JMKiRoM&t=3s&ab_channel=ShortFatOtaku Please spread awarness. |

Original Medium post can be found here: https://medium.com/@spreadstreet/bitcoin-madness-how-to-simulate-bitcoin-prices-in-google-sheets-c61cb42f26ed

You know the scenario...

Bitcoin had another huge increase, but you missed the opportunity. You wanted to get in, but your gut instinct told you no. And rightfully so...no one knows where the price is going to go. What if you invested, and it had another 20% loss? These sort of price movements are common in the volatile world of cryptocurrencies.

Seriously...how far can this Bitcoin price really go?

## BITCOIN IS A VOLATILE BEAST

Risk analysis must be a part of every decision you make.

You are constantly faced with uncertainty, ambiguity, and variability. Variability, in the case of Bitcoin, unlike anything we have ever seen before. And even though we have unprecedented access to information, we can’t accurately predict the future.

Luckily, we have methods that enable you to see all the possible outcomes of your decisions, and assess the impact of risk.

## WHERE TO START?

Running simulations can prepare us for the worst.

Monte Carlo simulation (also known as the Monte Carlo Method) allows for better decision making under uncertainty.

One of the most common ways to estimate risk is the use of a Monte Carlo simulation (MCS). From Investopedia:

## STEP 1. WTF IS GEOMETRIC BROWNIAN MOTION?

The geometric Brownian motion (GBM) is a statistical method that is used heavily in the forecasting of stock prices. The reason the process is so attractive for this is because of the following:

Math geeks have a habit of making things infinitely more complicated than they have to be. I will do my best to make this as simple as possible.

The formula for GBM is as follows:

gBm formula

Where:

For each time period, our model assumes the price will "drift" up by the expected return. But the drift will be shocked (added or subtracted) by a random shock. The random shock will be the standard deviation "s" multiplied by a random number "e". This is simply a way of scaling the standard deviation.

## STEP 1A. THE THUNDER GOD ELI5

**The ELI5 version:** The thunder god Zeus is a great god. A just god.

But Zeus is subject to wild mood swings.

Every day Zeus can shoot his magic lightning into the price of Bitcoin, and cause it to go up or down.

Some days he is in such a good mood, that he shocks the price up by a random amount. On other days, he is in such a poor mood that he shocks the price down for opposing him.

Zeus Striking Down the Price

And thus, we have the essence of GBM: a series of steps with an expected upward drift, where each step is hit with a plus/minus shock (which is a function of the stock's standard deviation).

## STEP 2. HISTORICAL DAILY BITCOIN PRICES

Copy the raw data scores from coinmarketcap. Paste the data into your own spreadsheet.

For this exercise, your columns will be: Time, Open, Close, High, Low, Volume.

Columns Setup OHLCV

Want to automatically pull in Bitcoin prices? Use the Spreadstreet Google Sheets Add-in.

## STEP 3. CALCULATE DAILY RETURNS

Calculate daily returns from the "Close" price. in H2 put the formula:

**Returns** column

Calculate Daily Returns

## STEP 4. NAME THE DAILY RETURNS RANGE

Create a named range from the returns column, called **returns**, to make our life easier. Highlight all the data in column H, i.e. cells H1:H1000, then click on the menu Data > Named ranges… and call the range **returns**:

Name the range returns

## STEP 5. SUMMARY STATISTICS

Set up a small summary table with the close, daily volatility, annual volatility, daily drift, annual drift, and mean drift of our population. The formulas are:

In K1, enter:

**close**.

In K2, enter:

**dailyVolatility**

In K3, enter:

**annualVolatility**

In K4, enter:

**dailyDrift**

In K5, enter:

**annualDrift**

In K6, enter:

**meanDrift**

Create Summary Statistics Table

## STEP 6. SIMULATE A YEAR

Setup the yearly simulation table with Time, Normdist, Log Return, and Simulated Price

### Time

In J12 put 0, and in J13 put:

Time

### Normdist

Let’s set up the normal distribution curve values.

Google Sheets has a formula NORMDIST which calculates the value of the normal distribution function for a given value, mean and standard deviation. Since we ascribe to the random walk theory, we want to use a mean of 0, and a standard deviation of 1.

In K13, put the formula:

**Normdist** column:

Normdist

### Log Return

To get the percentage of daily stock movement, we will calculate log return.

In L13, put the formula:

Log Return

### Simulated Price

Now to the real meat. Let's calculate the simulated Bitcoin price.

In M12 put the Close price, and in M13, put:

Simulated Price

### Forecasted Bitcoin price for one year

Let's see what the pricing data looks like.

Select from M12 to M377, then Insert - Chart and select line chart:

Simulated Price for One Year

We have now successfully completed one simulation. And depending on your results, they could look normal...or downright crazy.

