105 |
106 | ### License
107 |
108 | This project is licensed under the GNU Affero General Public License v3.0.
109 |
110 | The complete license text can be accessed in the repository at [LICENSE](https://github.com/Zaczero/CBBI/blob/main/LICENSE).
111 |
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/api/cbbiinfo_api.py:
--------------------------------------------------------------------------------
1 | import pandas as pd
2 |
3 | from utils import HTTP
4 |
5 |
6 | def cbbi_fetch(key: str) -> pd.DataFrame:
7 | response = HTTP.get('https://colintalkscrypto.com/cbbi/data/latest.json')
8 | response.raise_for_status()
9 | response_data = response.json()[key]
10 |
11 | df = pd.DataFrame(
12 | response_data.items(),
13 | columns=[
14 | 'Date',
15 | 'Value',
16 | ],
17 | )
18 | df['Date'] = pd.to_datetime(df['Date'], unit='s').dt.tz_localize(None)
19 |
20 | return df
21 |
--------------------------------------------------------------------------------
/api/coinsoto_api.py:
--------------------------------------------------------------------------------
1 | import pandas as pd
2 |
3 | from utils import HTTP
4 |
5 |
6 | def cs_fetch(path: str, data_selector: str, col_name: str) -> pd.DataFrame:
7 | response = HTTP.get(f'https://coinank.com/indicatorapi/{path}')
8 | response.raise_for_status()
9 | data = response.json()['data']
10 |
11 | if 'timeList' not in data and 'line' in data:
12 | data = data['line']
13 |
14 | data_x = data['timeList']
15 | data_y = data[data_selector]
16 | assert len(data_x) == len(data_y), f'{len(data_x)=} != {len(data_y)=}'
17 |
18 | df = pd.DataFrame(
19 | {
20 | 'Date': data_x[: len(data_y)],
21 | col_name: data_y,
22 | }
23 | )
24 |
25 | df['Date'] = pd.to_datetime(df['Date'], unit='ms').dt.tz_localize(None)
26 |
27 | return df
28 |
--------------------------------------------------------------------------------
/api/glassnode_api.py:
--------------------------------------------------------------------------------
1 | import os
2 |
3 | import pandas as pd
4 |
5 | from utils import HTTP
6 |
7 |
8 | def gn_fetch(url_selector: str, col_name: str, **kwargs) -> pd.DataFrame:
9 | api_key = os.getenv('GLASSNODE_API_KEY')
10 |
11 | if not api_key:
12 | raise Exception('GlassNode fallback in unavailable (missing api key)')
13 |
14 | response = HTTP.get(
15 | f'https://api.glassnode.com/v1/metrics/indicators/{url_selector}',
16 | params=kwargs,
17 | headers={'X-Api-Key': api_key},
18 | )
19 | response.raise_for_status()
20 | response_json = response.json()
21 | response_x = [d['t'] for d in response_json]
22 | response_y = [d['v'] for d in response_json]
23 |
24 | df = pd.DataFrame(
25 | {
26 | 'Date': response_x,
27 | col_name: response_y,
28 | }
29 | )
30 | df['Date'] = pd.to_datetime(df['Date'], unit='s').dt.tz_localize(None)
31 |
32 | return df
33 |
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/asciinema/thumbnail.webp:
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https://raw.githubusercontent.com/Zaczero/CBBI/4b275fa095b550bf5f7e9de15e6d8f0ab6fa14be/asciinema/thumbnail.webp
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/asciinema/thumbnail.xcf:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/Zaczero/CBBI/4b275fa095b550bf5f7e9de15e6d8f0ab6fa14be/asciinema/thumbnail.xcf
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/fetch_bitcoin_data.py:
--------------------------------------------------------------------------------
1 | import numpy as np
2 | import pandas as pd
3 | from filecache import filecache
4 |
5 | from utils import HTTP, mark_days_since, mark_highs_lows
6 |
7 |
8 | @filecache(7200) # 2 hours
9 | def fetch_bitcoin_data() -> pd.DataFrame:
10 | """
11 | Fetches historical Bitcoin data into a DataFrame.
12 | Very early data is discarded due to high volatility.
13 |
14 | Returns:
15 | DataFrame containing Bitcoin data.
16 | """
17 | print('📈 Requesting historical Bitcoin data…')
18 |
19 | response = HTTP.get(
20 | 'https://api.blockchair.com/bitcoin/blocks',
21 | params={
22 | 'a': 'date,count(),min(id),max(id),sum(generation),sum(generation_usd)',
23 | 's': 'date(desc)',
24 | },
25 | )
26 | response.raise_for_status()
27 | response_json = response.json()
28 |
29 | df = pd.DataFrame(response_json['data'][::-1])
30 | df.rename(
31 | columns={
32 | 'date': 'Date',
33 | 'count()': 'TotalBlocks',
34 | 'min(id)': 'MinBlockID',
35 | 'max(id)': 'MaxBlockID',
36 | 'sum(generation)': 'TotalGeneration',
37 | 'sum(generation_usd)': 'TotalGenerationUSD',
38 | },
39 | inplace=True,
40 | )
41 |
42 | df['Date'] = pd.to_datetime(df['Date'])
43 | df['TotalGeneration'] /= 1e8
44 | df['BlockGeneration'] = df['TotalGeneration'] / df['TotalBlocks']
45 | df['BlockGenerationUSD'] = df['TotalGenerationUSD'] / df['TotalBlocks']
46 |
47 | df = df.merge(fetch_price_data(), on='Date', how='left')
48 | df.loc[df['Price'].isna(), 'Price'] = df['BlockGenerationUSD'] / df['BlockGeneration']
49 | df['PriceLog'] = np.log(df['Price'])
50 | df['PriceLogInterp'] = np.interp(
51 | x=df['PriceLog'],
52 | xp=(df['PriceLog'].min(), df['PriceLog'].max()),
53 | fp=(0, 1),
54 | )
55 |
56 | df = df.loc[df['Date'] >= '2011-06-27']
57 | df.reset_index(drop=True, inplace=True)
58 |
59 | df = fix_current_day_data(df)
60 | df = add_block_halving_data(df)
61 | df = mark_highs_lows(df, 'Price', False, round(365 * 2), 180)
62 |
63 | # move 2021' peak to the first price peak
64 | df.loc[df['Date'] == '2021-11-09', 'PriceHigh'] = 0
65 | df.loc[df['Date'] == '2021-04-14', 'PriceHigh'] = 1
66 |
67 | df = mark_days_since(df, ['PriceHigh', 'PriceLow', 'Halving'])
68 | return df
69 |
70 |
71 | def fetch_price_data() -> pd.DataFrame:
72 | response = HTTP.get(
73 | 'https://api.coinmarketcap.com/data-api/v3/cryptocurrency/detail/chart',
74 | params={
75 | 'id': 1,
76 | 'range': 'ALL',
77 | },
78 | )
79 |
80 | response.raise_for_status()
81 | response_json = response.json()
82 | response_x = [float(k) for k in response_json['data']['points']]
83 | response_y = [value['v'][0] for value in response_json['data']['points'].values()]
84 |
85 | df = pd.DataFrame(
