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Word-cloud-for-post-on-large-price-moves

Thoughts on the best BTC price moves

Money, , , ,

As the great and powerful Satoshi Nakamoto once said, “If you don’t believe me or don’t get it, I don’t have time to try to convince you, sorry.”

For a while I have used a piece of code that started as back-testing code and forked it to produce output whenever BTC-USD makes a 2 standard deviation move in price up or down. Basically I manually adjusted the threshold that is normally a fluid value in backtesting to a fixed value, so that 95% of the time a U for Up, or D for Down move is not recorded, It’s counted as O, which means nothing interesting happened and is not printed. CRON runs this hourly and the machine responsible for trading runs 24/7 and collects prices, since August 28,2018. It even plays a sound when one of these outlier moves occurs.

Here are a few lines from the end of the output….

2024-01-03 16:00:02.618000       63874 42197.47 43004.35 U 2 1.02
2024-01-03 19:00:03.434000       63877 42923.71 42269.13 D 2 0.98
O: 43795  95.0%
D: 1165  2.5%
U: 1148  2.5%

The top two lines have a time stamp followed by a tick count, this is where it is in the data set, in order, adding 1 per hour. Next are the two prices, the starting and ending prices, U or D, signalling which way it just went. The next number shows how many hours have elapsed since the last radical move followed by the scale of the move. This is a rounded number that shows what number would be required to multiply the first price to get the current price, how big a move in other words.

2 Standard Deviations for Bitcoin

Daily

I recently adjusted the calibration threshold as BTC is settling down into less radical swings as time goes on. Currently it is….

THRESHOLD = 0.01459 # 0.0156

…which means on an hourly basis a 1.459% move registers as a 2 standard deviation move. It was 1.56% earlier this year, so volatility is trending down over time.

Can that be tradable, not likely as fees and slippage would most likely eat up the 1.459%, granted some higher swings do occur and I have pondered this. What is you bought all the big down moves and sold on the big up moves. Sounds good in theory and would most likely only work in an up market or else, you just wind up buying at bad entries and ‘bag hold’ too much at a loss until the next bull run.

Weekly

I got the idea to look at a daily and weekly time frame. Weekly is such a short output that I will paste it here….

Start of Data: 2018-09-05
Lines of input data: 279
Lines to process: 279
2018-11-21 05:00:11.243000       17809 6268.44 4452.68 D 10 0.71
2019-04-03 04:00:15.519000       17828 3976.88 5050.01 U 18 1.27
2019-05-15 04:00:15.266000       17834 5782.6 8063.78 U 5 1.39
2019-06-26 04:00:16.011000       17840 9139.01 12228.39 U 5 1.34
2019-07-17 04:00:14.972000       17843 12983.74 9411.06 D 2 0.72
2020-03-15 04:00:05.033000       17878 8749.01 5168.75 D 34 0.59
2020-08-02 04:00:06.489000       17898 9686.6 12000.0 U 19 1.24
2020-12-20 05:00:12.005000       17918 18865.0 23460.71 U 19 1.24
2021-01-03 05:00:14.866000       17920 26732.29 33231.68 U 1 1.24
2021-02-14 05:00:19.359000       17926 38365.06 47607.09 U 5 1.24
2021-03-14 05:00:09.349000       17930 49437.95 61191.88 U 3 1.24
2021-08-01 04:00:03.128000       17950 34192.29 42461.2 U 19 1.24
2022-06-19 04:00:02.056000       17996 27515.55 18282.06 D 45 0.66
2023-03-19 04:00:02.137000       18035 20598.23 27282.54 U 38 1.32
D: 4  1.4%
O: 264  95.0%
U: 10  3.6%

A few things to note:

  • There is asymmetry as the market has in general been rising since 2018, so there are only 4 ‘large’ down moves and 10 up. 1.4% weeks are up moves, 3.6% are down. The number after U and D is the weeks between moves.
  • Note the magnitude of the moves. They are truly massive. As the threshold is now 0.235.
  • Note that the 5 sequential up moves in a row 2020-2021 barely made in over the threshold at 1.24
  • Make note of the fact that Bitcoin is calming down over the years by looking at how many times per year it makes these epic moves.
  • Last but not least. I wonder if the what the linear regression of the prices would tell. Perhaps good targets for the future?
Large Weekly BTC Price Moves
Large Weekly BTC Price Moves

From now on, I will monitor this more carefully. As one can see, entry and exit although infrequent would have lead to stellar performance. I’ve been doing crypto TA and writing related code for almost 6 years now and it is still interesting that some new price relationships can be sussed out.

