Repository: Zaczero/CBBI
Branch: main
Commit: efd6dcf08b9b
Files: 26
Total size: 89.5 KB
Directory structure:
gitextract_j3fsxwr5/
├── .envrc
├── .gitattributes
├── .gitignore
├── .vscode/
│ └── launch.json
├── LICENSE
├── README.md
├── api/
│ ├── cbbiinfo_api.py
│ ├── coinmetrics_api.py
│ └── coinsoto_api.py
├── asciinema/
│ └── thumbnail.xcf
├── fetch_bitcoin_data.py
├── main.py
├── metrics/
│ ├── _common.py
│ ├── base_metric.py
│ ├── mvrv_z_score.py
│ ├── pi_cycle.py
│ ├── puell_multiple.py
│ ├── reserve_risk.py
│ ├── rhodl_ratio.py
│ ├── rupl.py
│ ├── trolololo.py
│ ├── two_year_moving_average.py
│ └── woobull_topcap_cvdd.py
├── pyproject.toml
├── shell.nix
└── utils.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .envrc
================================================
# shellcheck disable=SC2148
use nix
================================================
FILE: .gitattributes
================================================
# Auto detect text files and perform LF normalization
* text=auto
================================================
FILE: .gitignore
================================================
# Created by https://www.toptal.com/developers/gitignore/api/dotenv,python,pycharm,visualstudiocode,direnv
# Edit at https://www.toptal.com/developers/gitignore?templates=dotenv,python,pycharm,visualstudiocode,direnv
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# AWS User-specific
.idea/**/aws.xml
# Generated files
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# Sensitive or high-churn files
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.idea/**/dataSources.ids
.idea/**/dataSources.local.xml
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# filecache
*.cache.*
*.cache
# application specific
output
*.ignore
================================================
FILE: .vscode/launch.json
================================================
{
"configurations": [
{
"console": "integratedTerminal",
"name": "Python Debugger: Current File",
"program": "${file}",
"request": "launch",
"type": "debugpy"
},
{
"console": "integratedTerminal",
"justMyCode": true,
"name": "Python: main.py",
"program": "${workspaceFolder}/main.py",
"request": "launch",
"type": "debugpy"
}
],
"version": "0.2.0"
}
================================================
FILE: LICENSE
================================================
GNU AFFERO GENERAL PUBLIC LICENSE
Version 3, 19 November 2007
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================================================
FILE: README.md
================================================
# ColinTalksCrypto Bitcoin Bull Run Index (CBBI)
[](https://github.com/Zaczero/CBBI)
The official Python implementation of the **ColinTalksCrypto Bitcoin Bull Run Index** (CBBI).
The CBBI is a Bitcoin index that utilizes advanced, real-time analysis of 9 metrics
to help us understand what stage of the Bitcoin bull run and bear market cycles we are in.
The CBBI is time-independent and price-independent.
It simply indicates whether it believes we are approaching the top/bottom of a Bitcoin cycle.
If you want to learn more, [check out this video](https://www.youtube.com/watch?v=bq7djf1n0j4).
## Visit our website
Bookmark it and receive latest CBBI updates.
- [CBBI.info](https://cbbi.info/)
## Script Demo
[](https://asciinema.org/a/KFkbKPULf9PGvY8Fmh4QLn0FE)
## Docker Usage
To use the CBBI script with Docker, run the following command:
```sh
docker run --rm --pull=always zaczero/cbbi --help
docker run --rm --pull=always zaczero/cbbi
```
## Manual Usage
To use the CBBI script without Docker, follow these two simple steps:
### 1. Install nix
Before you jump in, make sure to install the [❄️ Nix](https://nixos.org/download) package manager. It's your shortcut to seamless dependency management and reproducible environment setup. It will save you lots of time and spare you from unnecessary stress.
### 2. Run the application
```sh
nix-shell --run "python main.py --help"
```
#### or with using interactive shell
```sh
nix-shell
python main.py --help
```
## Metrics
The current CBBI version _(November 2022)_ includes the following metrics:
| Name | Link |
| ---------------------------- | --------------------------------------------------------------------- |
| Pi Cycle Top Indicator | [Visit page](https://coinank.com/indexdata/piCycleTop) |
| RUPL/NUPL Chart | [Visit page](https://coinank.com/indexdata/realizedProf) |
| RHODL Ratio | [Visit page](https://coinank.com/indexdata/rhodlRatio) |
| Puell Multiple | [Visit page](https://coinank.com/indexdata/puellMultiple) |
| 2 Year Moving Average | [Visit page](https://coinank.com/indexdata/year2MA) |
| Bitcoin Trolololo Trend Line | [Visit page](https://www.blockchaincenter.net/bitcoin-rainbow-chart/) |
| MVRV Z-Score | [Visit page](https://coinank.com/indexdata/score) |
| Reserve Risk | [Visit page](https://coinank.com/indexdata/reserveRisk) |
| Woobull Top Cap vs CVDD | [Visit page](https://woocharts.com/bitcoin-price-models/) |
## Environment Variables
This project supports `.env` files, which provide a convenient way of setting environment variables.
To use this feature, create a file called `.env` in the project's root directory,
and add environment variables in the following format:
```sh
VARIABLE_NAME=value
```
### TELEGRAM_TOKEN, TELEGRAM_CHAT_ID
Define both variables to receive Telegram notifications about metric errors that occur during the execution.
#### Example usage
- TELEGRAM_TOKEN=REPLACE_ME
- TELEGRAM_CHAT_ID=123456
## Footer
### Contact me
<https://monicz.dev/#get-in-touch>
### License
This project is licensed under the GNU Affero General Public License v3.0.
The complete license text can be accessed in the repository at [LICENSE](https://github.com/Zaczero/CBBI/blob/main/LICENSE).
================================================
FILE: api/cbbiinfo_api.py
================================================
import polars as pl
from utils import HTTP
def cbbi_fetch(key: str):
response = HTTP.get('https://colintalkscrypto.com/cbbi/data/latest.json')
response.raise_for_status()
response_data = response.json()[key]
return (
pl
.DataFrame({
'Date': pl.Series([int(k) for k in response_data], dtype=pl.Int64),
'Value': pl.Series(list(response_data.values()), dtype=pl.Float64),
})
.with_columns(
Date=(
pl
.from_epoch(pl.col('Date'), time_unit='s')
.dt.cast_time_unit('us')
.dt.replace_time_zone('UTC')
),
)
.select('Date', 'Value')
)
================================================
FILE: api/coinmetrics_api.py
================================================
import polars as pl
from utils import HTTP
def cm_fetch_asset_metrics(
*,
asset: str,
metrics: list[str],
frequency: str = '1d',
start_time: str,
page_size: int = 10_000,
null_as_zero: bool = False,
):
"""
Fetch Coin Metrics Community time series asset metrics.
Notes:
- Coin Metrics returns numbers as strings. This helper casts metric columns to numeric.
"""
response = HTTP.get(
'https://community-api.coinmetrics.io/v4/timeseries/asset-metrics',
params={
'assets': asset,
'metrics': ','.join(metrics),
'frequency': frequency,
'start_time': start_time,
'paging_from': 'start',
'page_size': page_size,
'sort': 'time',
'null_as_zero': str(null_as_zero).lower(),
},
)
response.raise_for_status()
response_json = response.json()
data = response_json['data']
while response_json.get('next_page_url'):
response = HTTP.get(response_json['next_page_url'])
response.raise_for_status()
response_json = response.json()
data.extend(response_json['data'])
return (
pl
.from_dicts(data, infer_schema_length=None)
.with_columns(
*[pl.col(col).cast(pl.Float64) for col in metrics if col in data[0]],
Date=pl.col('time').str.to_datetime(time_zone='UTC').dt.truncate('1d'),
)
.drop('time')
)
================================================
FILE: api/coinsoto_api.py
================================================
import polars as pl
from utils import HTTP
def cs_fetch(path: str, data_selector: str, col_name: str):
response = HTTP.get(f'https://api.coinank.com/indicatorapi/{path}')
response.raise_for_status()
data = response.json()['data']
if 'timeList' not in data and 'line' in data:
data = data['line']
data_x = data['timeList']
data_y = data[data_selector]
return (
pl
.DataFrame({
'Date': data_x[: len(data_y)],
col_name: data_y,
})
.with_columns(
Date=(
pl
.from_epoch(pl.col('Date'), time_unit='ms')
.dt.cast_time_unit('us')
.dt.replace_time_zone('UTC')
)
)
.select('Date', col_name)
)
================================================
FILE: fetch_bitcoin_data.py
================================================
import polars as pl
from api.coinmetrics_api import cm_fetch_asset_metrics
from utils import mark_days_since, mark_highs_lows
def fetch_coinmetrics_data():
"""
Fetch historical Bitcoin blockchain data from Coin Metrics Community API.