86 | {
87 | 'Date': response_x,
88 | 'Price': response_y,
89 | }
90 | )
91 | df['Date'] = pd.to_datetime(df['Date'], unit='s').dt.tz_localize(None).dt.floor('d')
92 | df.sort_values(by='Date', inplace=True)
93 | df.drop_duplicates('Date', keep='last', inplace=True)
94 |
95 | return df
96 |
97 |
98 | def fix_current_day_data(df: pd.DataFrame) -> pd.DataFrame:
99 | row = df.iloc[-1].copy()
100 |
101 | target_total_blocks = 24 * 6
102 | target_scale = target_total_blocks / row['TotalBlocks']
103 |
104 | for col_name in ['TotalBlocks', 'TotalGeneration', 'TotalGenerationUSD']:
105 | row[col_name] *= target_scale
106 |
107 | df.iloc[-1] = row
108 | return df
109 |
110 |
111 | def add_block_halving_data(df: pd.DataFrame) -> pd.DataFrame:
112 | reward_halving_every = 210000
113 | current_block_halving_id = reward_halving_every
114 | current_block_production = 50
115 | df['Halving'] = 0
116 | df['NextHalvingBlock'] = current_block_halving_id
117 |
118 | while True:
119 | df.loc[
120 | (current_block_halving_id - reward_halving_every) <= df['MaxBlockID'],
121 | 'BlockGeneration',
122 | ] = current_block_production
123 |
124 | block_halving_row = df[
125 | (df['MinBlockID'] <= current_block_halving_id) & (df['MaxBlockID'] >= current_block_halving_id)
126 | ].squeeze()
127 |
128 | if block_halving_row.shape[0] == 0:
129 | break
130 |
131 | current_block_halving_id += reward_halving_every
132 | current_block_production /= 2
133 | df.loc[block_halving_row.name, 'Halving'] = 1
134 | df.loc[df.index > block_halving_row.name, 'NextHalvingBlock'] = current_block_halving_id
135 |
136 | df['DaysToHalving'] = pd.to_timedelta((df['NextHalvingBlock'] - df['MaxBlockID']) / (24 * 6), unit='D')
137 | df['NextHalvingDate'] = df['Date'] + df['DaysToHalving']
138 | return df
139 |
--------------------------------------------------------------------------------
/main.py:
--------------------------------------------------------------------------------
1 | import asyncio
2 | import time
3 | import traceback
4 | from pathlib import Path
5 |
6 | import fire
7 | import numpy as np
8 | import pandas as pd
9 | import seaborn as sns
10 | from matplotlib import pyplot as plt
11 | from pyfiglet import figlet_format
12 | from sty import bg, ef, fg, rs
13 | from tqdm import tqdm
14 |
15 | from fetch_bitcoin_data import fetch_bitcoin_data
16 | from metrics.base_metric import BaseMetric
17 | from metrics.mvrv_z_score import MVRVMetric
18 | from metrics.pi_cycle import PiCycleMetric
19 | from metrics.puell_multiple import PuellMetric
20 | from metrics.reserve_risk import ReserveRiskMetric
21 | from metrics.rhodl_ratio import RHODLMetric
22 | from metrics.rupl import RUPLMetric
23 | from metrics.trolololo import TrolololoMetric
24 | from metrics.two_year_moving_average import TwoYearMovingAverageMetric
25 | from metrics.woobull_topcap_cvdd import WoobullMetric
26 | from utils import format_percentage, get_color
27 |
28 |
29 | def get_metrics() -> list[BaseMetric]:
30 | """
31 | Returns a list of available metrics to be calculated.
32 | """
33 | return [
34 | PiCycleMetric(),
35 | RUPLMetric(),
36 | RHODLMetric(),
37 | PuellMetric(),
38 | TwoYearMovingAverageMetric(),
39 | TrolololoMetric(),
40 | MVRVMetric(),
41 | ReserveRiskMetric(),
42 | WoobullMetric(),
43 | ]
44 |
45 |
46 | def calculate_confidence_score(df: pd.DataFrame, cols: list[str]) -> pd.Series:
47 | """
48 | Calculate the confidence score for a DataFrame.
49 |
50 | This function takes in a DataFrame and a list of column names
51 | and returns a Series with the mean value of the specified columns for each row.
52 |
53 | Args:
54 | df: A pandas DataFrame.
55 | cols: A list of column names to include in the calculation.
56 |
57 | Returns:
58 | A pandas Series with the mean value for the specified columns for each row in the DataFrame.
59 | """
60 | return df[cols].mean(axis=1)
61 |
62 |
63 | async def run(json_file: str, charts_file: str, output_dir: str | None) -> None:
64 | output_dir_path = Path.cwd() if output_dir is None else Path(output_dir)
65 |
66 | json_file_path = output_dir_path / Path(json_file)
67 | charts_file_path = output_dir_path / Path(charts_file)
68 |
69 | if not output_dir_path.exists():
70 | output_dir_path.mkdir(mode=0o755, parents=True)
71 |
72 | df_bitcoin = fetch_bitcoin_data()
73 | df_bitcoin_org = df_bitcoin.copy()
74 |
75 | current_price = df_bitcoin['Price'].tail(1).values[0]
76 | print('Current Bitcoin price: ' + ef.b + fg.li_green + bg.da_green + f' $ {round(current_price):,} ' + rs.all)
77 |
78 | metrics = get_metrics()
79 | metrics_cols = []
80 | metrics_descriptions = []
81 |
82 | sns.set(
83 | font_scale=0.225,
84 | rc={
85 | 'figure.titlesize': 12, # For suptitle (overridden later)
86 | 'axes.titlesize': 7.5, # 50% larger than original 5
87 | 'axes.labelsize': 6, # 50% larger than original 4
88 | 'xtick.labelsize': 4,
89 | 'ytick.labelsize': 4,
90 | 'lines.linewidth': 0.5,
91 | 'grid.linewidth': 0.3,
92 | 'savefig.dpi': 1000,
93 | 'figure.dpi': 300,
94 | },
95 | )
96 |
97 | axes_per_metric = 2
98 | fig, axes = plt.subplots(len(metrics), axes_per_metric, figsize=(4 * axes_per_metric, 3 * len(metrics)))
99 | axes = axes.reshape(-1, axes_per_metric)
100 |
101 | # Adjust layout
102 | plt.tight_layout(pad=10)
103 | plt.subplots_adjust(top=0.98)
104 |
105 | # Updated title
106 | plt.suptitle("CBBI metric data input → output", fontsize=11.25, weight='bold', y=0.99508)
107 |
108 | for metric, ax_row in zip(metrics, axes, strict=True):
109 | # Swap chart positions so visual flow goes from left to right.
110 | df_bitcoin[metric.name] = (await metric.calculate(df_bitcoin_org.copy(), [ax_row[1], ax_row[0]])).clip(0, 1)
111 | metrics_cols.append(metric.name)
112 | metrics_descriptions.append(metric.description)
113 |
114 | # Add black horizontal lines at y=1 and y=0 to show metric boundaries.
115 | ax_row[1].axhline(y=1, color='black', linewidth=0.5)
116 | ax_row[1].axhline(y=0, color='black', linewidth=0.5)
117 |
118 | # Shade above y=1 and below y=0 with 10% black, to bring focus to the data within range.