Looking ahead

It’s a guess buy based on the chart above it will be worth seeing if there is a pullback below the price of the blue linear regression termination, 37K-ish down to the termination of the D and E price lines 20598.23 27282.54. It will be interesting to followup on this.

Daily

For daily the THRESHOLD = 0.076, so that means greater that 7.6% moves are counted. Below I have pasted in the 2022 to 2023 results. 2022 being terrible and 2023 awesome. I let the reader ponder the readability of these price moves.

 

2022-02-05 05:00:03.175000       19053 37326.16 41505.25 U 62 1.11
2022-02-24 05:00:04.472000       19072 38042.82 35122.81 D 18 0.92
2022-02-25 05:00:04.643000       19073 35122.81 38740.11 U 0 1.1
2022-03-01 05:00:06.659000       19077 37830.34 43388.77 U 3 1.15
2022-03-09 05:00:02.706000       19085 38554.62 41656.28 U 7 1.08
2022-05-06 04:00:02.780000       19143 39727.55 36446.18 D 57 0.92
2022-05-10 04:00:02.150000       19147 33556.09 30678.51 D 3 0.91
2022-05-12 04:00:02.124000       19149 31191.22 28234.93 D 1 0.91
2022-05-13 04:00:02.622000       19150 28234.93 30523.85 U 0 1.08
2022-06-14 04:00:02.293000       19182 25483.35 22074.01 D 31 0.87
2022-06-17 04:00:02.808000       19185 22182.06 20359.29 D 2 0.92
2022-06-19 04:00:02.056000       19187 20424.2 18282.06 D 1 0.9
2022-06-20 04:00:02.514000       19188 18282.06 19961.12 U 0 1.09
2022-07-08 04:00:02.559000       19206 20465.35 22125.65 U 17 1.08
2022-07-28 04:00:01.952000       19226 21076.25 23137.12 U 19 1.1
2022-09-14 04:00:02.511000       19274 22235.95 20324.97 D 47 0.91
2022-11-10 05:00:02.217000       19331 18242.1 16433.61 D 56 0.9
2023-01-14 05:00:02.777000       19396 18820.73 20901.76 U 64 1.11
2023-01-21 05:00:02.053000       19403 20991.31 22602.95 U 6 1.08
2023-02-16 05:00:02.079000       19429 22092.46 24634.33 U 25 1.12
2023-03-10 05:00:02.124000       19451 21737.7 19877.69 D 21 0.91
2023-03-13 04:00:01.980000       19454 20598.23 22326.45 U 2 1.08
2023-03-14 04:00:02.715000       19455 22326.45 24517.05 U 0 1.1
2023-08-18 04:00:02.811000       19612 28619.6 26395.38 D 156 0.92
2023-10-24 03:00:02.742000       19679 30389.89 34553.97 U 66 1.14
D: 46  2.4%
O: 1856  95.0%
U: 51  2.6%

 

Looking at the plot for daily and remember this does not mean the X axis is time, just moves, even though it does look similar to a price -vs- time chart. In this case both regression lines overlap 100%.

 

Large BTC Daily Price Moves
Large BTC Daily Price Moves

Looking ahead

This is a guess but looking at this I am inclined to think a pullback between the regression line termination at 40K and the termination of the D and E price lines at 30 and 35K would be something to keep and eye out for.

Alternative Daily Move Chart

Taking the geometric mean of the daily and weekly thresholds, the result is 0.133. This produces using a daily tick an output that has as many large price moves as the weekly chart and can be seen as an alternative while slightly different, lines up similarly on the chart. The moves are roughly 3 standard deviation moves in this case.