"""
df_cm = cm_fetch_asset_metrics(
asset='btc',
metrics=['BlkCnt', 'IssTotNtv', 'IssTotUSD', 'PriceUSD'],
start_time='2009-01-03',
)
df = df_cm.select(
Date=pl.col('Date'),
TotalBlocks=pl.col('BlkCnt'),
IssTotNtv=pl.col('IssTotNtv'),
IssTotUSD=pl.col('IssTotUSD'),
Price=pl.col('PriceUSD'),
)
avg_subsidy = (
pl
.when(pl.col('TotalBlocks') > 0)
.then(pl.col('IssTotNtv') / pl.col('TotalBlocks'))
.otherwise(None)
)
subsidy_floor = pl.lit(50.0) / (
pl.lit(2.0) ** (pl.lit(50.0) / avg_subsidy).log(base=2).ceil()
)
df = df.with_columns(
Halving=(
subsidy_floor.is_not_null()
& subsidy_floor.shift(1).is_not_null()
& (subsidy_floor != subsidy_floor.shift(1))
)
)
return df.select(
'Date',
'IssTotUSD',
'Price',
'Halving',
)
def fetch_bitcoin_data():
"""
Fetches historical Bitcoin data into a DataFrame.
Very early data is discarded due to high volatility.
"""
print('📈 Requesting historical Bitcoin data…')
df = fetch_coinmetrics_data()
df = df.with_columns(
PuellMultiple=(
pl.col('IssTotUSD')
/ pl.col('IssTotUSD').rolling_mean(window_size=365, min_samples=365)
),
Price730DMA=pl.col('Price').rolling_mean(window_size=730, min_samples=1),
)
df = df.filter(pl.col('Date') >= pl.datetime(2011, 6, 27, time_zone='UTC'))
df = df.with_columns(
PriceLog=pl.col('Price').log(),
)
df = mark_highs_lows(df, 'Price', False, 365 * 2, 180)
# move 2021' peak to the first price peak
df = df.with_columns(
PriceHigh=(
pl
.when(pl.col('Date') == pl.datetime(2021, 11, 8, time_zone='UTC'))
.then(False)
.when(pl.col('Date') == pl.datetime(2021, 4, 14, time_zone='UTC'))
.then(True)
.otherwise(pl.col('PriceHigh'))
)
)
return mark_days_since(df, ['PriceLow', 'Halving'])
================================================
FILE: main.py
================================================
import asyncio
import math
import time
import traceback
from pathlib import Path
import fire
import numpy as np
import polars as pl
import seaborn as sns
from matplotlib import pyplot as plt
from pyfiglet import figlet_format
from sty import bg, ef, fg, rs
from tqdm import tqdm
from fetch_bitcoin_data import fetch_bitcoin_data
from metrics.mvrv_z_score import MVRVMetric
from metrics.pi_cycle import PiCycleMetric
from metrics.puell_multiple import PuellMetric
from metrics.reserve_risk import ReserveRiskMetric
from metrics.rhodl_ratio import RHODLMetric
from metrics.rupl import RUPLMetric
from metrics.trolololo import TrolololoMetric
from metrics.two_year_moving_average import TwoYearMovingAverageMetric
from metrics.woobull_topcap_cvdd import WoobullMetric
from utils import format_percentage, get_color
def get_metrics():
"""
Returns a list of available metrics to be calculated.
"""
return [
PiCycleMetric(),
RUPLMetric(),
RHODLMetric(),
PuellMetric(),
TwoYearMovingAverageMetric(),
TrolololoMetric(),
MVRVMetric(),
ReserveRiskMetric(),
WoobullMetric(),
]
def _json_number(value: float | None, *, precision: int = 4):
if value is None or (isinstance(value, float) and math.isnan(value)):
return 'null'
s = f'{value:.{precision}f}'.rstrip('0')
if s.endswith('.'):
s += '0'
return s
def _write_columns_orient_json(df: pl.DataFrame, path: Path, *, precision: int = 4):
ts = df.get_column('Date').dt.epoch(time_unit='s').to_list()
cols = [c for c in df.columns if c != 'Date']
with path.open('w', encoding='utf-8') as f:
f.write('{\n')
for col_i, col in enumerate(cols):
f.write(f' "{col}":{{\n')
values = df.get_column(col).to_list()
for row_i, (t, v) in enumerate(zip(ts, values, strict=True)):
comma = ',' if row_i < len(ts) - 1 else ''
f.write(f' "{t}":{_json_number(v, precision=precision)}{comma}\n')
col_comma = ',' if col_i < len(cols) - 1 else ''
f.write(f' }}{col_comma}\n')
f.write('}')
def _add_common_markers(
ax, *, halvings: np.ndarray, highs: np.ndarray, lows: np.ndarray
):
for dt in halvings:
ax.axvline(x=dt, color='navy', linestyle=':', linewidth=0.5)
for dt in highs:
ax.axvline(x=dt, color='green', linestyle=':', linewidth=0.5)
for dt in lows:
ax.axvline(x=dt, color='red', linestyle=':', linewidth=0.5)
def _shade_metric_bounds(ax):
ax.axhline(y=1, color='black', linewidth=0.5)
ax.axhline(y=0, color='black', linewidth=0.5)
y_min, y_max = ax.get_ylim()
ax.fill_betweenx(
y=[1, y_max],
x1=0,
x2=1,
transform=ax.get_yaxis_transform(),
color='black',
alpha=0.1,
edgecolor='none',
zorder=0,
)
ax.fill_betweenx(
y=[y_min, 0],
x1=0,
x2=1,
transform=ax.get_yaxis_transform(),
color='black',
alpha=0.1,
edgecolor='none',
zorder=0,
)
async def run(json_file: str, charts_file: str, output_dir: str | None):
output_dir_path = Path.cwd() if output_dir is None else Path(output_dir)
json_file_path = output_dir_path / Path(json_file)
charts_file_path = output_dir_path / Path(charts_file)
output_dir_path.mkdir(mode=0o755, parents=True, exist_ok=True)
df_bitcoin = fetch_bitcoin_data()
current_price = df_bitcoin.get_column('Price')[-1]
print(
'Current Bitcoin price: '
+ ef.b
+ fg.li_green
+ bg.da_green
+ f' $ {round(current_price):,} '
+ rs.all
)
metrics = get_metrics()
metrics_cols = []
metrics_descriptions = []
sns.set_theme(
font_scale=0.225,
rc={
'figure.titlesize': 12,
'axes.titlesize': 7.5,
'axes.labelsize': 6,
'xtick.labelsize': 4,
'ytick.labelsize': 4,
'lines.linewidth': 0.5,
'grid.linewidth': 0.3,
'savefig.dpi': 1000,
'figure.dpi': 300,
},
)
x = df_bitcoin.get_column('Date').to_numpy()
price_log = df_bitcoin.get_column('PriceLog').to_numpy()
price = (price_log - price_log.min()) / (price_log.max() - price_log.min())
halvings = x[df_bitcoin.get_column('Halving').to_numpy()]
highs = x[df_bitcoin.get_column('PriceHigh').to_numpy()]
lows = x[df_bitcoin.get_column('PriceLow').to_numpy()]
axes_per_metric = 2
fig, axes = plt.subplots(
len(metrics), axes_per_metric, figsize=(4 * axes_per_metric, 3 * len(metrics))
)
axes = axes.reshape(-1, axes_per_metric)
plt.tight_layout(pad=10)
plt.subplots_adjust(top=0.98)
plt.suptitle(
'CBBI metric data input → output', fontsize=11.25, weight='bold', y=0.99508
)
for metric, ax_row in zip(metrics, axes, strict=True):
ax_out = ax_row[1]
ax_in = ax_row[0]
sns.lineplot(x=x, y=price, alpha=0.4, color='orange', ax=ax_out)
values = await metric.calculate(df_bitcoin, [ax_out, ax_in])
_add_common_markers(ax_out, halvings=halvings, highs=highs, lows=lows)
_add_common_markers(ax_in, halvings=halvings, highs=highs, lows=lows)
_shade_metric_bounds(ax_out)
ax_in.annotate(
'',
xy=(1.0967, 0.75),
xycoords='axes fraction',
xytext=(1.0367, 0.75),
textcoords='axes fraction',
arrowprops={
'arrowstyle': '->',
'color': 'darkgray',
'lw': 1.5,
'shrinkA': 0,
'shrinkB': 0,
'mutation_scale': 10,
},
ha='center',
va='center',
)
values = values.clip(0, 1).rename(metric.name)
df_bitcoin = df_bitcoin.with_columns(values)