## STEP 7. SIMULATE A YEAR MANY TIMES

We completed one simulation, but we want to run many different trials.

Create a scenario tab, setup a table to simulate 1,000 different one-year trials. In A3 to A1003, put the numbers 1 through 1000.

In B3, put the formula:

Simulate Bitcoin Prices for Many Years

## STEP 8. MULTI-YEAR SUMMARY STATISTICS

Set up a small summary table with the mean, median, standard deviation, min, max, and range of our new population. The formulas are:

## STEP 9. QUICK ANALYSIS OF RESULTS

My results will look different than yours (due to the random nature of NORMDIST and the time you pulled the Bitcoin prices). But let's take a look at the results:

**How to read:** We can be 95% certain that the price of Bitcoin will fall between $3,005, and $81,998 in one year.

**Wait really? Should I buy?** No, this is not telling you to buy. This should be one tool of many to help you in your buying and risk decisions.

Lognormal Distribution of Bitcoin Prices

## CONCLUSION

You now know how to complete a geometric Brownian motion analysis of Bitcoin prices. Congratulations!

Good statistical analysis methods can be scary, but they don't have to be. Here we covered off on a great method for estimating future Bitcoin prices, which can also be applied to other cryptocurrencies.

With this new tool in place, you can be confident in your risk analysis methods by seeing all the possible outcomes of your decisions, and assess the impact of risk.

Deliberate. Analytical. Intelligent.

## WANT YOUR OWN COPY?

Simulate Bitcoin Prices Download

## RELATED POSTS

High-Flyers and Shitcoins: What I Learned from Analyzing CoinMarketCap Data in Google Sheets

7 Smart Ethereum Price Prediction Methods for HODL’ers

## About the Author

John Young is the founder of Spreadstreet, former financial analyst for a big-ass company, and runner-up in the 6th grade spelling bee. He would have invested in Google if he knew about it...and had any money.

He is the author of the Spreadstreet blog, which has over 3 readers (not a typo). He hopes to hit 10, but honestly writing is a lot of work.

submitted by 1kexperimentdotcom to BitcoinMarkets [link] [comments]
You know the scenario...

Bitcoin had another huge increase, but you missed the opportunity. You wanted to get in, but your gut instinct told you no. And rightfully so...no one knows where the price is going to go. What if you invested, and it had another 20% loss? These sort of price movements are common in the volatile world of cryptocurrencies.

Seriously...how far can this Bitcoin price really go?

You are constantly faced with uncertainty, ambiguity, and variability. Variability, in the case of Bitcoin, unlike anything we have ever seen before. And even though we have unprecedented access to information, we can’t accurately predict the future.

Luckily, we have methods that enable you to see all the possible outcomes of your decisions, and assess the impact of risk.

Monte Carlo simulation (also known as the Monte Carlo Method) allows for better decision making under uncertainty.

One of the most common ways to estimate risk is the use of a Monte Carlo simulation (MCS). From Investopedia:

For example, to calculate the value at risk (VaR) of a portfolio, we can run a Monte Carlo simulation that attempts to predict the worst likely loss for a portfolio given a confidence interval over a specified time horizon - we always need to specify two conditions for VaR: confidence and horizon. (For related reading, see The Uses And Limits Of Volatility and Introduction To Value At Risk (VAR) - Part 1 and Part 2.)A MCS can be run with many different models. Our own process will be:

- Specify a model (for here, we will use geometric Brownian motion)
- Get historical daily bitcoin prices
- Calculate daily returns
- Name the daily return range
- Summary statistics
- Simulate a year
- Simulate a year many times
- Multi-year summary statistics
- Quick analysis of results

- The change in price over one period of time is unrelated to the change in price over a disjoint period of time.
- The change in log(price) over any period of time is normally distributed with a distribution depending only on the length of the period.
- Samples of the distribution are continuous, with probability 100%.

Math geeks have a habit of making things infinitely more complicated than they have to be. I will do my best to make this as simple as possible.

The formula for GBM is as follows:

gBm formula

Where:

- B is the bitcoin price
- m or "mu" is the expected return
- s or "sigma" is the standard deviation of returns
- t is time
- e or "epsilon" is the random variable

For each time period, our model assumes the price will "drift" up by the expected return. But the drift will be shocked (added or subtracted) by a random shock. The random shock will be the standard deviation "s" multiplied by a random number "e". This is simply a way of scaling the standard deviation.

But Zeus is subject to wild mood swings.

Every day Zeus can shoot his magic lightning into the price of Bitcoin, and cause it to go up or down.

Some days he is in such a good mood, that he shocks the price up by a random amount. On other days, he is in such a poor mood that he shocks the price down for opposing him.