Start of Data: 2018-08-30
Lines of input data: 1955
Lines to process: 1955
2018-11-20 05:00:17.949000       17880 5495.99 4641.55 D 81 0.84
2018-11-25 05:00:13.877000       17885 4301.01 3632.02 D 4 0.84
2019-04-03 04:00:15.519000       18014 4177.0 5050.01 U 128 1.21
2019-05-14 04:00:15.992000       18055 7016.99 7963.14 U 40 1.13
2019-06-28 04:00:15.313000       18100 12821.64 11110.99 D 44 0.87
2019-10-26 04:00:17.489000       18220 7455.01 9599.17 U 119 1.29
2020-03-13 04:00:04.431000       18359 7659.09 5385.44 D 138 0.7
2020-03-20 04:00:04.240000       18366 5301.62 6163.32 U 6 1.16
2020-04-30 04:00:04.743000       18407 7821.73 9291.9 U 40 1.19
2021-01-06 05:00:08.853000       18658 30880.51 35456.93 U 250 1.15
2021-02-09 05:00:09.512000       18692 38618.01 46888.08 U 33 1.21
2022-03-01 05:00:06.659000       19077 37830.34 43388.77 U 384 1.15
2022-06-14 04:00:02.293000       19182 25483.35 22074.01 D 104 0.87
2023-10-24 03:00:02.742000       19679 30389.89 34553.97 U 496 1.14
U: 9  0.5%
O: 1940  99.3%
D: 5  0.3%

 

Large-BTC-Daily-Moves-w-Linear-Regression-0.133
Large-BTC-Daily-Moves-w-Linear-Regression-0.133

 

Code

Code is on Github, it is quite a hack as it was made from pieces of other backtest code and production code that has not been cleaned or refactored.

https://github.com/erickclasen/cbpro-cli-tools/blob/main/uod.py

Writing

ChatGPT at it’s finest: writing short helpful code

ChatGPT, Scripts, Trading, , , , , , , , , , , , , ,

ChatGPT at it’s finest: writing short helpful code without having to do fancy work.

The following is a dialog with ChatGPT. I had this script that I would normally have to hand edit all of the ticker  symbols by adding them to the end of the lines of code. I am sure there is some clever way using sed or awk but, I figured why not get ChatGPT to help.

ChatGPT then was also able to generate the Tags for this post,by feeding the post into a prompt and I used Stable Diffusion to generate the featured image using the blog title as the prompt. Sometimes it is good to use ChatGPT to proofread as well but, this post was short and simple enough not to do that.

User

I want to show you an output, in the left column are the ticker symbols for a number of cryptocurrencies, I want you to remember all of them as they will be used in the next question that I will pose…..

Currency  Price      Available  Hold      Avail in USD  Held in USD
AAVE 98.16          0.0 0.0              0.0
ACH 0.020353          0.0 0.0              0.0
ADA 0.3859          0.0 0.0              0.0
ALGO 0.1341          0.0 0.0              0.0
AMP 0.00248          0.0 0.0              0.0
ANKR 0.0244          0.0 0.0              0.0
ATOM 9.283          0.0 0.0              0.0
AVAX 20.66          0.0 0.0              0.0
AXS 6.8          0.0 0.0              0.0
BAL 3.68          0.0 0.0              0.0
BAND 1.417          0.0 0.0              0.0
BAT 0.21159          0.0 0.0              0.0
BCH 223.72          0.0 0.0              0.0
BNT 0.75          0.0 0.0              0.0
BOND 2.2          0.0 0.0              0.0
BTC 37964.39          0.0 0.0              0.0
CGLD 0.53          0.0 0.0              0.0
COMP 51.17          0.0 0.0              0.0
CRV 0.5658          0.0 0.0              0.0
CVC 0.1102          0.0 0.0              0.0
DAI 0.9999          0.0 0.0              0.0
DOGE 0.08033          0.0 0.0              0.0
DOT 5.263          0.0 0.0              0.0
ETC 18.86          0.0 0.0              0.0
ETH 2054.84          0.0 0.0              0.0
FARM 32.73          0.0 0.0              0.0
FET 0.5347          0.0 0.0              0.0
FIL 4.545          0.0 0.0              0.0
GLM 0.2418          0.0 0.0              0.0
GRT 0.1483          0.0 0.0              0.0
ICP 4.605          0.0 0.0              0.0
KNC 0.7065          0.0 0.0              0.0
LINK 14.588          0.0 0.0              0.0
LOOM 0.0673          0.0 0.0              0.0
LPT 6.33          0.0 0.0              0.0
LRC 0.216          0.0 0.0              0.0
LTC 69.78          0.0 0.0              0.0
MANA 0.4405          0.0 0.0              0.0
MATIC 0.7528          0.0 0.0              0.0
MIR 0.0941          0.0 0.0              0.0
MKR 1483.6          0.0 0.0              0.0
MLN 15.12          0.0 0.0              0.0
NKN 0.1064          0.0 0.0              0.0
NMR 15.68          0.0 0.0              0.0
OGN 0.12922          0.0 0.0              0.0
OMG 1.7217          0.0 0.0              0.0
OXT 0.0724          0.0 0.0              0.0
PLA 0.204          0.0 0.0              0.0
POLY 0.1963          0.0 0.0              0.0
QNT 99.56          0.0 0.0              0.0
REN 0.0823          0.0 0.0              0.0
REQ 0.083          0.0 0.0              0.0
RLC 1.4794          0.0 0.0              0.0
RLY 0.0126          0.0 0.0              0.0
SKL 0.0436          0.0 0.0              0.0
SNX 3.392          0.0 0.0              0.0
SOL 58.2          0.0 0.0              0.0
STORJ 0.7658          0.0 0.0              0.0
UMA 1.953          0.0 0.0              0.0
UNI 6.161          0.0 0.0              0.0
USD              0.01 0.0
USDC              0.0 0.0
XLM 0.118572          0.0 0.0              0.0
XTZ 0.817          0.0 0.0              0.0
YFI 8519.94          0.0 0.0              0.0
ZEC 29.03          0.0 0.0              0.0
ZRX 0.39338          0.0 0.0              0.0