metrics_cols.append(metric.name)
metrics_descriptions.append(metric.description)
df_result = df_bitcoin.select('Date', 'Price', *metrics_cols).with_columns(
Confidence=pl.mean_horizontal([pl.col(c).fill_nan(None) for c in metrics_cols])
)
print('Generating charts…')
plt.savefig(charts_file_path)
plt.close(fig)
_write_columns_orient_json(df_result, json_file_path, precision=4)
last = df_result.tail(1).row(0, named=True)
confidence_details = {
desc: last[name]
for name, desc in zip(metrics_cols, metrics_descriptions, strict=True)
}
print('\n' + ef.b + ':: Confidence we are at the peak ::' + rs.all)
print(
fg.cyan
+ ef.bold
+ figlet_format(format_percentage(last['Confidence'], ''), font='univers')
+ rs.all,
end='',
)
for description, value in confidence_details.items():
if value is not None and not (isinstance(value, float) and np.isnan(value)):
print(
fg.white + get_color(value) + f'{format_percentage(value)} ' + rs.all,
end='',
)
print(f' - {description}')
print()
print(
'Source code: ' + ef.u + fg.li_blue + 'https://github.com/Zaczero/CBBI' + rs.all
)
print('License: ' + ef.b + 'AGPL-3.0' + rs.all)
print()
def run_and_retry(
json_file: str = 'latest.json',
charts_file: str = 'charts.svg',
output_dir: str | None = 'output',
max_attempts: int = 3,
sleep_seconds_on_error: int = 60,
):
"""
Calculates the current CBBI confidence value alongside all the required metrics.
Everything gets pretty printed to the current standard output and a clean copy
is saved to a JSON file specified by the path in the ``json_file`` argument.
A charts image is generated on the path specified by the ``charts_file`` argument
which summarizes all individual metrics' historical data in a visual way.
The execution is attempted multiple times in case an error occurs.
Args:
json_file: File path where the output is saved in the JSON format.
charts_file: File path where the charts are saved (format inferred from file extension).
output_dir: Directory path where the output is stored.
If set to ``None`` then use the current working directory.
If the directory does not exist, it will be created.
max_attempts: Maximum number of attempts before termination. An attempt is counted when an error occurs.
sleep_seconds_on_error: Duration of the sleep in seconds before attempting again after an error occurs.
"""
assert max_attempts > 0, 'Value of the max_attempts argument must be positive'
assert sleep_seconds_on_error >= 0, (
'Value of the sleep_seconds_on_error argument must be non-negative'
)
for _ in range(max_attempts):
try:
asyncio.run(run(json_file, charts_file, output_dir))
exit(0)
except Exception:
print(fg.black + bg.yellow + ' An error has occurred! ' + rs.all)
traceback.print_exc()
print(f'\nRetrying in {sleep_seconds_on_error} seconds…', flush=True)
for _ in tqdm(range(sleep_seconds_on_error)):
time.sleep(1)
print(f'Max attempts limit has been reached ({max_attempts}).')
print('Better luck next time!')
exit(-1)
if __name__ == '__main__':
fire.Fire(run_and_retry)
================================================
FILE: metrics/_common.py
================================================
import numpy as np
import polars as pl
def join_left_on_date(df: pl.DataFrame, other: pl.DataFrame):
return df.join(other, on='Date', how='left', maintain_order='left')
def linreg_predict(
x_train: np.ndarray, y_train: np.ndarray, x_all: np.ndarray
) -> np.ndarray:
x_train = x_train.astype(np.float64)
y_train = y_train.astype(np.float64)
x_mean = x_train.mean()
y_mean = y_train.mean()
x_centered = x_train - x_mean
slope = (x_centered * (y_train - y_mean)).sum() / (x_centered * x_centered).sum()
intercept = y_mean - slope * x_mean
return intercept + slope * x_all.astype(np.float64)
================================================
FILE: metrics/base_metric.py
================================================
import traceback
from abc import ABC, abstractmethod
import polars as pl
from matplotlib.axes import Axes
from sty import bg, fg, rs
from api.cbbiinfo_api import cbbi_fetch
from utils import send_error_notification
class BaseMetric(ABC):
@property
@abstractmethod
def name(self) -> str:
pass
@property
@abstractmethod
def description(self) -> str:
pass
@abstractmethod
def _calculate(self, df: pl.DataFrame, ax: list[Axes]) -> pl.Series:
pass
def _fallback(self, df: pl.DataFrame):
return (
df
.join(cbbi_fetch(self.name), on='Date', how='left', maintain_order='left')
.with_columns(pl.col('Value').forward_fill())
.get_column('Value')
)
async def calculate(self, df: pl.DataFrame, ax: list[Axes]):
try:
return self._calculate(df, ax)
except Exception as ex:
traceback.print_exc()
await send_error_notification(ex)
print(
fg.black
+ bg.yellow
+ f' Requesting fallback values for {self.name} (from CBBI.info) '
+ rs.all
)
return self._fallback(df)
================================================
FILE: metrics/mvrv_z_score.py
================================================
import numpy as np
import polars as pl
import seaborn as sns
from matplotlib.axes import Axes
from api.coinsoto_api import cs_fetch
from metrics._common import join_left_on_date, linreg_predict
from metrics.base_metric import BaseMetric
class MVRVMetric(BaseMetric):
@property
def name(self):
return 'MVRV'
@property
def description(self):
return 'MVRV Z-Score'
def _calculate(self, df: pl.DataFrame, ax: list[Axes]):
bull_days_shift = 6
low_model_adjust = 0.26
df = join_left_on_date(
df,
cs_fetch(
path='chain/index/charts?type=/charts/mvrv-zscore/',
data_selector='value4',
col_name='MVRV',
),
)
df = df.with_columns(
MVRV=(
pl
.when(pl.col('DaysSinceHalving') < pl.col('DaysSincePriceLow'))
.then(pl.col('MVRV').shift(bull_days_shift))
.otherwise(pl.col('MVRV'))
)
)
df = df.with_columns(MVRV=(pl.col('MVRV').forward_fill() + 1).log())
row_nr = np.arange(df.height)
high_idx = row_nr[df.get_column('PriceHigh').to_numpy()]
low_idx = row_nr[df.get_column('PriceLow').to_numpy()]
mvrv = df.get_column('MVRV').to_numpy()
high_model = linreg_predict(high_idx, mvrv[high_idx], row_nr)
low_model = linreg_predict(low_idx, mvrv[low_idx], row_nr) + low_model_adjust
x = df.get_column('Date').to_numpy()
mvrv_index = (mvrv - low_model) / (high_model - low_model)
y_out = np.nan_to_num(mvrv_index, nan=0.0)
ax[0].set_title(self.description)
ax[0].set_xlabel('Date')
ax[0].set_ylabel('MVRVIndex')
sns.lineplot(x=x, y=y_out, ax=ax[0])
ax[1].set_xlabel('Date')
ax[1].set_ylabel('MVRV')
sns.lineplot(x=x, y=mvrv, ax=ax[1])
sns.lineplot(x=x, y=high_model, ax=ax[1])
sns.lineplot(x=x, y=low_model, ax=ax[1])
return pl.Series(mvrv_index)
================================================
FILE: metrics/pi_cycle.py
================================================
import numpy as np