Zeus Striking Down the Price

And thus, we have the essence of GBM: a series of steps with an expected upward drift, where each step is hit with a plus/minus shock (which is a function of the stock's standard deviation).

For this exercise, your columns will be: Time, Open, Close, High, Low, Volume.

Columns Setup OHLCV

Want to automatically pull in Bitcoin prices? Use the Spreadstreet Google Sheets Add-in.

=LN(C2/B2)Drag it all the way down to the end of the prices to fill the entire

Calculate Daily Returns

Name the range returns

In K1, enter:

=C2and name it

In K2, enter:

=STDEV(returns)and name it

In K3, enter:

=dailyVolatility*SQRT(365)and name it

In K4, enter:

=AVERAGE(returns)and name it

In K5, enter:

=dailyDrift*365and name it

In K6, enter:

=dailyDrift-0.5*dailyVolatility^2and name it

Create Summary Statistics Table

=J12+1Drag it all the way down to your preferred forecast timeframe. Here I simulated a year (365 days), so I copied down to J377

Time

Google Sheets has a formula NORMDIST which calculates the value of the normal distribution function for a given value, mean and standard deviation. Since we ascribe to the random walk theory, we want to use a mean of 0, and a standard deviation of 1.

In K13, put the formula:

=NORMINV(RAND(),0,1)Drag it all the way down to K377 to fill the whole

Normdist

In L13, put the formula:

=meanDrift+dailyVolatility*K13Copy the formula all the way down to L377:

Log Return

In M12 put the Close price, and in M13, put:

=M12*EXP(L13)Copy the formula all the way down to M377:

Simulated Price

Select from M12 to M377, then Insert - Chart and select line chart:

Simulated Price for One Year

We have now successfully completed one simulation. And depending on your results, they could look normal...or downright crazy.

Create a scenario tab, setup a table to simulate 1,000 different one-year trials. In A3 to A1003, put the numbers 1 through 1000.

In B3, put the formula:

=Close*EXP((annualDrift-0.5*annualVolatility^2)+annualVolatility*norminv(rand(),0,1))Copy the formula down all the way. Name this range "scores":

Simulate Bitcoin Prices for Many Years

=AVERAGE(scores) =STDEVP(scores) =MIN(scores) =MAX(scores) =E6-E5Multiyear Summary Statistics

Mean $27,147 Median $16,097 St. Dev $37,243 Min $556 Max $479,586 Range $479,029 3sd $1,486 2sd $3,005 1sd $5,850 Cur $16,098 1sd $43,896 2sd $81,998 3sd $190,129

Lognormal Distribution of Bitcoin Prices

Good statistical analysis methods can be scary, but they don't have to be. Here we covered off on a great method for estimating future Bitcoin prices, which can also be applied to other cryptocurrencies.

With this new tool in place, you can be confident in your risk analysis methods by seeing all the possible outcomes of your decisions, and assess the impact of risk.

Deliberate. Analytical. Intelligent.

7 Smart Ethereum Price Prediction Methods for HODL’ers

He is the author of the Spreadstreet blog, which has over 3 readers (not a typo). He hopes to hit 10, but honestly writing is a lot of work.

Original Medium post can be found here: https://medium.com/@spreadstreet/bitcoin-madness-how-to-simulate-bitcoin-prices-in-google-sheets-c61cb42f26ed

You know the scenario...

Bitcoin had another huge increase, but you missed the opportunity. You wanted to get in, but your gut instinct told you no. And rightfully so...no one knows where the price is going to go. What if you invested, and it had another 20% loss? These sort of price movements are common in the volatile world of cryptocurrencies.

Seriously...how far can this Bitcoin price really go?

## BITCOIN IS A VOLATILE BEAST

Risk analysis must be a part of every decision you make.

You are constantly faced with uncertainty, ambiguity, and variability. Variability, in the case of Bitcoin, unlike anything we have ever seen before. And even though we have unprecedented access to information, we can’t accurately predict the future.

Luckily, we have methods that enable you to see all the possible outcomes of your decisions, and assess the impact of risk.

## WHERE TO START?

Running simulations can prepare us for the worst.

Monte Carlo simulation (also known as the Monte Carlo Method) allows for better decision making under uncertainty.

One of the most common ways to estimate risk is the use of a Monte Carlo simulation (MCS). From Investopedia:

## STEP 1. WTF IS GEOMETRIC BROWNIAN MOTION?

The geometric Brownian motion (GBM) is a statistical method that is used heavily in the forecasting of stock prices. The reason the process is so attractive for this is because of the following:

Math geeks have a habit of making things infinitely more complicated than they have to be. I will do my best to make this as simple as possible.