I don’t have the ability to remember information from one interaction to the next, but I can certainly help you analyze or work with the data you’ve provided in this session. If you have a specific question or task related to this cryptocurrency data, feel free to let me know, and I’ll do my best to assist you!

User

Can you print out all of the symbols in the left column?

Certainly! Here are all the ticker symbols from the left column:

objectivec
AAVE, ACH, ADA, ALGO, AMP, ANKR, ATOM, AVAX, AXS, BAL, BAND, BAT, BCH, BNT, BOND, BTC, CGLD, COMP, CRV, CVC, DAI, DOGE, DOT, ETC, ETH, FARM, FET, FIL, GLM, GRT, ICP, KNC, LINK, LOOM, LPT, LRC, LTC, MANA, MATIC, MIR, MKR, MLN, NKN, NMR, OGN, OMG, OXT, PLA, POLY, QNT, REN, REQ, RLC, RLY, SKL, SNX, SOL, STORJ, UMA, UNI, USD, USDC, XLM, XTZ, YFI, ZEC, ZRX

If you have any specific tasks or questions related to these symbols, feel free to let me know!

User

OK, good, you remembered them. Now for the next step. I am going to show an example using btc and I would like you to create a listing with all of the ticker symbols from the left column. Example: $DIR/rsi-fractals-avgs-bbands-txt-summary.py btc

Sure, I understand. If you could provide an example of the desired output or clarify the specific format or information you’re looking for related to each ticker symbol (similar to the example you provided with btc), I’d be happy to assist you further.

User

I am looking for you to create code that has $DIR/rsi-fractals-avgs-bbands-txt-summary.py on every line but with the ticker symbols from the left column appear on that line right after it.

Certainly! It sounds like you want a list of commands where $DIR/rsi-fractals-avgs-bbands-txt-summary.py is followed by each ticker symbol. Here’s an example Python script that generates those commands:

python
# List of ticker symbols ticker_symbols = [     "AAVE", "ACH", "ADA", "ALGO", "AMP", "ANKR", "ATOM", "AVAX", "AXS", "BAL",     "BAND", "BAT", "BCH", "BNT", "BOND", "BTC", "CGLD", "COMP", "CRV", "CVC",     "DAI", "DOGE", "DOT", "ETC", "ETH", "FARM", "FET", "FIL", "GLM", "GRT", "ICP",     "KNC", "LINK", "LOOM", "LPT", "LRC", "LTC", "MANA", "MATIC", "MIR", "MKR",     "MLN", "NKN", "NMR", "OGN", "OMG", "OXT", "PLA", "POLY", "QNT", "REN", "REQ",     "RLC", "RLY", "SKL", "SNX", "SOL", "STORJ", "UMA", "UNI", "USD", "USDC", "XLM",     "XTZ", "YFI", "ZEC", "ZRX" ] # Directory variable DIR = "$DIR" # Generate commands commands = [f"{DIR}/rsi-fractals-avgs-bbands-txt-summary.py {symbol}" for symbol in ticker_symbols] # Print commands for command in commands:     
       print(command)