import polars as pl
import seaborn as sns
from matplotlib.axes import Axes
from metrics._common import linreg_predict
from metrics.base_metric import BaseMetric
from utils import mark_highs_lows
class PiCycleMetric(BaseMetric):
@property
def name(self):
return 'PiCycle'
@property
def description(self):
return 'Pi Cycle Top Indicator'
def _calculate(self, df: pl.DataFrame, ax: list[Axes]):
dma_111 = pl.col('Price').rolling_mean(window_size=111, min_samples=111)
dma_350x2 = pl.col('Price').rolling_mean(window_size=350, min_samples=350) * 2
df = df.with_columns(
dma_111.alias('111DMA'),
dma_350x2.alias('350DMAx2'),
).with_columns(PiCycleDiff=pl.col('111DMA').log() - pl.col('350DMAx2').log())
diff = df.get_column('PiCycleDiff').to_numpy()
df = mark_highs_lows(df, 'PiCycleDiff', True, 365 * 2, 365)
crossed = diff > 0
crossed_idx = np.flatnonzero(crossed)
uncrossed_idx = np.flatnonzero(~crossed)
crossed_segments = (
np.split(crossed_idx, np.where(np.diff(crossed_idx) > 1)[0] + 1)
if crossed_idx.size
else []
)
uncrossed_segments = (
np.split(uncrossed_idx, np.where(np.diff(uncrossed_idx) > 1)[0] + 1)
if uncrossed_idx.size
else []
)
distance_floor = np.zeros(df.height, dtype=np.float64)
distance_from_zero = np.abs(diff)
for i, crossed_seg in enumerate(crossed_segments):
seg_distance = distance_from_zero[crossed_seg]
max_pos = int(np.nanargmax(seg_distance))
max_idx = int(crossed_seg[max_pos])
max_distance = float(seg_distance[max_pos])
distance_floor[max_idx + 1 :] = max_distance
uncrossed_seg = (
uncrossed_segments[i + 1] if i + 1 < len(uncrossed_segments) else None
)
if uncrossed_seg is not None and uncrossed_seg.size:
above = uncrossed_seg[distance_from_zero[uncrossed_seg] >= max_distance]
if above.size:
distance_floor[int(above.min()) :] = 0
crossed_next = (
crossed_segments[i + 1] if i + 1 < len(crossed_segments) else None
)
if crossed_next is not None and crossed_next.size:
distance_floor[int(crossed_next.min()) :] = 0
high_idx = np.flatnonzero(df.get_column('PiCycleDiffHigh').to_numpy())
row_nr = np.arange(df.height)
low_idx = np.flatnonzero(df.get_column('PiCycleDiffLow').to_numpy())
target = np.zeros(df.height, dtype=np.float64)
if high_idx.size >= 3:
target = np.minimum(
linreg_predict(high_idx, diff[high_idx], row_nr),
0.0,
)
finite_idx = np.flatnonzero(np.isfinite(diff))
cold_model = np.full(df.height, np.nan, dtype=np.float64)
cycle_starts = np.array([int(finite_idx[0]), *low_idx], dtype=np.int64)
for i, start in enumerate(cycle_starts):
end = (
int(cycle_starts[i + 1] - 1)
if i + 1 < cycle_starts.size
else df.height - 1
)
cold_model[start : end + 1] = diff[start]
distance = np.maximum(np.maximum(target - diff, 0.0), distance_floor)
index = 1 - distance / np.abs(target - cold_model)
x = df.get_column('Date').to_numpy()
y_out = np.nan_to_num(index, nan=0.0)
ax[0].set_title(self.description)
ax[0].set_xlabel('Date')
ax[0].set_ylabel('PiCycleIndex')
sns.lineplot(x=x, y=y_out, ax=ax[0])
ax[1].set_xlabel('Date')
ax[1].set_ylabel('PiCycleDiff')
sns.lineplot(x=x, y=diff, ax=ax[1])
sns.lineplot(x=x, y=target, ax=ax[1])
sns.lineplot(x=x, y=cold_model, ax=ax[1])
sns.lineplot(x=x, y=target - distance_floor, ax=ax[1], linestyle='--')
return pl.Series('PiCycleIndex', index)
================================================
FILE: metrics/puell_multiple.py
================================================
import numpy as np
import polars as pl
import seaborn as sns
from matplotlib.axes import Axes
from metrics._common import linreg_predict
from metrics.base_metric import BaseMetric
class PuellMetric(BaseMetric):
@property
def name(self):
return 'Puell'
@property
def description(self):
return 'Puell Multiple'
def _calculate(self, df: pl.DataFrame, ax: list[Axes]):
puell_log = (
df
.get_column('PuellMultiple')
.forward_fill()
.log()
.rolling_mean(window_size=3, min_samples=1)
.to_numpy()
)
row_nr = np.arange(df.height)
high_idx = row_nr[df.get_column('PriceHigh').to_numpy()]
high_model = linreg_predict(high_idx, puell_log[high_idx], row_nr)
low_model = -1.0
x = df.get_column('Date').to_numpy()
puell_index = (puell_log - low_model) / (high_model - low_model)
y_out = np.nan_to_num(puell_index, nan=0.0)
ax[0].set_title(self.description)
ax[0].set_xlabel('Date')
ax[0].set_ylabel('PuellIndex')
sns.lineplot(x=x, y=y_out, ax=ax[0])
ax[1].set_xlabel('Date')
ax[1].set_ylabel('PuellLog')
sns.lineplot(x=x, y=puell_log, ax=ax[1])
sns.lineplot(x=x, y=high_model, ax=ax[1])
sns.lineplot(x=x, y=np.full(df.height, low_model, dtype=np.float64), ax=ax[1])
return pl.Series(puell_index)
================================================
FILE: metrics/reserve_risk.py
================================================
import numpy as np
import polars as pl
import seaborn as sns
from matplotlib.axes import Axes
from api.coinsoto_api import cs_fetch
from metrics._common import join_left_on_date, linreg_predict
from metrics.base_metric import BaseMetric
class ReserveRiskMetric(BaseMetric):
@property
def name(self):
return 'ReserveRisk'
@property
def description(self):
return 'Reserve Risk'
def _calculate(self, df: pl.DataFrame, ax: list[Axes]):
days_shift = 1
df = join_left_on_date(
df,
cs_fetch(
path='chain/index/charts?type=/charts/reserve-risk/',
data_selector='value4',
col_name='Risk',
),
)
df = df.with_columns(Risk=pl.col('Risk').shift(days_shift).forward_fill())
row_nr = np.arange(df.height)
high_idx = row_nr[df.get_column('PriceHigh').to_numpy()]
low_idx = row_nr[df.get_column('PriceLow').to_numpy()][1:]
risk_log = np.log(df.get_column('Risk').to_numpy())
high_model = linreg_predict(high_idx, risk_log[high_idx], row_nr) - 0.15
low_model = linreg_predict(low_idx, risk_log[low_idx], row_nr)
x = df.get_column('Date').to_numpy()
risk_index = (risk_log - low_model) / (high_model - low_model)
y_out = np.nan_to_num(risk_index, nan=0.0)
ax[0].set_title(self.description)
ax[0].set_xlabel('Date')
ax[0].set_ylabel('RiskIndex')
sns.lineplot(x=x, y=y_out, ax=ax[0])
ax[1].set_xlabel('Date')
ax[1].set_ylabel('RiskLog')
sns.lineplot(x=x, y=risk_log, ax=ax[1])
sns.lineplot(x=x, y=high_model, ax=ax[1])
sns.lineplot(x=x, y=low_model, ax=ax[1])
return pl.Series(risk_index)
================================================
FILE: metrics/rhodl_ratio.py
================================================
import numpy as np
import polars as pl
import seaborn as sns
from matplotlib.axes import Axes
from api.coinsoto_api import cs_fetch
from metrics._common import join_left_on_date, linreg_predict
from metrics.base_metric import BaseMetric
class RHODLMetric(BaseMetric):
@property
def name(self):
return 'RHODL'
@property
def description(self):
return 'RHODL Ratio'
def _calculate(self, df: pl.DataFrame, ax: list[Axes]):