The formula for GBM is as follows:

gBm formula

Where:

For each time period, our model assumes the price will "drift" up by the expected return. But the drift will be shocked (added or subtracted) by a random shock. The random shock will be the standard deviation "s" multiplied by a random number "e". This is simply a way of scaling the standard deviation.

## STEP 1A. THE THUNDER GOD ELI5

**The ELI5 version:** The thunder god Zeus is a great god. A just god.

But Zeus is subject to wild mood swings.

Every day Zeus can shoot his magic lightning into the price of Bitcoin, and cause it to go up or down.

Some days he is in such a good mood, that he shocks the price up by a random amount. On other days, he is in such a poor mood that he shocks the price down for opposing him.

Zeus Striking Down the Price

And thus, we have the essence of GBM: a series of steps with an expected upward drift, where each step is hit with a plus/minus shock (which is a function of the stock's standard deviation).

## STEP 2. HISTORICAL DAILY BITCOIN PRICES

Copy the raw data scores from coinmarketcap. Paste the data into your own spreadsheet.

For this exercise, your columns will be: Time, Open, Close, High, Low, Volume.

Columns Setup OHLCV

Want to automatically pull in Bitcoin prices? Use the Spreadstreet Google Sheets Add-in.

## STEP 3. CALCULATE DAILY RETURNS

Calculate daily returns from the "Close" price. in H2 put the formula:

**Returns** column

Calculate Daily Returns

## STEP 4. NAME THE DAILY RETURNS RANGE

Create a named range from the returns column, called **returns**, to make our life easier. Highlight all the data in column H, i.e. cells H1:H1000, then click on the menu Data > Named ranges… and call the range **returns**:

Name the range returns

## STEP 5. SUMMARY STATISTICS

Set up a small summary table with the close, daily volatility, annual volatility, daily drift, annual drift, and mean drift of our population. The formulas are:

In K1, enter:

**close**.

In K2, enter:

**dailyVolatility**

In K3, enter:

**annualVolatility**

In K4, enter:

**dailyDrift**

In K5, enter:

**annualDrift**

In K6, enter:

**meanDrift**

Create Summary Statistics Table

## STEP 6. SIMULATE A YEAR

Setup the yearly simulation table with Time, Normdist, Log Return, and Simulated Price

### Time

In J12 put 0, and in J13 put:

Time

### Normdist

Let’s set up the normal distribution curve values.

Google Sheets has a formula NORMDIST which calculates the value of the normal distribution function for a given value, mean and standard deviation. Since we ascribe to the random walk theory, we want to use a mean of 0, and a standard deviation of 1.

In K13, put the formula:

**Normdist** column:

Normdist

### Log Return

To get the percentage of daily stock movement, we will calculate log return.

In L13, put the formula:

Log Return

### Simulated Price

Now to the real meat. Let's calculate the simulated Bitcoin price.

In M12 put the Close price, and in M13, put:

Simulated Price

### Forecasted Bitcoin price for one year

Let's see what the pricing data looks like.

Select from M12 to M377, then Insert - Chart and select line chart:

Simulated Price for One Year

We have now successfully completed one simulation. And depending on your results, they could look normal...or downright crazy.

## STEP 7. SIMULATE A YEAR MANY TIMES

We completed one simulation, but we want to run many different trials.

Create a scenario tab, setup a table to simulate 1,000 different one-year trials. In A3 to A1003, put the numbers 1 through 1000.

In B3, put the formula:

Simulate Bitcoin Prices for Many Years

## STEP 8. MULTI-YEAR SUMMARY STATISTICS

Set up a small summary table with the mean, median, standard deviation, min, max, and range of our new population. The formulas are:

## STEP 9. QUICK ANALYSIS OF RESULTS

My results will look different than yours (due to the random nature of NORMDIST and the time you pulled the Bitcoin prices). But let's take a look at the results:

**How to read:** We can be 95% certain that the price of Bitcoin will fall between $3,005, and $81,998 in one year.

**Wait really? Should I buy?** No, this is not telling you to buy. This should be one tool of many to help you in your buying and risk decisions.

Lognormal Distribution of Bitcoin Prices

## CONCLUSION

You now know how to complete a geometric Brownian motion analysis of Bitcoin prices. Congratulations!

Good statistical analysis methods can be scary, but they don't have to be. Here we covered off on a great method for estimating future Bitcoin prices, which can also be applied to other cryptocurrencies.

With this new tool in place, you can be confident in your risk analysis methods by seeing all the possible outcomes of your decisions, and assess the impact of risk.

Deliberate. Analytical. Intelligent.