Replace $DIR with the actual directory you want to use. This script will generate commands for each ticker symbol, following the format you provided. If you have any specific adjustments or requirements, feel free to let me know!

OK how did it turn out?

This is the output, just as I expected and was looking for. Saved me probably about 15 minutes of tedious hand editing…

$DIR/rsi-fractals-avgs-bbands-txt-summary.py AAVE
$DIR/rsi-fractals-avgs-bbands-txt-summary.py ACH
$DIR/rsi-fractals-avgs-bbands-txt-summary.py ADA
$DIR/rsi-fractals-avgs-bbands-txt-summary.py ALGO
$DIR/rsi-fractals-avgs-bbands-txt-summary.py AMP
$DIR/rsi-fractals-avgs-bbands-txt-summary.py ANKR
$DIR/rsi-fractals-avgs-bbands-txt-summary.py ATOM
$DIR/rsi-fractals-avgs-bbands-txt-summary.py AVAX
$DIR/rsi-fractals-avgs-bbands-txt-summary.py AXS
$DIR/rsi-fractals-avgs-bbands-txt-summary.py BAL
$DIR/rsi-fractals-avgs-bbands-txt-summary.py BAND
$DIR/rsi-fractals-avgs-bbands-txt-summary.py BAT
$DIR/rsi-fractals-avgs-bbands-txt-summary.py BCH
$DIR/rsi-fractals-avgs-bbands-txt-summary.py BNT
$DIR/rsi-fractals-avgs-bbands-txt-summary.py BOND
$DIR/rsi-fractals-avgs-bbands-txt-summary.py BTC
$DIR/rsi-fractals-avgs-bbands-txt-summary.py CGLD
$DIR/rsi-fractals-avgs-bbands-txt-summary.py COMP
$DIR/rsi-fractals-avgs-bbands-txt-summary.py CRV
$DIR/rsi-fractals-avgs-bbands-txt-summary.py CVC
$DIR/rsi-fractals-avgs-bbands-txt-summary.py DAI
$DIR/rsi-fractals-avgs-bbands-txt-summary.py DOGE
$DIR/rsi-fractals-avgs-bbands-txt-summary.py DOT
$DIR/rsi-fractals-avgs-bbands-txt-summary.py ETC
$DIR/rsi-fractals-avgs-bbands-txt-summary.py ETH
$DIR/rsi-fractals-avgs-bbands-txt-summary.py FARM
$DIR/rsi-fractals-avgs-bbands-txt-summary.py FET
$DIR/rsi-fractals-avgs-bbands-txt-summary.py FIL
$DIR/rsi-fractals-avgs-bbands-txt-summary.py GLM
$DIR/rsi-fractals-avgs-bbands-txt-summary.py GRT
$DIR/rsi-fractals-avgs-bbands-txt-summary.py ICP
$DIR/rsi-fractals-avgs-bbands-txt-summary.py KNC
$DIR/rsi-fractals-avgs-bbands-txt-summary.py LINK
$DIR/rsi-fractals-avgs-bbands-txt-summary.py LOOM
$DIR/rsi-fractals-avgs-bbands-txt-summary.py LPT
$DIR/rsi-fractals-avgs-bbands-txt-summary.py LRC
$DIR/rsi-fractals-avgs-bbands-txt-summary.py LTC
$DIR/rsi-fractals-avgs-bbands-txt-summary.py MANA
$DIR/rsi-fractals-avgs-bbands-txt-summary.py MATIC
$DIR/rsi-fractals-avgs-bbands-txt-summary.py MIR
$DIR/rsi-fractals-avgs-bbands-txt-summary.py MKR
$DIR/rsi-fractals-avgs-bbands-txt-summary.py MLN
$DIR/rsi-fractals-avgs-bbands-txt-summary.py NKN
$DIR/rsi-fractals-avgs-bbands-txt-summary.py NMR
$DIR/rsi-fractals-avgs-bbands-txt-summary.py OGN
$DIR/rsi-fractals-avgs-bbands-txt-summary.py OMG
$DIR/rsi-fractals-avgs-bbands-txt-summary.py OXT
$DIR/rsi-fractals-avgs-bbands-txt-summary.py PLA
$DIR/rsi-fractals-avgs-bbands-txt-summary.py POLY
$DIR/rsi-fractals-avgs-bbands-txt-summary.py QNT
$DIR/rsi-fractals-avgs-bbands-txt-summary.py REN
$DIR/rsi-fractals-avgs-bbands-txt-summary.py REQ
$DIR/rsi-fractals-avgs-bbands-txt-summary.py RLC
$DIR/rsi-fractals-avgs-bbands-txt-summary.py RLY
$DIR/rsi-fractals-avgs-bbands-txt-summary.py SKL
$DIR/rsi-fractals-avgs-bbands-txt-summary.py SNX
$DIR/rsi-fractals-avgs-bbands-txt-summary.py SOL
$DIR/rsi-fractals-avgs-bbands-txt-summary.py STORJ
$DIR/rsi-fractals-avgs-bbands-txt-summary.py UMA
$DIR/rsi-fractals-avgs-bbands-txt-summary.py UNI
$DIR/rsi-fractals-avgs-bbands-txt-summary.py USD
$DIR/rsi-fractals-avgs-bbands-txt-summary.py USDC
$DIR/rsi-fractals-avgs-bbands-txt-summary.py XLM
$DIR/rsi-fractals-avgs-bbands-txt-summary.py XTZ
$DIR/rsi-fractals-avgs-bbands-txt-summary.py YFI
$DIR/rsi-fractals-avgs-bbands-txt-summary.py ZEC
$DIR/rsi-fractals-avgs-bbands-txt-summary.py ZRX