remote_df = cs_fetch(
path='chain/index/charts?type=/charts/rhodl-ratio/',
data_selector='value1',
col_name='RHODL',
)
df = join_left_on_date(df, remote_df).with_columns(
RHODL=pl.col('RHODL').forward_fill()
)
row_nr = np.arange(df.height)
high_mask = df.get_column('PriceHigh').to_numpy() | (
df.get_column('Date').to_numpy() == np.datetime64('2024-12-18')
)
high_idx = row_nr[high_mask]
low_idx = row_nr[df.get_column('PriceLow').to_numpy()][1:]
rhodl = df.get_column('RHODL').to_numpy()
rhodl_log = np.log(rhodl)
high_model = linreg_predict(high_idx, rhodl_log[high_idx], row_nr)
low_model = linreg_predict(low_idx, rhodl_log[low_idx], row_nr)
x = df.get_column('Date').to_numpy()
rhodl_index = (rhodl_log - low_model) / (high_model - low_model)
y_out = np.nan_to_num(rhodl_index, nan=0.0)
ax[0].set_title(self.description)
ax[0].set_xlabel('Date')
ax[0].set_ylabel('RHODLIndex')
sns.lineplot(x=x, y=y_out, ax=ax[0])
ax[1].set_xlabel('Date')
ax[1].set_ylabel('RHODLLog')
sns.lineplot(x=x, y=rhodl_log, ax=ax[1])
sns.lineplot(x=x, y=high_model, ax=ax[1])
sns.lineplot(x=x, y=low_model, ax=ax[1])
return pl.Series(rhodl_index)
================================================
FILE: metrics/rupl.py
================================================
import numpy as np
import polars as pl
import seaborn as sns
from matplotlib.axes import Axes
from api.coinsoto_api import cs_fetch
from metrics._common import join_left_on_date, linreg_predict
from metrics.base_metric import BaseMetric
class RUPLMetric(BaseMetric):
@property
def name(self):
return 'RUPL'
@property
def description(self):
return 'RUPL/NUPL Chart'
def _calculate(self, df: pl.DataFrame, ax: list[Axes]):
df = join_left_on_date(
df,
cs_fetch(
path='chain/index/charts?type=/charts/relative-unrealized-prof/',
data_selector='value1',
col_name='RUPL',
),
)
df = df.with_columns(RUPL=pl.col('RUPL').forward_fill())
row_nr = np.arange(df.height)
high_idx = row_nr[df.get_column('PriceHigh').to_numpy()]
low_idx = row_nr[df.get_column('PriceLow').to_numpy()][1:]
rupl = df.get_column('RUPL').to_numpy()
x_all = row_nr
high_model = linreg_predict(high_idx, rupl[high_idx], x_all)
low_model = linreg_predict(low_idx, rupl[low_idx], x_all)
x = df.get_column('Date').to_numpy()
rupl_index = (rupl - low_model) / (high_model - low_model)
y_out = np.nan_to_num(rupl_index, nan=0.0)
ax[0].set_title(self.description)
ax[0].set_xlabel('Date')
ax[0].set_ylabel('RUPLIndex')
sns.lineplot(x=x, y=y_out, ax=ax[0])
ax[1].set_xlabel('Date')
ax[1].set_ylabel('RUPL')
sns.lineplot(x=x, y=rupl, ax=ax[1])
sns.lineplot(x=x, y=high_model, ax=ax[1])
sns.lineplot(x=x, y=low_model, ax=ax[1])
return pl.Series(rupl_index)
================================================
FILE: metrics/trolololo.py
================================================
import numpy as np
import polars as pl
import seaborn as sns
from matplotlib.axes import Axes
from metrics._common import linreg_predict
from metrics.base_metric import BaseMetric
class TrolololoMetric(BaseMetric):
@property
def name(self):
return 'Trolololo'
@property
def description(self):
return 'Bitcoin Trolololo Trend Line'
def _calculate(self, df: pl.DataFrame, ax: list[Axes]):
x = df.get_column('Date').to_numpy()
price_log = df.get_column('PriceLog').to_numpy()
begin_date = np.datetime64('2012-01-01T00:00:00')
days_since_begin = (x - begin_date) / np.timedelta64(1, 'D')
trolo_top_log = np.log(10.0) * (
2.900 * np.log(days_since_begin + 1400) - 19.463
)
trolo_bottom_log = np.log(10.0) * (
2.788 * np.log(days_since_begin + 1200) - 19.463
)
overshoot_actual = price_log - trolo_top_log
undershoot_actual = price_log - trolo_bottom_log
row_nr = np.arange(df.height)
after_begin = days_since_begin >= 0
high_mask = df.get_column('PriceHigh').to_numpy() & after_begin
low_mask = df.get_column('PriceLow').to_numpy() & after_begin
high_idx = row_nr[high_mask]
low_idx = row_nr[low_mask]
high_y = overshoot_actual[high_idx].copy()
high_y[0] *= 0.6 # the first value seems too high
overshoot_model = linreg_predict(high_idx, high_y, row_nr)
undershoot_model = linreg_predict(low_idx, undershoot_actual[low_idx], row_nr)
high_model = trolo_top_log + overshoot_model
low_model = trolo_bottom_log + undershoot_model
trolo_index = (price_log - low_model) / (high_model - low_model)
y_out = np.nan_to_num(trolo_index, nan=0.0)
ax[0].set_title(self.description)
ax[0].set_xlabel('Date')
ax[0].set_ylabel('TroloIndex')
sns.lineplot(x=x, y=y_out, ax=ax[0])
ax[1].set_xlabel('Date')
ax[1].set_ylabel('PriceLog')
sns.lineplot(x=x, y=price_log, ax=ax[1])
sns.lineplot(x=x, y=high_model, ax=ax[1])
sns.lineplot(x=x, y=low_model, ax=ax[1])
return pl.Series(trolo_index)
================================================
FILE: metrics/two_year_moving_average.py
================================================
import numpy as np
import polars as pl
import seaborn as sns
from matplotlib.axes import Axes
from metrics._common import linreg_predict
from metrics.base_metric import BaseMetric
class TwoYearMovingAverageMetric(BaseMetric):
@property
def name(self):
return '2YMA'
@property
def description(self):
return '2 Year Moving Average'
def _calculate(self, df: pl.DataFrame, ax: list[Axes]):
row_nr = np.arange(df.height)
high_idx = row_nr[df.get_column('PriceHigh').to_numpy()]
low_idx = row_nr[df.get_column('PriceLow').to_numpy()]
price_log = df.get_column('PriceLog').to_numpy()
two_yma = df.get_column('Price730DMA').to_numpy()
two_yma_log = np.log(two_yma)
log_diff = price_log - two_yma_log
overshoot_model = linreg_predict(high_idx, log_diff[high_idx], row_nr)
undershoot_model = linreg_predict(low_idx, log_diff[low_idx], row_nr)
x = df.get_column('Date').to_numpy()
two_yma_high_model = overshoot_model + two_yma_log
two_yma_low_model = undershoot_model + two_yma_log
two_yma_index = (price_log - two_yma_low_model) / (
two_yma_high_model - two_yma_low_model
)
y_out = np.nan_to_num(two_yma_index, nan=0.0)
ax[0].set_title(self.description)
ax[0].set_xlabel('Date')
ax[0].set_ylabel('2YMAIndex')
sns.lineplot(x=x, y=y_out, ax=ax[0])
ax[1].set_xlabel('Date')
ax[1].set_ylabel('PriceLog')
sns.lineplot(x=x, y=price_log, ax=ax[1])
sns.lineplot(x=x, y=two_yma_high_model, ax=ax[1])
sns.lineplot(x=x, y=two_yma_low_model, ax=ax[1])
return pl.Series(two_yma_index)
================================================
FILE: metrics/woobull_topcap_cvdd.py
================================================
import numpy as np
import polars as pl
import seaborn as sns
from matplotlib.axes import Axes
from metrics._common import join_left_on_date, linreg_predict
from metrics.base_metric import BaseMetric
from utils import HTTP