## WANT YOUR OWN COPY?

Simulate Bitcoin Prices Download

## RELATED POSTS

High-Flyers and Shitcoins: What I Learned from Analyzing CoinMarketCap Data in Google Sheets

7 Smart Ethereum Price Prediction Methods for HODL’ers

## About the Author

John Young is the founder of Spreadstreet, former financial analyst for a big-ass company, and runner-up in the 6th grade spelling bee. He would have invested in Google if he knew about it...and had any money.

He is the author of the Spreadstreet blog, which has over 3 readers (not a typo). He hopes to hit 10, but honestly writing is a lot of work.

submitted by 1kexperimentdotcom to CryptoMarkets [link] [comments]
You know the scenario...

Bitcoin had another huge increase, but you missed the opportunity. You wanted to get in, but your gut instinct told you no. And rightfully so...no one knows where the price is going to go. What if you invested, and it had another 20% loss? These sort of price movements are common in the volatile world of cryptocurrencies.

Seriously...how far can this Bitcoin price really go?

You are constantly faced with uncertainty, ambiguity, and variability. Variability, in the case of Bitcoin, unlike anything we have ever seen before. And even though we have unprecedented access to information, we can’t accurately predict the future.

Luckily, we have methods that enable you to see all the possible outcomes of your decisions, and assess the impact of risk.

Monte Carlo simulation (also known as the Monte Carlo Method) allows for better decision making under uncertainty.

One of the most common ways to estimate risk is the use of a Monte Carlo simulation (MCS). From Investopedia:

For example, to calculate the value at risk (VaR) of a portfolio, we can run a Monte Carlo simulation that attempts to predict the worst likely loss for a portfolio given a confidence interval over a specified time horizon - we always need to specify two conditions for VaR: confidence and horizon. (For related reading, see The Uses And Limits Of Volatility and Introduction To Value At Risk (VAR) - Part 1 and Part 2.)A MCS can be run with many different models. Our own process will be:

- Specify a model (for here, we will use geometric Brownian motion)
- Get historical daily bitcoin prices
- Calculate daily returns
- Name the daily return range
- Summary statistics
- Simulate a year
- Simulate a year many times
- Multi-year summary statistics
- Quick analysis of results

- The change in price over one period of time is unrelated to the change in price over a disjoint period of time.
- The change in log(price) over any period of time is normally distributed with a distribution depending only on the length of the period.
- Samples of the distribution are continuous, with probability 100%.

Math geeks have a habit of making things infinitely more complicated than they have to be. I will do my best to make this as simple as possible.

The formula for GBM is as follows:

gBm formula

Where:

- B is the bitcoin price
- m or "mu" is the expected return
- s or "sigma" is the standard deviation of returns
- t is time
- e or "epsilon" is the random variable

For each time period, our model assumes the price will "drift" up by the expected return. But the drift will be shocked (added or subtracted) by a random shock. The random shock will be the standard deviation "s" multiplied by a random number "e". This is simply a way of scaling the standard deviation.

But Zeus is subject to wild mood swings.

Every day Zeus can shoot his magic lightning into the price of Bitcoin, and cause it to go up or down.

Some days he is in such a good mood, that he shocks the price up by a random amount. On other days, he is in such a poor mood that he shocks the price down for opposing him.

Zeus Striking Down the Price

And thus, we have the essence of GBM: a series of steps with an expected upward drift, where each step is hit with a plus/minus shock (which is a function of the stock's standard deviation).

For this exercise, your columns will be: Time, Open, Close, High, Low, Volume.

Columns Setup OHLCV

Want to automatically pull in Bitcoin prices? Use the Spreadstreet Google Sheets Add-in.

=LN(C2/B2)Drag it all the way down to the end of the prices to fill the entire

Calculate Daily Returns

Name the range returns

In K1, enter:

=C2and name it

In K2, enter:

=STDEV(returns)and name it

In K3, enter:

=dailyVolatility*SQRT(365)and name it

In K4, enter:

=AVERAGE(returns)and name it

In K5, enter:

=dailyDrift*365and name it

In K6, enter:

=dailyDrift-0.5*dailyVolatility^2and name it

Create Summary Statistics Table

=J12+1Drag it all the way down to your preferred forecast timeframe. Here I simulated a year (365 days), so I copied down to J377

Time

Google Sheets has a formula NORMDIST which calculates the value of the normal distribution function for a given value, mean and standard deviation. Since we ascribe to the random walk theory, we want to use a mean of 0, and a standard deviation of 1.

In K13, put the formula:

=NORMINV(RAND(),0,1)Drag it all the way down to K377 to fill the whole

Normdist

In L13, put the formula:

=meanDrift+dailyVolatility*K13Copy the formula all the way down to L377:

Log Return

In M12 put the Close price, and in M13, put:

=M12*EXP(L13)Copy the formula all the way down to M377:

Simulated Price

Select from M12 to M377, then Insert - Chart and select line chart:

Simulated Price for One Year

We have now successfully completed one simulation. And depending on your results, they could look normal...or downright crazy.