What’s it for?

It is screener code that will go into a script that prints out all the cryptos in the list sorted by their position relative to their 20 day moving average. It uses the same logic as used for the Mayer Multiple, except it is on a 20 day MA basis.

X = Current Price / 20DMA

Code

#!/bin/bash 
DIR="/home/erick/python/ta"


rm $DIR/output-rsi-er-price-sma20-multiple.csv

$DIR/rsi-fractals-avgs-bbands-txt-summary.py AAVE
$DIR/rsi-fractals-avgs-bbands-txt-summary.py ACH
$DIR/rsi-fractals-avgs-bbands-txt-summary.py ADA
$DIR/rsi-fractals-avgs-bbands-txt-summary.py ALGO
$DIR/rsi-fractals-avgs-bbands-txt-summary.py AMP
$DIR/rsi-fractals-avgs-bbands-txt-summary.py ANKR
$DIR/rsi-fractals-avgs-bbands-txt-summary.py ATOM
$DIR/rsi-fractals-avgs-bbands-txt-summary.py AVAX
$DIR/rsi-fractals-avgs-bbands-txt-summary.py AXS
$DIR/rsi-fractals-avgs-bbands-txt-summary.py BAL
$DIR/rsi-fractals-avgs-bbands-txt-summary.py BAND
$DIR/rsi-fractals-avgs-bbands-txt-summary.py BAT
$DIR/rsi-fractals-avgs-bbands-txt-summary.py BCH
$DIR/rsi-fractals-avgs-bbands-txt-summary.py BNT
$DIR/rsi-fractals-avgs-bbands-txt-summary.py BOND
$DIR/rsi-fractals-avgs-bbands-txt-summary.py BTC
$DIR/rsi-fractals-avgs-bbands-txt-summary.py CGLD
$DIR/rsi-fractals-avgs-bbands-txt-summary.py COMP
$DIR/rsi-fractals-avgs-bbands-txt-summary.py CRV
$DIR/rsi-fractals-avgs-bbands-txt-summary.py CVC
$DIR/rsi-fractals-avgs-bbands-txt-summary.py DAI
$DIR/rsi-fractals-avgs-bbands-txt-summary.py DOGE
$DIR/rsi-fractals-avgs-bbands-txt-summary.py DOT
$DIR/rsi-fractals-avgs-bbands-txt-summary.py ETC
$DIR/rsi-fractals-avgs-bbands-txt-summary.py ETH
$DIR/rsi-fractals-avgs-bbands-txt-summary.py FARM
$DIR/rsi-fractals-avgs-bbands-txt-summary.py FET
$DIR/rsi-fractals-avgs-bbands-txt-summary.py FIL
$DIR/rsi-fractals-avgs-bbands-txt-summary.py GLM
$DIR/rsi-fractals-avgs-bbands-txt-summary.py GRT
$DIR/rsi-fractals-avgs-bbands-txt-summary.py ICP
$DIR/rsi-fractals-avgs-bbands-txt-summary.py KNC
$DIR/rsi-fractals-avgs-bbands-txt-summary.py LINK
$DIR/rsi-fractals-avgs-bbands-txt-summary.py LOOM
$DIR/rsi-fractals-avgs-bbands-txt-summary.py LPT
$DIR/rsi-fractals-avgs-bbands-txt-summary.py LRC
$DIR/rsi-fractals-avgs-bbands-txt-summary.py LTC