def _woocharts_xy_ms_df(*, x: list[object], y: list[object], y_name: str):
return (
pl
.DataFrame({
'Date': pl.Series(x, dtype=pl.Int64),
y_name: pl.Series(y, dtype=pl.Float64),
})
.with_columns(
Date=(
pl
.from_epoch(pl.col('Date'), time_unit='ms')
.dt.cast_time_unit('us')
.dt.replace_time_zone('UTC')
)
)
.select('Date', y_name)
)
def _fetch_df():
response = HTTP.get('https://woocharts.com/bitcoin-price-models/data/chart.json')
response.raise_for_status()
data = response.json()
df_top = _woocharts_xy_ms_df(
x=data['top_']['x'],
y=data['top_']['y'],
y_name='Top',
)
df_cvdd = _woocharts_xy_ms_df(
x=data['cvdd']['x'],
y=data['cvdd']['y'],
y_name='CVDD',
)
return df_top.join(df_cvdd, on='Date', how='inner', maintain_order='left')
class WoobullMetric(BaseMetric):
@property
def name(self):
return 'Woobull'
@property
def description(self):
return 'Woobull Top Cap vs CVDD'
def _calculate(self, df: pl.DataFrame, ax: list[Axes]):
df = join_left_on_date(df, _fetch_df())
row_nr = np.arange(df.height)
high_idx = row_nr[df.get_column('PriceHigh').to_numpy()]
low_idx = row_nr[df.get_column('PriceLow').to_numpy()][1:]
top_log = np.log(df.get_column('Top').to_numpy())
cvdd_log = np.log(df.get_column('CVDD').to_numpy())
price_log = df.get_column('PriceLog').to_numpy()
woobull = (price_log - cvdd_log) / (top_log - cvdd_log)
high_model = linreg_predict(high_idx, woobull[high_idx], row_nr) - 0.025
low_model = linreg_predict(low_idx, woobull[low_idx], row_nr)
x = df.get_column('Date').to_numpy()
woobull_index = (woobull - low_model) / (high_model - low_model)
y_out = np.nan_to_num(woobull_index, nan=0.0)
ax[0].set_title(self.description)
ax[0].set_xlabel('Date')
ax[0].set_ylabel('WoobullIndex')
sns.lineplot(x=x, y=y_out, ax=ax[0])
ax[1].set_xlabel('Date')
ax[1].set_ylabel('Woobull')
sns.lineplot(x=x, y=woobull, ax=ax[1])
sns.lineplot(x=x, y=high_model, ax=ax[1])
sns.lineplot(x=x, y=low_model, ax=ax[1])
return pl.Series(woobull_index)
================================================
FILE: pyproject.toml
================================================
[project]
name = "cbbi"
version = "0.0.0"
requires-python = "~=3.14.0"
dependencies = [
"fire",
"httpx[brotli,zstd]",
"matplotlib",
"numpy",
"polars",
"pyfiglet",
"python-telegram-bot",
"seaborn",
"sty",
"tqdm",
]
[tool.ruff]
indent-width = 4
line-length = 88
target-version = "py314"
[tool.ruff.lint]
ignore = [
"S101", # assert
]
# see https://docs.astral.sh/ruff/rules/ for rules documentation
select = [
"A", # flake8-builtins
"ARG", # flake8-unused-argumentsf
"ASYNC", # flake8-async
"B", # flake8-bugbear
# "COM", # flake8-commas
"C4", # flake8-comprehensions
"E4", # pycodestyle
"E7",
"E9",
"F", # pyflakes
# "FBT", # flake8-boolean-trap
"FLY", # flynt
# "FURB", # refurb (preview)
"G", # flake8-logging-format
"I", # isort
"INT", # flake8-gettext
# "LOG", # flake8-logging (preview)
"N", # pep8-naming
"NPY", # numpy
"PERF", # perflint
"PGH", # pygrep-hooks
"PIE", # flake8-pie
"Q", # flake8-quotes
"UP", # pyupgrade
# "PL", # pylint
"PT", # flake8-pytest-style
"PTH", # flake8-use-pathlib
"PYI", # flake8-pyi
"RSE", # flake8-raise
"RUF", # ruff
"S", # flake8-bandit
"SIM", # flake8-simplify
"SLF", # flake8-self
"SLOT", # flake8-slots
"T10", # flake8-debugger
# "T20", # flake8-print
# "TRY", # tryceratops
"YTT", # flake8-2020
]
fixable = ["ALL"]
unfixable = []
[tool.ruff.lint.flake8-builtins]
builtins-ignorelist = ["id", "open", "type"]
[tool.ruff.lint.flake8-quotes]
docstring-quotes = "double"
inline-quotes = "single"
multiline-quotes = "double"
[tool.ruff.lint.pylint]
max-args = 10
[tool.ruff.format]
indent-style = "space"
line-ending = "lf"
quote-style = "single"
skip-magic-trailing-comma = false
preview = true
[tool.setuptools]
packages = ["."]
[tool.uv]
package = false
python-downloads = "never"
python-preference = "only-system"
================================================
FILE: shell.nix
================================================
{ }:
let
# Update with `nixpkgs-update` command
pkgs =
import
(fetchTarball "https://github.com/NixOS/nixpkgs/archive/68a8af93ff4297686cb68880845e61e5e2e41d92.tar.gz")
{ };
pythonLibs = with pkgs; [
stdenv.cc.cc.lib
zlib.out
];
python' =
with pkgs;
(symlinkJoin {
name = "python";
paths = [ python314 ];
buildInputs = [ makeWrapper ];
postBuild = ''
wrapProgram "$out/bin/python3.14" --prefix LD_LIBRARY_PATH : "${lib.makeLibraryPath pythonLibs}"
'';
});
packages' = with pkgs; [
python'
coreutils
curl
gnused
jq
ruff
uv
(writeShellScriptBin "nixpkgs-update" ''
set -e
hash=$(
curl -fsSL \
https://prometheus.nixos.org/api/v1/query \
-d 'query=channel_revision{channel="nixpkgs-unstable"}' |
jq -r ".data.result[0].metric.revision"
)
sed -i "s|nixpkgs/archive/[0-9a-f]\\{40\\}|nixpkgs/archive/$hash|" shell.nix
echo "Nixpkgs updated to $hash"
'')
(writeShellScriptBin "docker-build-push" ''
set -e
if command -v podman &> /dev/null; then docker() { podman "$@"; } fi
docker push $(docker load < $(nix-build --no-out-link) | sed -En 's/Loaded image: (\S+)/\1/p')
'')
];
shell' = ''
export SSL_CERT_FILE=${pkgs.cacert}/etc/ssl/certs/ca-bundle.crt
export PYTHONNOUSERSITE=1
export PYTHONPATH=""
export TZ=UTC
if [ -f .env ]; then
echo "Loading .env file"
set -a; . .env; set +a
else
echo "Skipped loading .env file (not found)"
fi
current_python=$(readlink -e .venv/bin/python || echo "")
current_python=''${current_python%/bin/*}
[ "$current_python" != "${python'}" ] && rm -rf .venv/
echo "Installing Python dependencies"
export UV_PYTHON="${python'}/bin/python"
uv sync --frozen
source .venv/bin/activate
export UV_PYTHON="$VIRTUAL_ENV/bin/python"
'';
in
pkgs.mkShell {
buildInputs = packages';
shellHook = shell';
}
================================================
FILE: utils.py
================================================
import os
import traceback
from math import ceil
import numpy as np
import polars as pl
import telegram
from httpx import Client
from sty import bg
HTTP = Client(
headers={
'User-Agent': 'Mozilla/5.0 (Linux x86_64; rv:140.0) Gecko/20100101 Firefox/140.0'
},
timeout=60,
follow_redirects=True,
)
def mark_highs_lows(
df: pl.DataFrame,
col: str,
begin_with_high: bool,
window_size: int,
ignore_last_rows: int,
):
"""
Marks highs and lows (peaks) of the column values inside the given DataFrame.
Marked points are indicated by `True` inside their corresponding, newly added, `col + "High"` and `col + "Low"` columns.
Args:
df: DataFrame from which the column values are selected and to which marked points columns are added.
col: Column name of which values are selected inside the given DataFrame.
begin_with_high: Indicates whether the first peak is high or low.
window_size: Window size for the algorithm to consider.