Create a scenario tab, setup a table to simulate 1,000 different one-year trials. In A3 to A1003, put the numbers 1 through 1000.

In B3, put the formula:

=Close*EXP((annualDrift-0.5*annualVolatility^2)+annualVolatility*norminv(rand(),0,1))Copy the formula down all the way. Name this range "scores":

Simulate Bitcoin Prices for Many Years

=AVERAGE(scores) =STDEVP(scores) =MIN(scores) =MAX(scores) =E6-E5Multiyear Summary Statistics

Mean $27,147 Median $16,097 St. Dev $37,243 Min $556 Max $479,586 Range $479,029 3sd $1,486 2sd $3,005 1sd $5,850 Cur $16,098 1sd $43,896 2sd $81,998 3sd $190,129

Lognormal Distribution of Bitcoin Prices

Good statistical analysis methods can be scary, but they don't have to be. Here we covered off on a great method for estimating future Bitcoin prices, which can also be applied to other cryptocurrencies.

With this new tool in place, you can be confident in your risk analysis methods by seeing all the possible outcomes of your decisions, and assess the impact of risk.

Deliberate. Analytical. Intelligent.

7 Smart Ethereum Price Prediction Methods for HODL’ers

He is the author of the Spreadstreet blog, which has over 3 readers (not a typo). He hopes to hit 10, but honestly writing is a lot of work.

Original Medium post can be found here: https://medium.com/@spreadstreet/bitcoin-madness-how-to-simulate-bitcoin-prices-in-google-sheets-c61cb42f26ed

You know the scenario...

Bitcoin had another huge increase, but you missed the opportunity. You wanted to get in, but your gut instinct told you no. And rightfully so...no one knows where the price is going to go. What if you invested, and it had another 20% loss? These sort of price movements are common in the volatile world of cryptocurrencies.

Seriously...how far can this Bitcoin price really go?

## BITCOIN IS A VOLATILE BEAST

Risk analysis must be a part of every decision you make.

You are constantly faced with uncertainty, ambiguity, and variability. Variability, in the case of Bitcoin, unlike anything we have ever seen before. And even though we have unprecedented access to information, we can’t accurately predict the future.

Luckily, we have methods that enable you to see all the possible outcomes of your decisions, and assess the impact of risk.

## WHERE TO START?

Running simulations can prepare us for the worst.

Monte Carlo simulation (also known as the Monte Carlo Method) allows for better decision making under uncertainty.

One of the most common ways to estimate risk is the use of a Monte Carlo simulation (MCS). From Investopedia:

## STEP 1. WTF IS GEOMETRIC BROWNIAN MOTION?

The geometric Brownian motion (GBM) is a statistical method that is used heavily in the forecasting of stock prices. The reason the process is so attractive for this is because of the following:

Math geeks have a habit of making things infinitely more complicated than they have to be. I will do my best to make this as simple as possible.

The formula for GBM is as follows:

gBm formula

Where:

For each time period, our model assumes the price will "drift" up by the expected return. But the drift will be shocked (added or subtracted) by a random shock. The random shock will be the standard deviation "s" multiplied by a random number "e". This is simply a way of scaling the standard deviation.

## STEP 1A. THE THUNDER GOD ELI5

**The ELI5 version:** The thunder god Zeus is a great god. A just god.

But Zeus is subject to wild mood swings.

Every day Zeus can shoot his magic lightning into the price of Bitcoin, and cause it to go up or down.

Some days he is in such a good mood, that he shocks the price up by a random amount. On other days, he is in such a poor mood that he shocks the price down for opposing him.

Zeus Striking Down the Price

And thus, we have the essence of GBM: a series of steps with an expected upward drift, where each step is hit with a plus/minus shock (which is a function of the stock's standard deviation).

## STEP 2. HISTORICAL DAILY BITCOIN PRICES

Copy the raw data scores from coinmarketcap. Paste the data into your own spreadsheet.

For this exercise, your columns will be: Time, Open, Close, High, Low, Volume.

Columns Setup OHLCV

Want to automatically pull in Bitcoin prices? Use the Spreadstreet Google Sheets Add-in.