119 | y_min, y_max = ax_row[1].get_ylim() # Get the y-axis limits for reference
120 | # Shade above y=1 to the top edge
121 | ax_row[1].fill_betweenx(
122 | y=[1, y_max], # From y=1 to the top
123 | x1=0, x2=1, # Full width in axes fraction (0 to 1)
124 | transform=ax_row[1].get_yaxis_transform(), # Use y-data coordinates, x-axes fraction
125 | color='black', alpha=0.1, edgecolor='none', zorder=0
126 | )
127 | # Shade below y=0 to the bottom edge
128 | ax_row[1].fill_betweenx(
129 | y=[y_min, 0], # From bottom to y=0
130 | x1=0, x2=1, # Full width in axes fraction (0 to 1)
131 | transform=ax_row[1].get_yaxis_transform(), # Use y-data coordinates, x-axes fraction
132 | color='black', alpha=0.1, edgecolor='none', zorder=0
133 | )
134 |
135 | # Add a gray arrow between charts, to make directional flow very clear.
136 | ax_row[0].annotate(
137 | '',
138 | xy=(1.0967, 0.75), xycoords='axes fraction',
139 | xytext=(1.0367, 0.75), textcoords='axes fraction',
140 | arrowprops=dict(arrowstyle='->', color='darkgray', lw=1.5, shrinkA=0, shrinkB=0, mutation_scale=10),
141 | ha='center', va='center'
142 | )
143 |
144 | print('Generating charts…')
145 | plt.savefig(charts_file_path)
146 |
147 | confidence_col = 'Confidence'
148 |
149 | df_result = pd.DataFrame(df_bitcoin[['Date', 'Price', *metrics_cols]])
150 | df_result.set_index('Date', inplace=True)
151 | df_result[confidence_col] = calculate_confidence_score(df_result, metrics_cols)
152 | df_result.to_json(json_file_path, double_precision=4, date_unit='s', indent=2)
153 |
154 | df_result_last = df_result.tail(1)
155 | confidence_details = {
156 | description: df_result_last[name].iloc[0]
157 | for name, description in zip(metrics_cols, metrics_descriptions, strict=True)
158 | }
159 |
160 | print('\n' + ef.b + ':: Confidence we are at the peak ::' + rs.all)
161 | print(
162 | fg.cyan
163 | + ef.bold
164 | + figlet_format(format_percentage(df_result_last[confidence_col].iloc[0], ''), font='univers')
165 | + rs.all,
166 | end='',
167 | )
168 |
169 | for description, value in confidence_details.items():
170 | if not np.isnan(value):
171 | print(fg.white + get_color(value) + f'{format_percentage(value)} ' + rs.all, end='')
172 | print(f' - {description}')
173 |
174 | print()
175 | print('Source code: ' + ef.u + fg.li_blue + 'https://github.com/Zaczero/CBBI' + rs.all)
176 | print('License: ' + ef.b + 'AGPL-3.0' + rs.all)
177 | print()
178 |
179 |
180 | def run_and_retry(
181 | json_file: str = 'latest.json',
182 | charts_file: str = 'charts.svg',
183 | output_dir: str | None = 'output',
184 | max_attempts: int = 10,
185 | sleep_seconds_on_error: int = 10,
186 | ) -> None:
187 | """
188 | Calculates the current CBBI confidence value alongside all the required metrics.
189 | Everything gets pretty printed to the current standard output and a clean copy
190 | is saved to a JSON file specified by the path in the ``json_file`` argument.
191 | A charts image is generated on the path specified by the ``charts_file`` argument
192 | which summarizes all individual metrics' historical data in a visual way.
193 | The execution is attempted multiple times in case an error occurs.
194 |
195 | Args:
196 | json_file: File path where the output is saved in the JSON format.
197 | charts_file: File path where the charts image is saved (formats supported by pyplot.savefig).
198 | output_dir: Directory path where the output is stored.
199 | If set to ``None`` then use the current working directory.
200 | If the directory does not exist, it will be created.
201 | max_attempts: Maximum number of attempts before termination. An attempt is counted when an error occurs.
202 | sleep_seconds_on_error: Duration of the sleep in seconds before attempting again after an error occurs.
203 |
204 | Returns:
205 | None
206 | """
207 | assert max_attempts > 0, 'Value of the max_attempts argument must be positive'
208 | assert sleep_seconds_on_error >= 0, 'Value of the sleep_seconds_on_error argument must be non-negative'
209 |
210 | for _ in range(max_attempts):
211 | try:
212 | asyncio.run(run(json_file, charts_file, output_dir))
213 | exit(0)
214 |
215 | except Exception:
216 | print(fg.black + bg.yellow + ' An error has occurred! ' + rs.all)
217 | traceback.print_exc()
218 |
219 | print(f'\nRetrying in {sleep_seconds_on_error} seconds…', flush=True)
220 | for _ in tqdm(range(sleep_seconds_on_error)):
221 | time.sleep(1)
222 |
223 | print(f'Max attempts limit has been reached ({max_attempts}).')
224 | print('Better luck next time!')
225 | exit(-1)
226 |
227 |
228 | if __name__ == '__main__':
229 | fire.Fire(run_and_retry)
230 |
--------------------------------------------------------------------------------
/metrics/base_metric.py:
--------------------------------------------------------------------------------
1 | import traceback
2 | from abc import ABC, abstractmethod
3 |
4 | import pandas as pd
5 | from matplotlib.axes import Axes
6 | from sty import bg, fg, rs
7 |
8 | from api.cbbiinfo_api import cbbi_fetch
9 | from utils import send_error_notification
10 |
11 |
12 | class BaseMetric(ABC):
13 | @property
14 | @abstractmethod
15 | def name(self) -> str:
16 | pass
17 |
18 | @property
19 | @abstractmethod
20 | def description(self) -> str:
21 | pass
22 |
23 | @abstractmethod
24 | def _calculate(self, df: pd.DataFrame, ax: list[Axes]) -> pd.Series:
25 | pass
26 |
27 | def _fallback(self, df: pd.DataFrame) -> pd.Series:
28 | df = df.merge(cbbi_fetch(self.name), on='Date', how='left')
29 | df['Value'] = df['Value'].ffill()
30 |
31 | return df['Value']
32 |
33 | async def calculate(self, df: pd.DataFrame, ax: list[Axes]) -> pd.Series:
34 | try:
35 | return self._calculate(df, ax)
36 | except Exception as ex:
37 | traceback.print_exc()
38 | await send_error_notification(ex)
39 |