Too low value will mark too many peaks, whereas, too high value will mark too little peaks.
ignore_last_rows: Amount of trailing DataFrame rows for which highs and lows should not be marked.
Returns:
Modified input DataFrame with columns, indicating the marked points, added.
"""
col_high = col + 'High'
col_low = col + 'Low'
values = df.get_column(col).to_numpy()
high_marks = np.zeros(len(values), dtype=np.bool_)
low_marks = np.zeros(len(values), dtype=np.bool_)
searching_high = begin_with_high
current_index = 0
while True:
window_end = min(current_index + window_size, len(values) - 1)
window = values[current_index : window_end + 1]
if sum(~np.isnan(window)) == 0 and window.shape[0] > 1:
current_index += window.shape[0]
continue
if window.shape[0] <= 1:
break
window_index = current_index + (
np.nanargmax(window) if searching_high else np.nanargmin(window)
)
if window_index == current_index:
if searching_high:
high_marks[window_index] = True
else:
low_marks[window_index] = True
searching_high = not searching_high
window_index = window_index + 1
current_index = window_index
if ignore_last_rows > 0:
high_marks[-ignore_last_rows:] = False
low_marks[-ignore_last_rows:] = False
# stabilize the algorithm until a next major update
stabilize_mask = df.get_column('Date').to_numpy() >= np.datetime64('2025-10-07')
high_marks[stabilize_mask] = False
low_marks[stabilize_mask] = False
return df.with_columns(
pl.Series(col_high, high_marks, dtype=pl.Boolean),
pl.Series(col_low, low_marks, dtype=pl.Boolean),
)
def mark_days_since(df: pl.DataFrame, cols: list[str]):
"""
This function takes a DataFrame and a list of column names
and calculates the number of days since the last `True` value for each column in the list.
The resulting DataFrame will have new columns for each input column, with the column name prefixed by 'DaysSince'.
The value in these new columns will be the number of days since the last `True` value in the corresponding input column.
Args:
df: The input DataFrame.
cols: The list of boolean marker columns to calculate the days since the last `True` value.
Returns:
The modified DataFrame with the new columns added.
"""
df = df.with_row_index(name='_row_nr')
exprs: dict[str, pl.Expr] = {}
for col in cols:
last_event_idx = (
pl.when(pl.col(col)).then(pl.col('_row_nr')).otherwise(None).forward_fill()
)
exprs[f'DaysSince{col}'] = pl.col('_row_nr') - last_event_idx
return df.with_columns(**exprs).drop('_row_nr')
def format_percentage(val: float, suffix: str = ' %'):
"""
Formats a percentage value (0.0 - 1.0) in the standardized way.
Returned value has a constant width and a trailing '%' sign.
Args:
val: Percentage value to be formatted.
suffix: String to be appended to the result.
Returns:
Formatted percentage value with a constant width and trailing '%' sign.
Examples:
>>> print(format_percentage(0.359))
str(' 36 %')
>>> print(format_percentage(1.1))
str('110 %')
"""
return f'{ceil(val * 100): >3d}{suffix}'
def get_color(val: float):
"""
Maps a percentage value (0.0 - 1.0) to its corresponding color.
The color is used to indicate whether the value is low (0.0) or high (1.0).
Returned value is a valid sty-package color string.
Args:
val: Percentage value to be mapped into a color.
Returns:
Valid sty-package color string.
"""
config = [
bg.da_red,
0.3,
bg.da_yellow,
0.65,
bg.da_green,
0.85,
bg.da_cyan,
0.97,
bg.da_magenta,
]
bin_index = np.digitize([round(val, 2)], config[1::2])[0]
return config[::2][bin_index]
async def send_error_notification(exception: Exception):
"""
This function sends a notification to a Telegram chat with details of the provided exception.
Args:
exception: The exception to be reported.
Returns:
A boolean indicating whether the notification was sent successfully.
"""
telegram_token = os.getenv('TELEGRAM_TOKEN')
telegram_chat_id = os.getenv('TELEGRAM_CHAT_ID')
if not telegram_token or not telegram_chat_id:
return False
async with telegram.Bot(telegram_token) as bot:
await bot.send_message(
telegram_chat_id,
f'🚨 An error has occurred: <b>{exception!s}</b>\n'
f'\n'
f'🔍️ <b>Stack trace</b>\n'
f'<pre>{"".join(traceback.format_exception(exception))}</pre>',
parse_mode='HTML',
)
return True
gitextract_j3fsxwr5/ ├── .envrc ├── .gitattributes ├── .gitignore ├── .vscode/ │ └── launch.json ├── LICENSE ├── README.md ├── api/ │ ├── cbbiinfo_api.py │ ├── coinmetrics_api.py │ └── coinsoto_api.py ├── asciinema/ │ └── thumbnail.xcf ├── fetch_bitcoin_data.py ├── main.py ├── metrics/ │ ├── _common.py │ ├── base_metric.py │ ├── mvrv_z_score.py │ ├── pi_cycle.py │ ├── puell_multiple.py │ ├── reserve_risk.py │ ├── rhodl_ratio.py │ ├── rupl.py │ ├── trolololo.py │ ├── two_year_moving_average.py │ └── woobull_topcap_cvdd.py ├── pyproject.toml ├── shell.nix └── utils.py
SYMBOL INDEX (63 symbols across 17 files)
FILE: api/cbbiinfo_api.py
function cbbi_fetch (line 6) | def cbbi_fetch(key: str):
FILE: api/coinmetrics_api.py
function cm_fetch_asset_metrics (line 6) | def cm_fetch_asset_metrics(
FILE: api/coinsoto_api.py
function cs_fetch (line 6) | def cs_fetch(path: str, data_selector: str, col_name: str):
FILE: fetch_bitcoin_data.py
function fetch_coinmetrics_data (line 7) | def fetch_coinmetrics_data():
function fetch_bitcoin_data (line 51) | def fetch_bitcoin_data():
FILE: main.py
function get_metrics (line 29) | def get_metrics():
function _json_number (line 46) | def _json_number(value: float | None, *, precision: int = 4):
function _write_columns_orient_json (line 56) | def _write_columns_orient_json(df: pl.DataFrame, path: Path, *, precisio...