$DIR/rsi-fractals-avgs-bbands-txt-summary.py MANA
$DIR/rsi-fractals-avgs-bbands-txt-summary.py MATIC
$DIR/rsi-fractals-avgs-bbands-txt-summary.py MIR
$DIR/rsi-fractals-avgs-bbands-txt-summary.py MKR
$DIR/rsi-fractals-avgs-bbands-txt-summary.py MLN
$DIR/rsi-fractals-avgs-bbands-txt-summary.py NKN
$DIR/rsi-fractals-avgs-bbands-txt-summary.py NMR
$DIR/rsi-fractals-avgs-bbands-txt-summary.py OGN
$DIR/rsi-fractals-avgs-bbands-txt-summary.py OMG
$DIR/rsi-fractals-avgs-bbands-txt-summary.py OXT
$DIR/rsi-fractals-avgs-bbands-txt-summary.py PLA
$DIR/rsi-fractals-avgs-bbands-txt-summary.py POLY
$DIR/rsi-fractals-avgs-bbands-txt-summary.py QNT
$DIR/rsi-fractals-avgs-bbands-txt-summary.py REN
$DIR/rsi-fractals-avgs-bbands-txt-summary.py REQ
$DIR/rsi-fractals-avgs-bbands-txt-summary.py RLC
$DIR/rsi-fractals-avgs-bbands-txt-summary.py RLY
$DIR/rsi-fractals-avgs-bbands-txt-summary.py SKL
$DIR/rsi-fractals-avgs-bbands-txt-summary.py SNX
$DIR/rsi-fractals-avgs-bbands-txt-summary.py SOL
$DIR/rsi-fractals-avgs-bbands-txt-summary.py STORJ
$DIR/rsi-fractals-avgs-bbands-txt-summary.py UMA
$DIR/rsi-fractals-avgs-bbands-txt-summary.py UNI
$DIR/rsi-fractals-avgs-bbands-txt-summary.py USD
$DIR/rsi-fractals-avgs-bbands-txt-summary.py USDC
$DIR/rsi-fractals-avgs-bbands-txt-summary.py XLM
$DIR/rsi-fractals-avgs-bbands-txt-summary.py XTZ
$DIR/rsi-fractals-avgs-bbands-txt-summary.py YFI
$DIR/rsi-fractals-avgs-bbands-txt-summary.py ZEC
$DIR/rsi-fractals-avgs-bbands-txt-summary.py ZRX


echo "Cur  ,Und,Targt,RSI, ER,20Ml,Mayer Mult."


# Sort by desending price/sma20 multiples
sort -nk6 -t, -r $DIR/output-rsi-er-price-sma20-multiple.csv

the script runs the rsi-fractals-avg-bbands-txt-summary code on all the tickers listed and saves output to…

output-rsi-er-price-sma20-multiple.csv

then sorts the output by the 20sma multiple column

 

Results

Best relative performers on top, worst on bottom. This idea comes from the book Fortune’s Formula and appears as a technique used by Princeton-Newport. Basically sell the top and buy the bottom ( deep value…maybe), or alternativly short the top and long the bottom. Or in the case of crypto the bottom ones might be just be duds that are always behind the pack, maybe don’t even bother with them.