## STEP 3. CALCULATE DAILY RETURNS

Calculate daily returns from the "Close" price. in H2 put the formula:

**Returns** column

Calculate Daily Returns

## STEP 4. NAME THE DAILY RETURNS RANGE

Create a named range from the returns column, called **returns**, to make our life easier. Highlight all the data in column H, i.e. cells H1:H1000, then click on the menu Data > Named ranges… and call the range **returns**:

Name the range returns

## STEP 5. SUMMARY STATISTICS

Set up a small summary table with the close, daily volatility, annual volatility, daily drift, annual drift, and mean drift of our population. The formulas are:

In K1, enter:

**close**.

In K2, enter:

**dailyVolatility**

In K3, enter:

**annualVolatility**

In K4, enter:

**dailyDrift**

In K5, enter:

**annualDrift**

In K6, enter:

**meanDrift**

Create Summary Statistics Table

## STEP 6. SIMULATE A YEAR

Setup the yearly simulation table with Time, Normdist, Log Return, and Simulated Price

### Time

In J12 put 0, and in J13 put:

Time

### Normdist

Let’s set up the normal distribution curve values.

Google Sheets has a formula NORMDIST which calculates the value of the normal distribution function for a given value, mean and standard deviation. Since we ascribe to the random walk theory, we want to use a mean of 0, and a standard deviation of 1.

In K13, put the formula:

**Normdist** column:

Normdist

### Log Return

To get the percentage of daily stock movement, we will calculate log return.

In L13, put the formula:

Log Return

### Simulated Price

Now to the real meat. Let's calculate the simulated Bitcoin price.

In M12 put the Close price, and in M13, put:

Simulated Price

### Forecasted Bitcoin price for one year

Let's see what the pricing data looks like.

Select from M12 to M377, then Insert - Chart and select line chart:

Simulated Price for One Year

We have now successfully completed one simulation. And depending on your results, they could look normal...or downright crazy.

## STEP 7. SIMULATE A YEAR MANY TIMES

We completed one simulation, but we want to run many different trials.

Create a scenario tab, setup a table to simulate 1,000 different one-year trials. In A3 to A1003, put the numbers 1 through 1000.

In B3, put the formula:

Simulate Bitcoin Prices for Many Years

## STEP 8. MULTI-YEAR SUMMARY STATISTICS

Set up a small summary table with the mean, median, standard deviation, min, max, and range of our new population. The formulas are:

## STEP 9. QUICK ANALYSIS OF RESULTS

My results will look different than yours (due to the random nature of NORMDIST and the time you pulled the Bitcoin prices). But let's take a look at the results:

**How to read:** We can be 95% certain that the price of Bitcoin will fall between $3,005, and $81,998 in one year.

**Wait really? Should I buy?** No, this is not telling you to buy. This should be one tool of many to help you in your buying and risk decisions.

Lognormal Distribution of Bitcoin Prices

## CONCLUSION

You now know how to complete a geometric Brownian motion analysis of Bitcoin prices. Congratulations!

Good statistical analysis methods can be scary, but they don't have to be. Here we covered off on a great method for estimating future Bitcoin prices, which can also be applied to other cryptocurrencies.

With this new tool in place, you can be confident in your risk analysis methods by seeing all the possible outcomes of your decisions, and assess the impact of risk.

Deliberate. Analytical. Intelligent.

## WANT YOUR OWN COPY?

Simulate Bitcoin Prices Download

## RELATED POSTS

High-Flyers and Shitcoins: What I Learned from Analyzing CoinMarketCap Data in Google Sheets

7 Smart Ethereum Price Prediction Methods for HODL’ers

## About the Author

John Young is the founder of Spreadstreet, former financial analyst for a big-ass company, and runner-up in the 6th grade spelling bee. He would have invested in Google if he knew about it...and had any money.

He is the author of the Spreadstreet blog, which has over 3 readers (not a typo). He hopes to hit 10, but honestly writing is a lot of work.

submitted by 1kexperimentdotcom to Bitcoin [link] [comments]
You know the scenario...

Bitcoin had another huge increase, but you missed the opportunity. You wanted to get in, but your gut instinct told you no. And rightfully so...no one knows where the price is going to go. What if you invested, and it had another 20% loss? These sort of price movements are common in the volatile world of cryptocurrencies.

Seriously...how far can this Bitcoin price really go?

You are constantly faced with uncertainty, ambiguity, and variability. Variability, in the case of Bitcoin, unlike anything we have ever seen before. And even though we have unprecedented access to information, we can’t accurately predict the future.

Luckily, we have methods that enable you to see all the possible outcomes of your decisions, and assess the impact of risk.

Monte Carlo simulation (also known as the Monte Carlo Method) allows for better decision making under uncertainty.