function _add_common_markers (line 73) | def _add_common_markers(
function _shade_metric_bounds (line 84) | def _shade_metric_bounds(ax):
function run (line 111) | async def run(json_file: str, charts_file: str, output_dir: str | None):
function run_and_retry (line 247) | def run_and_retry(
FILE: metrics/_common.py
function join_left_on_date (line 5) | def join_left_on_date(df: pl.DataFrame, other: pl.DataFrame):
function linreg_predict (line 9) | def linreg_predict(
FILE: metrics/base_metric.py
class BaseMetric (line 12) | class BaseMetric(ABC):
method name (line 15) | def name(self) -> str:
method description (line 20) | def description(self) -> str:
method _calculate (line 24) | def _calculate(self, df: pl.DataFrame, ax: list[Axes]) -> pl.Series:
method _fallback (line 27) | def _fallback(self, df: pl.DataFrame):
method calculate (line 35) | async def calculate(self, df: pl.DataFrame, ax: list[Axes]):
FILE: metrics/mvrv_z_score.py
class MVRVMetric (line 11) | class MVRVMetric(BaseMetric):
method name (line 13) | def name(self):
method description (line 17) | def description(self):
method _calculate (line 20) | def _calculate(self, df: pl.DataFrame, ax: list[Axes]):
FILE: metrics/pi_cycle.py
class PiCycleMetric (line 11) | class PiCycleMetric(BaseMetric):
method name (line 13) | def name(self):
method description (line 17) | def description(self):
method _calculate (line 20) | def _calculate(self, df: pl.DataFrame, ax: list[Axes]):
FILE: metrics/puell_multiple.py
class PuellMetric (line 10) | class PuellMetric(BaseMetric):
method name (line 12) | def name(self):
method description (line 16) | def description(self):
method _calculate (line 19) | def _calculate(self, df: pl.DataFrame, ax: list[Axes]):
FILE: metrics/reserve_risk.py
class ReserveRiskMetric (line 11) | class ReserveRiskMetric(BaseMetric):
method name (line 13) | def name(self):
method description (line 17) | def description(self):
method _calculate (line 20) | def _calculate(self, df: pl.DataFrame, ax: list[Axes]):
FILE: metrics/rhodl_ratio.py
class RHODLMetric (line 11) | class RHODLMetric(BaseMetric):
method name (line 13) | def name(self):
method description (line 17) | def description(self):
method _calculate (line 20) | def _calculate(self, df: pl.DataFrame, ax: list[Axes]):
FILE: metrics/rupl.py
class RUPLMetric (line 11) | class RUPLMetric(BaseMetric):
method name (line 13) | def name(self):
method description (line 17) | def description(self):
method _calculate (line 20) | def _calculate(self, df: pl.DataFrame, ax: list[Axes]):
FILE: metrics/trolololo.py
class TrolololoMetric (line 10) | class TrolololoMetric(BaseMetric):
method name (line 12) | def name(self):
method description (line 16) | def description(self):
method _calculate (line 19) | def _calculate(self, df: pl.DataFrame, ax: list[Axes]):
FILE: metrics/two_year_moving_average.py
class TwoYearMovingAverageMetric (line 10) | class TwoYearMovingAverageMetric(BaseMetric):
method name (line 12) | def name(self):
method description (line 16) | def description(self):
method _calculate (line 19) | def _calculate(self, df: pl.DataFrame, ax: list[Axes]):
FILE: metrics/woobull_topcap_cvdd.py
function _woocharts_xy_ms_df (line 11) | def _woocharts_xy_ms_df(*, x: list[object], y: list[object], y_name: str):
function _fetch_df (line 30) | def _fetch_df():
class WoobullMetric (line 49) | class WoobullMetric(BaseMetric):
method name (line 51) | def name(self):
method description (line 55) | def description(self):
method _calculate (line 58) | def _calculate(self, df: pl.DataFrame, ax: list[Axes]):
FILE: utils.py
function mark_highs_lows (line 20) | def mark_highs_lows(
function mark_days_since (line 92) | def mark_days_since(df: pl.DataFrame, cols: list[str]):
function format_percentage (line 119) | def format_percentage(val: float, suffix: str = ' %'):
function get_color (line 142) | def get_color(val: float):
function send_error_notification (line 171) | async def send_error_notification(exception: Exception):
Condensed preview — 26 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (96K chars).
[
{
"path": ".envrc",
"chars": 37,
"preview": "# shellcheck disable=SC2148\n\nuse nix\n"
},
{
"path": ".gitattributes",
"chars": 66,
"preview": "# Auto detect text files and perform LF normalization\n* text=auto\n"
},
{
"path": ".gitignore",
"chars": 6564,
"preview": "# Created by https://www.toptal.com/developers/gitignore/api/dotenv,python,pycharm,visualstudiocode,direnv\n# Edit at htt"
},
{
"path": ".vscode/launch.json",
"chars": 526,
"preview": "{\n \"configurations\": [\n {\n \"console\": \"integratedTerminal\",\n \"name\": \"Python Debugger: C"
},
{
"path": "LICENSE",
"chars": 34523,
"preview": " GNU AFFERO GENERAL PUBLIC LICENSE\n Version 3, 19 November 2007\n\n Copyright (C)"
},
{
"path": "README.md",
"chars": 3767,
"preview": "# ColinTalksCrypto Bitcoin Bull Run Index (CBBI)\n\n\n"
},
{
"path": "api/cbbiinfo_api.py",
"chars": 710,
"preview": "import polars as pl\n\nfrom utils import HTTP\n\n\ndef cbbi_fetch(key: str):\n response = HTTP.get('https://colintalkscrypt"
},
{
"path": "api/coinmetrics_api.py",
"chars": 1479,
"preview": "import polars as pl\n\nfrom utils import HTTP\n\n\ndef cm_fetch_asset_metrics(\n *,\n asset: str,\n metrics: list[str],"
},
{
"path": "api/coinsoto_api.py",
"chars": 789,
"preview": "import polars as pl\n\nfrom utils import HTTP\n\n\ndef cs_fetch(path: str, data_selector: str, col_name: str):\n response ="
},
{
"path": "fetch_bitcoin_data.py",
"chars": 2360,
"preview": "import polars as pl\n\nfrom api.coinmetrics_api import cm_fetch_asset_metrics\nfrom utils import mark_days_since, mark_high"
},
{
"path": "main.py",
"chars": 9472,
"preview": "import asyncio\nimport math\nimport time\nimport traceback\nfrom pathlib import Path\n\nimport fire\nimport numpy as np\nimport "
},
{
"path": "metrics/_common.py",
"chars": 635,
"preview": "import numpy as np\nimport polars as pl\n\n\ndef join_left_on_date(df: pl.DataFrame, other: pl.DataFrame):\n return df.joi"
},
{
"path": "metrics/base_metric.py",
"chars": 1236,
"preview": "import traceback\nfrom abc import ABC, abstractmethod\n\nimport polars as pl\nfrom matplotlib.axes import Axes\nfrom sty impo"
},
{
"path": "metrics/mvrv_z_score.py",
"chars": 2054,
"preview": "import numpy as np\nimport polars as pl\nimport seaborn as sns\nfrom matplotlib.axes import Axes\n\nfrom api.coinsoto_api imp"
},
{
"path": "metrics/pi_cycle.py",
"chars": 4089,
"preview": "import numpy as np\nimport polars as pl\nimport seaborn as sns\nfrom matplotlib.axes import Axes\n\nfrom metrics._common impo"
},
{
"path": "metrics/puell_multiple.py",
"chars": 1453,
"preview": "import numpy as np\nimport polars as pl\nimport seaborn as sns\nfrom matplotlib.axes import Axes\n\nfrom metrics._common impo"
},
{
"path": "metrics/reserve_risk.py",
"chars": 1784,
"preview": "import numpy as np\nimport polars as pl\nimport seaborn as sns\nfrom matplotlib.axes import Axes\n\nfrom api.coinsoto_api imp"
},
{
"path": "metrics/rhodl_ratio.py",
"chars": 1864,
"preview": "import numpy as np\nimport polars as pl\nimport seaborn as sns\nfrom matplotlib.axes import Axes\n\nfrom api.coinsoto_api imp"
},
{
"path": "metrics/rupl.py",
"chars": 1728,
"preview": "import numpy as np\nimport polars as pl\nimport seaborn as sns\nfrom matplotlib.axes import Axes\n\nfrom api.coinsoto_api imp"
},
{
"path": "metrics/trolololo.py",
"chars": 2215,
"preview": "import numpy as np\nimport polars as pl\nimport seaborn as sns\nfrom matplotlib.axes import Axes\n\nfrom metrics._common impo"
},
{
"path": "metrics/two_year_moving_average.py",
"chars": 1722,
"preview": "import numpy as np\nimport polars as pl\nimport seaborn as sns\nfrom matplotlib.axes import Axes\n\nfrom metrics._common impo"
},
{
"path": "metrics/woobull_topcap_cvdd.py",
"chars": 2655,
"preview": "import numpy as np\nimport polars as pl\nimport seaborn as sns\nfrom matplotlib.axes import Axes\n\nfrom metrics._common impo"
},
{
"path": "pyproject.toml",
"chars": 1891,
"preview": "[project]\nname = \"cbbi\"\nversion = \"0.0.0\"\nrequires-python = \"~=3.14.0\"\ndependencies = [\n \"fire\",\n \"httpx[brotli,zstd]\""
},
{
"path": "shell.nix",
"chars": 2029,
"preview": "{ }:\n\nlet\n # Update with `nixpkgs-update` command\n pkgs =\n import\n (fetchTarball \"https://github.com/NixOS/nix"
},
{
"path": "utils.py",
"chars": 6033,
"preview": "import os\nimport traceback\nfrom math import ceil\n\nimport numpy as np\nimport polars as pl\nimport telegram\nfrom httpx impo"
}
]
// ... and 1 more files (download for full content)
About this extraction
This page contains the full source code of the Zaczero/CBBI GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 26 files (89.5 KB), approximately 23.0k tokens, and a symbol index with 63 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.