Cur  ,Und,Targt,RSI, ER,20Ml,Mayer Mult.
UNI  ,USD,False,73,0.06,1.18,1.31
SKL  ,USD,False,63,0.07,1.1 ,1.59
FET  ,USD,False,67,0.14,1.09,1.97
SNX  ,USD,False,63,0.09,1.07,1.43
GRT  ,USD,False,60,0.05,1.07,1.37
AXS  ,USD,False,56,0.0 ,1.07,1.2
AMP  ,USD,False,63,0.05,1.06,1.17
MKR  ,USD,False,60,0.13,1.05,1.34
AVAX ,USD,False,61,0.07,1.05,1.61
RLC  ,USD,False,61,0.01,1.04,1.19
OGN  ,USD,False,56,0.05,1.03,1.33
UMA  ,USD,False,52,0.07,1.01,1.13
NMR  ,USD,False,55,0.02,1.01,1.14
MANA ,USD,False,48,0.01,1.01,1.18
DOGE ,USD,False,55,0.02,1.01,1.15
BTC  ,USD,False,54,0.14,1.01,1.27
REQ  ,USD,False,52,0.02,1.0 ,1.1
NKN  ,USD,False,59,0.02,1.0 ,1.1
ICP  ,USD,False,51,0.04,1.0 ,1.14
FARM ,USD,False,53,0.04,1.0 ,1.31
ETH  ,USD,False,47,0.04,1.0 ,1.13
DAI  ,USD,False,44,0.01,1.0 ,1.0
ATOM ,USD,False,49,0.05,1.0 ,1.09
ADA  ,USD,False,57,0.01,1.0 ,1.25
AAVE ,USD,False,52,0.08,1.0 ,1.38
STORJ,USD,False,46,0.13,0.99,2.0
SOL  ,USD,False,51,0.19,0.98,2.12
ALGO ,USD,False,47,0.06,0.98,1.13
ACH  ,USD,False,51,0.02,0.98,1.07
ZEC  ,USD,False,43,0.04,0.97,1.0
XLM  ,USD,False,43,0.05,0.97,1.03
LTC  ,USD,False,42,0.03,0.97,0.89
LINK ,USD,False,46,0.17,0.97,1.8
DOT  ,USD,False,45,0.01,0.97,1.08
COMP ,USD,False,49,0.05,0.97,1.11
QNT  ,USD,False,43,0.02,0.96,0.98
PLA  ,USD,False,45,0.02,0.96,1.19
OXT  ,USD,False,47,0.01,0.96,1.13
ETC  ,USD,False,39,0.0 ,0.96,1.07
CRV  ,USD,False,47,0.08,0.96,0.92
BCH  ,USD,False,36,0.1 ,0.96,1.08
LRC  ,USD,False,42,0.08,0.95,0.99
FIL  ,USD,False,41,0.0 ,0.95,1.14
CGLD ,USD,False,44,0.0 ,0.95,1.07
BAL  ,USD,False,38,0.12,0.95,0.88
XTZ  ,USD,False,38,0.03,0.94,1.03
MLN  ,USD,False,36,0.05,0.94,0.9
BAT  ,USD,False,38,0.01,0.94,1.07
BAND ,USD,False,44,0.01,0.94,1.12
ANKR ,USD,False,43,0.01,0.94,1.06
LPT  ,USD,False,41,0.04,0.92,1.13
GLM  ,USD,False,41,0.02,0.92,1.18
CVC  ,USD,False,48,0.05,0.91,1.27
ZRX  ,USD,False,36,0.09,0.9 ,1.68
YFI  ,USD,False,42,0.04,0.9 ,1.32
MATIC,USD,False,28,0.03,0.9 ,1.1
KNC  ,USD,False,27,0.03,0.9 ,1.11
BNT  ,USD,False,32,0.08,0.9 ,1.56
POLY ,USD,False,30,0.01,0.81,1.2

 

 

ChatGPT will create Tags

Finally by posting this post into a prompt you can ask ChatGPT to generate Tags for the post. I used most of them but trimmed a few that were redundant.