One of the most common ways to estimate risk is the use of a Monte Carlo simulation (MCS). From Investopedia:

For example, to calculate the value at risk (VaR) of a portfolio, we can run a Monte Carlo simulation that attempts to predict the worst likely loss for a portfolio given a confidence interval over a specified time horizon - we always need to specify two conditions for VaR: confidence and horizon. (For related reading, see The Uses And Limits Of Volatility and Introduction To Value At Risk (VAR) - Part 1 and Part 2.)A MCS can be run with many different models. Our own process will be:

- Specify a model (for here, we will use geometric Brownian motion)
- Get historical daily bitcoin prices
- Calculate daily returns
- Name the daily return range
- Summary statistics
- Simulate a year
- Simulate a year many times
- Multi-year summary statistics
- Quick analysis of results

- The change in price over one period of time is unrelated to the change in price over a disjoint period of time.
- The change in log(price) over any period of time is normally distributed with a distribution depending only on the length of the period.
- Samples of the distribution are continuous, with probability 100%.

Math geeks have a habit of making things infinitely more complicated than they have to be. I will do my best to make this as simple as possible.

The formula for GBM is as follows:

gBm formula

Where:

- B is the bitcoin price
- m or "mu" is the expected return
- s or "sigma" is the standard deviation of returns
- t is time
- e or "epsilon" is the random variable

For each time period, our model assumes the price will "drift" up by the expected return. But the drift will be shocked (added or subtracted) by a random shock. The random shock will be the standard deviation "s" multiplied by a random number "e". This is simply a way of scaling the standard deviation.

But Zeus is subject to wild mood swings.

Every day Zeus can shoot his magic lightning into the price of Bitcoin, and cause it to go up or down.

Some days he is in such a good mood, that he shocks the price up by a random amount. On other days, he is in such a poor mood that he shocks the price down for opposing him.

Zeus Striking Down the Price

And thus, we have the essence of GBM: a series of steps with an expected upward drift, where each step is hit with a plus/minus shock (which is a function of the stock's standard deviation).

For this exercise, your columns will be: Time, Open, Close, High, Low, Volume.

Columns Setup OHLCV

Want to automatically pull in Bitcoin prices? Use the Spreadstreet Google Sheets Add-in.

=LN(C2/B2)Drag it all the way down to the end of the prices to fill the entire

Calculate Daily Returns

Name the range returns

In K1, enter:

=C2and name it

In K2, enter:

=STDEV(returns)and name it

In K3, enter:

=dailyVolatility*SQRT(365)and name it

In K4, enter:

=AVERAGE(returns)and name it

In K5, enter:

=dailyDrift*365and name it

In K6, enter:

=dailyDrift-0.5*dailyVolatility^2and name it

Create Summary Statistics Table

=J12+1Drag it all the way down to your preferred forecast timeframe. Here I simulated a year (365 days), so I copied down to J377

Time

Google Sheets has a formula NORMDIST which calculates the value of the normal distribution function for a given value, mean and standard deviation. Since we ascribe to the random walk theory, we want to use a mean of 0, and a standard deviation of 1.

In K13, put the formula:

=NORMINV(RAND(),0,1)Drag it all the way down to K377 to fill the whole

Normdist

In L13, put the formula:

=meanDrift+dailyVolatility*K13Copy the formula all the way down to L377:

Log Return

In M12 put the Close price, and in M13, put:

=M12*EXP(L13)Copy the formula all the way down to M377:

Simulated Price

Select from M12 to M377, then Insert - Chart and select line chart:

Simulated Price for One Year

We have now successfully completed one simulation. And depending on your results, they could look normal...or downright crazy.

Create a scenario tab, setup a table to simulate 1,000 different one-year trials. In A3 to A1003, put the numbers 1 through 1000.

In B3, put the formula:

=Close*EXP((annualDrift-0.5*annualVolatility^2)+annualVolatility*norminv(rand(),0,1))Copy the formula down all the way. Name this range "scores":

Simulate Bitcoin Prices for Many Years

=AVERAGE(scores) =STDEVP(scores) =MIN(scores) =MAX(scores) =E6-E5Multiyear Summary Statistics

Mean $27,147 Median $16,097 St. Dev $37,243 Min $556 Max $479,586 Range $479,029 3sd $1,486 2sd $3,005 1sd $5,850 Cur $16,098 1sd $43,896 2sd $81,998 3sd $190,129

Lognormal Distribution of Bitcoin Prices

Good statistical analysis methods can be scary, but they don't have to be. Here we covered off on a great method for estimating future Bitcoin prices, which can also be applied to other cryptocurrencies.

With this new tool in place, you can be confident in your risk analysis methods by seeing all the possible outcomes of your decisions, and assess the impact of risk.

Deliberate. Analytical. Intelligent.

7 Smart Ethereum Price Prediction Methods for HODL’ers

He is the author of the Spreadstreet blog, which has over 3 readers (not a typo). He hopes to hit 10, but honestly writing is a lot of work.

submitted by HailCorporateRobot to PotentialHailCorp [link] [comments]

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