40 | print(fg.black + bg.yellow + f' Requesting fallback values for {self.name} (from CBBI.info) ' + rs.all)
41 | return self._fallback(df)
42 |
--------------------------------------------------------------------------------
/metrics/mvrv_z_score.py:
--------------------------------------------------------------------------------
1 | import numpy as np
2 | import pandas as pd
3 | import seaborn as sns
4 | from matplotlib.axes import Axes
5 | from sklearn.linear_model import LinearRegression
6 |
7 | from api.coinsoto_api import cs_fetch
8 | from metrics.base_metric import BaseMetric
9 | from utils import add_common_markers
10 |
11 |
12 | class MVRVMetric(BaseMetric):
13 | @property
14 | def name(self) -> str:
15 | return 'MVRV'
16 |
17 | @property
18 | def description(self) -> str:
19 | return 'MVRV Z-Score'
20 |
21 | def _calculate(self, df: pd.DataFrame, ax: list[Axes]) -> pd.Series:
22 | bull_days_shift = 6
23 | low_model_adjust = 0.26
24 |
25 | df = df.merge(
26 | cs_fetch(
27 | path='chain/index/charts?type=/charts/mvrv-zscore/',
28 | data_selector='value4',
29 | col_name='MVRV',
30 | ),
31 | on='Date',
32 | how='left',
33 | )
34 | df.loc[df['DaysSinceHalving'] < df['DaysSincePriceLow'], 'MVRV'] = df['MVRV'].shift(bull_days_shift)
35 | df['MVRV'] = df['MVRV'].ffill()
36 | df['MVRV'] = np.log(df['MVRV'] + 1)
37 |
38 | high_rows = df.loc[df['PriceHigh'] == 1]
39 | high_x = high_rows.index.values.reshape(-1, 1)
40 | high_y = high_rows['MVRV'].values.reshape(-1, 1)
41 |
42 | low_rows = df.loc[df['PriceLow'] == 1]
43 | low_x = low_rows.index.values.reshape(-1, 1)
44 | low_y = low_rows['MVRV'].values.reshape(-1, 1)
45 |
46 | x = df.index.values.reshape(-1, 1)
47 |
48 | lin_model = LinearRegression()
49 | lin_model.fit(high_x, high_y)
50 | df['HighModel'] = lin_model.predict(x)
51 |
52 | lin_model.fit(low_x, low_y)
53 | df['LowModel'] = lin_model.predict(x) + low_model_adjust
54 |
55 | df['Index'] = (df['MVRV'] - df['LowModel']) / (df['HighModel'] - df['LowModel'])
56 |
57 | df['IndexNoNa'] = df['Index'].fillna(0)
58 | ax[0].set_title(self.description)
59 | sns.lineplot(data=df, x='Date', y='IndexNoNa', ax=ax[0])
60 | add_common_markers(df, ax[0])
61 |
62 | sns.lineplot(data=df, x='Date', y='MVRV', ax=ax[1])
63 | sns.lineplot(data=df, x='Date', y='HighModel', ax=ax[1])
64 | sns.lineplot(data=df, x='Date', y='LowModel', ax=ax[1])
65 | add_common_markers(df, ax[1], price_line=False)
66 |
67 | return df['Index']
68 |
--------------------------------------------------------------------------------
/metrics/pi_cycle.py:
--------------------------------------------------------------------------------
1 | from itertools import zip_longest
2 |
3 | import numpy as np
4 | import pandas as pd
5 | import seaborn as sns
6 | from matplotlib.axes import Axes
7 |
8 | from metrics.base_metric import BaseMetric
9 | from utils import add_common_markers, mark_highs_lows, split_df_on_index_gap
10 |
11 |
12 | class PiCycleMetric(BaseMetric):
13 | @property
14 | def name(self) -> str:
15 | return 'PiCycle'
16 |
17 | @property
18 | def description(self) -> str:
19 | return 'Pi Cycle Top Indicator'
20 |
21 | def _calculate(self, df: pd.DataFrame, ax: list[Axes]) -> pd.Series:
22 | df['111DMA'] = df['Price'].rolling(111).mean()
23 | df['350DMAx2'] = df['Price'].rolling(350).mean() * 2
24 |
25 | df['111DMALog'] = np.log(df['111DMA'])
26 | df['350DMAx2Log'] = np.log(df['350DMAx2'])
27 | df['PiCycleDiff'] = np.abs(df['111DMALog'] - df['350DMAx2Log'])
28 | df['PiCycleDiffThreshold'] = 0.0
29 |
30 | df['PiCycleIsFluke'] = df['111DMA'] > df['350DMAx2']
31 |
32 | df_flukes = [*split_df_on_index_gap(df[df['PiCycleIsFluke']])]
33 | df_actuals = [*split_df_on_index_gap(df[~df['PiCycleIsFluke']])]
34 |
35 | for df_fluke, df_actual, df_fluke_next in zip_longest(df_flukes, df_actuals[1:], df_flukes[1:], fillvalue=None):
36 | if df_fluke is None:
37 | break
38 |
39 | max_divergence_idx = df_fluke['PiCycleDiff'].argmax()
40 | max_divergence_row = df_fluke.iloc[max_divergence_idx]
41 | df.loc[max_divergence_row.name < df.index, 'PiCycleDiffThreshold'] = max_divergence_row['PiCycleDiff']
42 |
43 | if df_actual is not None:
44 | df_actual_above = df_actual[df_actual['PiCycleDiff'] >= max_divergence_row['PiCycleDiff']]
45 |
46 | if df_actual_above.shape[0] > 0:
47 | df.loc[df_actual_above.index.min() <= df.index, 'PiCycleDiffThreshold'] = 0
48 |
49 | if df_fluke_next is not None:
50 | df.loc[df_fluke_next.index.min() <= df.index, 'PiCycleDiffThreshold'] = 0
51 |
52 | df.loc[df['PiCycleDiff'] < df['PiCycleDiffThreshold'], 'PiCycleDiff'] = df['PiCycleDiffThreshold']
53 | df = mark_highs_lows(df, 'PiCycleDiff', True, round(365 * 2), 365)
54 |
55 | for _, row in df.loc[df['PiCycleDiffHigh'] == 1].iterrows():
56 | df.loc[df.index > row.name, 'PreviousPiCycleDiffHighValue'] = row['PiCycleDiff']
57 |
58 | df['PiCycleIndex'] = 1 - (df['PiCycleDiff'] / df['PreviousPiCycleDiffHighValue'])
59 | df.loc[df['PiCycleIndex'] < 0, 'PiCycleIndex'] = 0
60 |
61 | df['PiCycleIndexNoNa'] = df['PiCycleIndex'].fillna(0)
62 | ax[0].set_title(self.description)
63 | sns.lineplot(data=df, x='Date', y='PiCycleIndexNoNa', ax=ax[0])
64 | add_common_markers(df, ax[0])
65 |
66 | return df['PiCycleIndex']
67 |
--------------------------------------------------------------------------------
/metrics/puell_multiple.py:
--------------------------------------------------------------------------------
1 | import numpy as np
2 | import pandas as pd
3 | import seaborn as sns
4 | from matplotlib.axes import Axes
5 | from sklearn.linear_model import LinearRegression
6 |
7 | from api.coinsoto_api import cs_fetch
8 | from metrics.base_metric import BaseMetric
9 | from utils import add_common_markers
10 |
11 |
12 | class PuellMetric(BaseMetric):
13 | @property
14 | def name(self) -> str:
15 | return 'Puell'
16 |
17 | @property
18 | def description(self) -> str:
19 | return 'Puell Multiple'
20 |
21 | def _calculate(self, df: pd.DataFrame, ax: list[Axes]) -> pd.Series:
22 | df = df.merge(
23 | cs_fetch(
24 | path='getPuellMultiple',
25 | data_selector='puellMultiplList',
26 | col_name='Puell',
27 | ),
28 | on='Date',
29 | how='left',
30 | )
31 | df['Puell'] = df['Puell'].ffill()
32 | df['PuellLog'] = np.log(df['Puell'])
33 |
34 | high_rows = df.loc[df['PriceHigh'] == 1]
35 | high_x = high_rows.index.values.reshape(-1, 1)
36 | high_y = high_rows['PuellLog'].values.reshape(-1, 1)
37 |
38 | # low_rows = df.loc[df['PriceLow'] == 1][1:]
39 | # low_x = low_rows.index.values.reshape(-1, 1)
40 | # low_y = low_rows['PuellLog'].values.reshape(-1, 1)
41 |
42 | x = df.index.values.reshape(-1, 1)
43 |
44 | lin_model = LinearRegression()
45 | lin_model.fit(high_x, high_y)
46 | df['PuellLogHighModel'] = lin_model.predict(x)
47 |
48 | # lin_model.fit(low_x, low_y)
49 | # df['PuellLogLowModel'] = lin_model.predict(x)
50 | df['PuellLogLowModel'] = -1
51 |
52 | df['PuellIndex'] = (df['PuellLog'] - df['PuellLogLowModel']) / (
53 | df['PuellLogHighModel'] - df['PuellLogLowModel']
54 | )
55 |
56 | df['PuellIndexNoNa'] = df['PuellIndex'].fillna(0)
57 | ax[0].set_title(self.description)
58 | sns.lineplot(data=df, x='Date', y='PuellIndexNoNa', ax=ax[0])
59 | add_common_markers(df, ax[0])
60 |
61 | sns.lineplot(data=df, x='Date', y='PuellLog', ax=ax[1])
62 | sns.lineplot(data=df, x='Date', y='PuellLogHighModel', ax=ax[1])
63 | sns.lineplot(data=df, x='Date', y='PuellLogLowModel', ax=ax[1])
64 | add_common_markers(df, ax[1], price_line=False)
65 |
66 | return df['PuellIndex']
67 |
--------------------------------------------------------------------------------
/metrics/reserve_risk.py:
--------------------------------------------------------------------------------
1 | import numpy as np
2 | import pandas as pd
3 | import seaborn as sns
4 | from matplotlib.axes import Axes
5 | from sklearn.linear_model import LinearRegression
6 |
7 | from api.coinsoto_api import cs_fetch
8 | from metrics.base_metric import BaseMetric
9 | from utils import add_common_markers
10 |
11 |
12 | class ReserveRiskMetric(BaseMetric):
13 | @property
14 | def name(self) -> str:
15 | return 'ReserveRisk'
16 |
17 | @property
18 | def description(self) -> str:
19 | return 'Reserve Risk'
20 |
21 | def _calculate(self, df: pd.DataFrame, ax: list[Axes]) -> pd.Series:
22 | days_shift = 1
23 |
24 | df = df.merge(
25 | cs_fetch(
26 | path='chain/index/charts?type=/charts/reserve-risk/',
27 | data_selector='value4',
28 | col_name='Risk',
29 | ),
30 | on='Date',
31 | how='left',
32 | )
33 | df['Risk'] = df['Risk'].shift(days_shift, fill_value=np.nan)
34 | df['Risk'] = df['Risk'].ffill()
35 | df['RiskLog'] = np.log(df['Risk'])
36 |
37 | high_rows = df.loc[df['PriceHigh'] == 1]
38 | high_x = high_rows.index.values.reshape(-1, 1)
39 | high_y = high_rows['RiskLog'].values.reshape(-1, 1)
40 |
41 | low_rows = df.loc[df['PriceLow'] == 1][1:]
42 | low_x = low_rows.index.values.reshape(-1, 1)
43 | low_y = low_rows['RiskLog'].values.reshape(-1, 1)
44 |
45 | x = df.index.values.reshape(-1, 1)
46 |
47 | lin_model = LinearRegression()
48 | lin_model.fit(high_x, high_y)
49 | df['HighModel'] = lin_model.predict(x)
50 | df['HighModel'] = df['HighModel'] - 0.15
51 |
52 | lin_model.fit(low_x, low_y)
53 | df['LowModel'] = lin_model.predict(x)
54 |
55 | df['RiskIndex'] = (df['RiskLog'] - df['LowModel']) / (df['HighModel'] - df['LowModel'])
56 |
57 | df['RiskIndexNoNa'] = df['RiskIndex'].fillna(0)
58 | ax[0].set_title(self.description)
59 | sns.lineplot(data=df, x='Date', y='RiskIndexNoNa', ax=ax[0])
60 | add_common_markers(df, ax[0])
61 |
62 | sns.lineplot(data=df, x='Date', y='RiskLog', ax=ax[1])
63 | sns.lineplot(data=df, x='Date', y='HighModel', ax=ax[1])
64 | sns.lineplot(data=df, x='Date', y='LowModel', ax=ax[1])
65 | add_common_markers(df, ax[1], price_line=False)
66 |
67 | return df['RiskIndex']
68 |
--------------------------------------------------------------------------------
/metrics/rhodl_ratio.py:
--------------------------------------------------------------------------------
1 | import traceback
2 |
3 | import numpy as np
4 | import pandas as pd
5 | import seaborn as sns
6 | from matplotlib.axes import Axes
7 | from sklearn.linear_model import LinearRegression
8 | from sty import bg, fg, rs
9 |
10 | from api.coinsoto_api import cs_fetch
11 | from api.glassnode_api import gn_fetch
12 | from metrics.base_metric import BaseMetric
13 | from utils import add_common_markers
14 |
15 |
16 | class RHODLMetric(BaseMetric):
17 | @property
18 | def name(self) -> str:
19 | return 'RHODL'
20 |
21 | @property
22 | def description(self) -> str:
23 | return 'RHODL Ratio'
24 |
25 | def _calculate(self, df: pd.DataFrame, ax: list[Axes]) -> pd.Series:
26 | try:
27 | remote_df = cs_fetch(
28 | path='chain/index/charts?type=/charts/rhodl-ratio/',
29 | data_selector='value1',
30 | col_name='RHODL',
31 | )
32 | except Exception:
33 | traceback.print_exc()
34 | print(fg.black + bg.yellow + f' Requesting fallback values for {self.name} (from GlassNode) ' + rs.all)
35 |
36 | remote_df = gn_fetch(url_selector='rhodl_ratio', col_name='RHODL', a='BTC')
37 |
38 | df = df.merge(remote_df, on='Date', how='left')
39 | df['RHODL'] = df['RHODL'].ffill()
40 | df['RHODLLog'] = np.log(df['RHODL'])
41 |
42 | high_rows = df.loc[(df['PriceHigh'] == 1) | (df['Date'] == '2024-12-18')]
43 | high_x = high_rows.index.values.reshape(-1, 1)
44 | high_y = high_rows['RHODLLog'].values.reshape(-1, 1)
45 |
46 | low_rows = df.loc[df['PriceLow'] == 1][1:]
47 | low_x = low_rows.index.values.reshape(-1, 1)
48 | low_y = low_rows['RHODLLog'].values.reshape(-1, 1)
49 |
50 | x = df.index.values.reshape(-1, 1)
51 |
52 | lin_model = LinearRegression()
53 | lin_model.fit(high_x, high_y)
54 | df['RHODLLogHighModel'] = lin_model.predict(x)
55 |
56 | lin_model.fit(low_x, low_y)
57 | df['RHODLLogLowModel'] = lin_model.predict(x)
58 |
59 | df['RHODLIndex'] = (df['RHODLLog'] - df['RHODLLogLowModel']) / (
60 | df['RHODLLogHighModel'] - df['RHODLLogLowModel']
61 | )
62 |
63 | df['RHODLIndexNoNa'] = df['RHODLIndex'].fillna(0)
64 | ax[0].set_title(self.description)
65 | sns.lineplot(data=df, x='Date', y='RHODLIndexNoNa', ax=ax[0])
66 | add_common_markers(df, ax[0])
67 |
68 | sns.lineplot(data=df, x='Date', y='RHODLLog', ax=ax[1])
69 | sns.lineplot(data=df, x='Date', y='RHODLLogHighModel', ax=ax[1])
70 | sns.lineplot(data=df, x='Date', y='RHODLLogLowModel', ax=ax[1])
71 | add_common_markers(df, ax[1], price_line=False)
72 |
73 | return df['RHODLIndex']
74 |
--------------------------------------------------------------------------------
/metrics/rupl.py:
--------------------------------------------------------------------------------
1 | import pandas as pd
2 | import seaborn as sns
3 | from matplotlib.axes import Axes
4 | from sklearn.linear_model import LinearRegression
5 |
6 | from api.coinsoto_api import cs_fetch
7 | from metrics.base_metric import BaseMetric
8 | from utils import add_common_markers
9 |
10 |
11 | class RUPLMetric(BaseMetric):
12 | @property
13 | def name(self) -> str:
14 | return 'RUPL'
15 |
16 | @property
17 | def description(self) -> str:
18 | return 'RUPL/NUPL Chart'
19 |
20 | def _calculate(self, df: pd.DataFrame, ax: list[Axes]) -> pd.Series:
21 | df = df.merge(
22 | cs_fetch(
23 | path='chain/index/charts?type=/charts/relative-unrealized-prof/',
24 | data_selector='value1',
25 | col_name='RUPL',
26 | ),
27 | on='Date',
28 | how='left',
29 | )
30 | df['RUPL'] = df['RUPL'].ffill()
31 |
32 | high_rows = df.loc[df['PriceHigh'] == 1]
33 | high_x = high_rows.index.values.reshape(-1, 1)
34 | high_y = high_rows['RUPL'].values.reshape(-1, 1)
35 |
36 | low_rows = df.loc[df['PriceLow'] == 1][1:]
37 | low_x = low_rows.index.values.reshape(-1, 1)
38 | low_y = low_rows['RUPL'].values.reshape(-1, 1)
39 |
40 | x = df.index.values.reshape(-1, 1)
41 |
42 | lin_model = LinearRegression()
43 | lin_model.fit(high_x, high_y)
44 | df['HighModel'] = lin_model.predict(x)
45 |
46 | lin_model.fit(low_x, low_y)
47 | df['LowModel'] = lin_model.predict(x)
48 |
49 | df['RUPLIndex'] = (df['RUPL'] - df['LowModel']) / (df['HighModel'] - df['LowModel'])
50 |
51 | ax[0].set_title(self.description)
52 | sns.lineplot(data=df, x='Date', y='RUPLIndex', ax=ax[0])
53 | add_common_markers(df, ax[0])
54 |
55 | sns.lineplot(data=df, x='Date', y='RUPL', ax=ax[1])
56 | sns.lineplot(data=df, x='Date', y='HighModel', ax=ax[1])
57 | sns.lineplot(data=df, x='Date', y='LowModel', ax=ax[1])
58 | add_common_markers(df, ax[1], price_line=False)
59 |
60 | return df['RUPLIndex']
61 |
--------------------------------------------------------------------------------
/metrics/trolololo.py:
--------------------------------------------------------------------------------
1 | import numpy as np
2 | import pandas as pd
3 | import seaborn as sns
4 | from matplotlib.axes import Axes
5 | from sklearn.linear_model import LinearRegression
6 |
7 | from metrics.base_metric import BaseMetric
8 | from utils import add_common_markers
9 |
10 |
11 | class TrolololoMetric(BaseMetric):
12 | @property
13 | def name(self) -> str:
14 | return 'Trolololo'
15 |
16 | @property
17 | def description(self) -> str:
18 | return 'Bitcoin Trolololo Trend Line'
19 |
20 | def _calculate(self, df: pd.DataFrame, ax: list[Axes]) -> pd.Series:
21 | begin_date = pd.to_datetime('2012-01-01')
22 |
23 | df['TroloDaysSinceBegin'] = (df['Date'] - begin_date).dt.days
24 |
25 | # Maximum Bubble Territory
26 | df['TroloTopPrice'] = np.power(10, 2.900 * np.log(df['TroloDaysSinceBegin'] + 1400) - 19.463)
27 | df['TroloTopPriceLog'] = np.log(df['TroloTopPrice'])
28 |
29 | # Basically a Fire Sale
30 | df['TroloBottomPrice'] = np.power(10, 2.788 * np.log(df['TroloDaysSinceBegin'] + 1200) - 19.463)
31 | df['TroloBottomPriceLog'] = np.log(df['TroloBottomPrice'])
32 |
33 | df['TroloDifference'] = df['TroloTopPriceLog'] - df['TroloBottomPriceLog']
34 | df['TroloOvershootActual'] = df['PriceLog'] - df['TroloTopPriceLog']
35 | df['TroloUndershootActual'] = df['PriceLog'] - df['TroloBottomPriceLog']
36 |
37 | high_rows = df.loc[(df['PriceHigh'] == 1) & (df['Date'] >= begin_date)]
38 | high_x = high_rows.index.values.reshape(-1, 1)
39 | high_y = high_rows['TroloOvershootActual'].values.reshape(-1, 1)
40 | high_y[0] *= 0.6 # the first value seems too high
41 |
42 | low_rows = df.loc[(df['PriceLow'] == 1) & (df['Date'] >= begin_date)]
43 | low_x = low_rows.index.values.reshape(-1, 1)
44 | low_y = low_rows['TroloUndershootActual'].values.reshape(-1, 1)
45 |
46 | x = df.index.values.reshape(-1, 1)
47 |
48 | lin_model = LinearRegression()
49 | lin_model.fit(high_x, high_y)
50 | df['TroloOvershootModel'] = lin_model.predict(x)
51 |
52 | lin_model.fit(low_x, low_y)
53 | df['TroloUndershootModel'] = lin_model.predict(x)
54 |
55 | df['TroloHighModel'] = df['TroloTopPriceLog'] + df['TroloOvershootModel']
56 | df['TroloLowModel'] = df['TroloBottomPriceLog'] + df['TroloUndershootModel']
57 |
58 | df['TroloIndex'] = (df['PriceLog'] - df['TroloLowModel']) / (df['TroloHighModel'] - df['TroloLowModel'])
59 |
60 | ax[0].set_title(self.description)
61 | sns.lineplot(data=df, x='Date', y='TroloIndex', ax=ax[0])
62 | add_common_markers(df, ax[0])
63 |
64 | sns.lineplot(data=df, x='Date', y='PriceLog', ax=ax[1])
65 | sns.lineplot(data=df, x='Date', y='TroloHighModel', ax=ax[1])
66 | sns.lineplot(data=df, x='Date', y='TroloLowModel', ax=ax[1])
67 | add_common_markers(df, ax[1], price_line=False)
68 |
69 | return df['TroloIndex']
70 |
--------------------------------------------------------------------------------
/metrics/two_year_moving_average.py:
--------------------------------------------------------------------------------
1 | import numpy as np
2 | import pandas as pd
3 | import seaborn as sns
4 | from matplotlib.axes import Axes
5 | from sklearn.linear_model import LinearRegression
6 |
7 | from api.coinsoto_api import cs_fetch
8 | from metrics.base_metric import BaseMetric
9 | from utils import add_common_markers
10 |
11 |
12 | class TwoYearMovingAverageMetric(BaseMetric):
13 | @property
14 | def name(self) -> str:
15 | return '2YMA'
16 |
17 | @property
18 | def description(self) -> str:
19 | return '2 Year Moving Average'
20 |
21 | def _calculate(self, df: pd.DataFrame, ax: list[Axes]) -> pd.Series:
22 | df = df.merge(
23 | cs_fetch(
24 | path='getBtcMultiplier',
25 | data_selector='mA730List',
26 | col_name='2YMA',
27 | ),
28 | on='Date',
29 | how='left',
30 | )
31 | df['2YMA'] = df['2YMA'].ffill()
32 | df['2YMALog'] = np.log(df['2YMA'])
33 | df['2YMALogDiff'] = df['PriceLog'] - df['2YMALog']
34 |
35 | high_rows = df.loc[df['PriceHigh'] == 1]
36 | high_x = high_rows.index.values.reshape(-1, 1)
37 | high_y = high_rows['2YMALogDiff'].values.reshape(-1, 1)
38 |
39 | low_rows = df.loc[df['PriceLow'] == 1]
40 | low_x = low_rows.index.values.reshape(-1, 1)
41 | low_y = low_rows['2YMALogDiff'].values.reshape(-1, 1)
42 |
43 | x = df.index.values.reshape(-1, 1)
44 |
45 | lin_model = LinearRegression()
46 | lin_model.fit(high_x, high_y)
47 | df['2YMALogOvershootModel'] = lin_model.predict(x)
48 |
49 | lin_model.fit(low_x, low_y)
50 | df['2YMALogUndershootModel'] = lin_model.predict(x)
51 |
52 | df['2YMAHighModel'] = df['2YMALogOvershootModel'] + df['2YMALog']
53 | df['2YMALowModel'] = df['2YMALogUndershootModel'] + df['2YMALog']
54 |
55 | df['2YMAIndex'] = (df['PriceLog'] - df['2YMALowModel']) / (df['2YMAHighModel'] - df['2YMALowModel'])
56 |
57 | df['2YMAIndexNoNa'] = df['2YMAIndex'].fillna(0)
58 | ax[0].set_title(self.description)
59 | sns.lineplot(data=df, x='Date', y='2YMAIndexNoNa', ax=ax[0])
60 | add_common_markers(df, ax[0])
61 |
62 | sns.lineplot(data=df, x='Date', y='PriceLog', ax=ax[1])
63 | sns.lineplot(data=df, x='Date', y='2YMAHighModel', ax=ax[1])
64 | sns.lineplot(data=df, x='Date', y='2YMALowModel', ax=ax[1])
65 | add_common_markers(df, ax[1], price_line=False)
66 |
67 | return df['2YMAIndex']
68 |
--------------------------------------------------------------------------------
/metrics/woobull_topcap_cvdd.py:
--------------------------------------------------------------------------------
1 | import numpy as np
2 | import pandas as pd
3 | import seaborn as sns
4 | from matplotlib.axes import Axes
5 | from sklearn.linear_model import LinearRegression
6 |
7 | from metrics.base_metric import BaseMetric
8 | from utils import HTTP, add_common_markers
9 |
10 |
11 | def _fetch_df() -> pd.DataFrame:
12 | response = HTTP.get('https://woocharts.com/bitcoin-price-models/data/chart.json')
13 | response.raise_for_status()
14 | data = response.json()
15 |
16 | df_top = pd.DataFrame(
17 | {
18 | 'Date': data['top_']['x'],
19 | 'Top': data['top_']['y'],
20 | }
21 | )
22 | df_top['Date'] = pd.to_datetime(df_top['Date'], unit='ms').dt.tz_localize(None)
23 |
24 | df_cvdd = pd.DataFrame(
25 | {
26 | 'Date': data['cvdd']['x'],
27 | 'CVDD': data['cvdd']['y'],
28 | }
29 | )
30 | df_cvdd['Date'] = pd.to_datetime(df_cvdd['Date'], unit='ms').dt.tz_localize(None)
31 |
32 | df = df_top.merge(df_cvdd, on='Date')
33 |
34 | return df
35 |
36 |
37 | class WoobullMetric(BaseMetric):
38 | @property
39 | def name(self) -> str:
40 | return 'Woobull'
41 |
42 | @property
43 | def description(self) -> str:
44 | return 'Woobull Top Cap vs CVDD'
45 |
46 | def _calculate(self, df: pd.DataFrame, ax: list[Axes]) -> pd.Series:
47 | df = df.merge(_fetch_df(), on='Date', how='left')
48 | df['Top'] = df['Top'].ffill()
49 | df['TopLog'] = np.log(df['Top'])
50 | df['CVDD'] = df['CVDD'].ffill()
51 | df['CVDDLog'] = np.log(df['CVDD'])
52 |
53 | df['Woobull'] = (df['PriceLog'] - df['CVDDLog']) / (df['TopLog'] - df['CVDDLog'])
54 |
55 | high_rows = df.loc[df['PriceHigh'] == 1]
56 | high_x = high_rows.index.values.reshape(-1, 1)
57 | high_y = high_rows['Woobull'].values.reshape(-1, 1)
58 |
59 | low_rows = df.loc[df['PriceLow'] == 1][1:]
60 | low_x = low_rows.index.values.reshape(-1, 1)
61 | low_y = low_rows['Woobull'].values.reshape(-1, 1)
62 |
63 | x = df.index.values.reshape(-1, 1)
64 |
65 | lin_model = LinearRegression()
66 | lin_model.fit(high_x, high_y)
67 | df['HighModel'] = lin_model.predict(x)
68 | df['HighModel'] = df['HighModel'] - 0.025
69 |
70 | lin_model.fit(low_x, low_y)
71 | df['LowModel'] = lin_model.predict(x)
72 |
73 | df['WoobullIndex'] = (df['Woobull'] - df['LowModel']) / (df['HighModel'] - df['LowModel'])
74 |
75 | ax[0].set_title(self.description)
76 | sns.lineplot(data=df, x='Date', y='WoobullIndex', ax=ax[0])
77 | add_common_markers(df, ax[0])
78 |
79 | sns.lineplot(data=df, x='Date', y='Woobull', ax=ax[1])
80 | sns.lineplot(data=df, x='Date', y='HighModel', ax=ax[1])
81 | sns.lineplot(data=df, x='Date', y='LowModel', ax=ax[1])
82 | add_common_markers(df, ax[1], price_line=False)
83 |
84 | return df['WoobullIndex']
85 |
--------------------------------------------------------------------------------
/pyproject.toml:
--------------------------------------------------------------------------------
1 | [project]
2 | dependencies = [
3 | "filecache",
4 | "fire",
5 | "httpx[brotli,zstd]",
6 | "matplotlib",
7 | "numpy",
8 | "pandas",
9 | "pyfiglet",
10 | "python-telegram-bot",
11 | "scikit-learn",
12 | "seaborn",
13 | "sty",
14 | "tqdm",
15 | ]
16 | name = "cbbi"
17 | requires-python = "~=3.13"
18 | version = "0.0.0"
19 |
20 | [tool.uv]
21 | package = false
22 | python-downloads = "never"
23 | python-preference = "only-system"
24 |
25 | [tool.setuptools]
26 | packages = ["."]
27 |
28 | [tool.ruff]
29 | # Exclude a variety of commonly ignored directories.
30 | exclude = [
31 | ".bzr",
32 | ".direnv",
33 | ".eggs",
34 | ".git",
35 | ".git-rewrite",
36 | ".hg",
37 | ".mypy_cache",
38 | ".nox",
39 | ".pants.d",
40 | ".pytype",
41 | ".ruff_cache",
42 | ".svn",
43 | ".tox",
44 | ".venv",
45 | "__pypackages__",
46 | "_build",
47 | "buck-out",
48 | "build",
49 | "dist",
50 | "node_modules",
51 | "venv",
52 | ]
53 |
54 | indent-width = 4
55 | line-length = 120
56 | target-version = "py313"
57 |
58 | [tool.ruff.lint]
59 | ignore = [
60 | "S101", # assert
61 | ]
62 | # see https://docs.astral.sh/ruff/rules/ for rules documentation
63 | select = [
64 | "A", # flake8-builtins
65 | "ARG", # flake8-unused-argumentsf
66 | "ASYNC", # flake8-async
67 | "B", # flake8-bugbear
68 | # "COM", # flake8-commas
69 | "C4", # flake8-comprehensions
70 | "E4", # pycodestyle
71 | "E7",
72 | "E9",
73 | "F", # pyflakes
74 | # "FBT", # flake8-boolean-trap
75 | "FLY", # flynt
76 | # "FURB", # refurb (preview)
77 | "G", # flake8-logging-format
78 | "I", # isort
79 | "INT", # flake8-gettext
80 | # "LOG", # flake8-logging (preview)
81 | "N", # pep8-naming
82 | "NPY", # numpy
83 | "PERF", # perflint
84 | "PGH", # pygrep-hooks
85 | "PIE", # flake8-pie
86 | "Q", # flake8-quotes
87 | "UP", # pyupgrade
88 | # "PL", # pylint
89 | "PT", # flake8-pytest-style
90 | "PTH", # flake8-use-pathlib
91 | "PYI", # flake8-pyi
92 | "RSE", # flake8-raise
93 | "RUF", # ruff
94 | "S", # flake8-bandit
95 | "SIM", # flake8-simplify
96 | "SLF", # flake8-self
97 | "SLOT", # flake8-slots
98 | "T10", # flake8-debugger
99 | # "T20", # flake8-print
100 | # "TRY", # tryceratops
101 | "YTT", # flake8-2020
102 | ]
103 |
104 | fixable = ["ALL"]
105 | unfixable = []
106 |
107 | [tool.ruff.format]
108 | quote-style = "single"
109 | indent-style = "space"
110 | skip-magic-trailing-comma = false
111 | line-ending = "lf"
112 |
113 | [tool.ruff.lint.flake8-builtins]
114 | builtins-ignorelist = ["id", "open", "type"]
115 |
116 | [tool.ruff.lint.flake8-quotes]
117 | docstring-quotes = "double"
118 | inline-quotes = "single"
119 | multiline-quotes = "double"
120 |
121 | [tool.ruff.lint.pylint]
122 | max-args = 10
123 |
--------------------------------------------------------------------------------
/shell.nix:
--------------------------------------------------------------------------------
1 | {}:
2 |
3 | let
4 | # Update with `nixpkgs-update` command
5 | pkgs = import (fetchTarball "https://github.com/NixOS/nixpkgs/archive/2bfc080955153be0be56724be6fa5477b4eefabb.tar.gz") { };
6 |
7 | pythonLibs = with pkgs; [
8 | stdenv.cc.cc.lib
9 | zlib.out
10 | ];
11 | python' = with pkgs; (symlinkJoin {
12 | name = "python";
13 | paths = [ python313 ];
14 | buildInputs = [ makeWrapper ];
15 | postBuild = ''
16 | wrapProgram "$out/bin/python3.13" --prefix LD_LIBRARY_PATH : "${lib.makeLibraryPath pythonLibs}"
17 | '';
18 | });
19 |
20 | packages' = with pkgs; [
21 | python'
22 | uv
23 | ruff
24 |
25 | (writeShellScriptBin "nixpkgs-update" ''
26 | set -e
27 | hash=$(
28 | curl --silent --location \
29 | https://prometheus.nixos.org/api/v1/query \
30 | -d "query=channel_revision{channel=\"nixpkgs-unstable\"}" | \
31 | grep --only-matching --extended-regexp "[0-9a-f]{40}")
32 | sed -i -E "s|/nixpkgs/archive/[0-9a-f]{40}\.tar\.gz|/nixpkgs/archive/$hash.tar.gz|" shell.nix
33 | echo "Nixpkgs updated to $hash"
34 | '')
35 | (writeShellScriptBin "docker-build-push" ''
36 | set -e
37 | if command -v podman &> /dev/null; then docker() { podman "$@"; } fi
38 | docker push $(docker load < $(nix-build --no-out-link) | sed -En 's/Loaded image: (\S+)/\1/p')
39 | '')
40 | ];
41 |
42 | shell' = ''
43 | export SSL_CERT_FILE=${pkgs.cacert}/etc/ssl/certs/ca-bundle.crt
44 | export PYTHONNOUSERSITE=1
45 | export PYTHONPATH=""
46 | export TZ=UTC
47 |
48 | current_python=$(readlink -e .venv/bin/python || echo "")
49 | current_python=''${current_python%/bin/*}
50 | [ "$current_python" != "${python'}" ] && rm -rf .venv/
51 |
52 | echo "Installing Python dependencies"
53 | export UV_PYTHON="${python'}/bin/python"
54 | uv sync --frozen
55 |
56 | echo "Activating Python virtual environment"
57 | source .venv/bin/activate
58 |
59 | if [ -f .env ]; then
60 | echo "Loading .env file"
61 | set -o allexport
62 | source .env set
63 | set +o allexport
64 | else
65 | echo "Skipped loading .env file (not found)"
66 | fi
67 | '';
68 | in
69 | pkgs.mkShell {
70 | buildInputs = packages';
71 | shellHook = shell';
72 | }
73 |
--------------------------------------------------------------------------------
/utils.py:
--------------------------------------------------------------------------------
1 | import os
2 | import traceback
3 | from datetime import datetime
4 | from math import ceil
5 |
6 | import numpy as np
7 | import pandas as pd
8 | import seaborn as sns
9 | import telegram
10 | from httpx import Client
11 | from matplotlib.axes import Axes
12 | from sty import bg
13 |
14 | HTTP = Client(
15 | headers={'User-Agent': 'Mozilla/5.0 (Linux x86_64; rv:128.0) Gecko/20100101 Firefox/128.0'},
16 | timeout=30,
17 | follow_redirects=True,
18 | )
19 |
20 |
21 | def mark_highs_lows(
22 | df: pd.DataFrame,
23 | col: str,
24 | begin_with_high: bool,
25 | window_size: float,
26 | ignore_last_rows: int,
27 | ) -> pd.DataFrame:
28 | """
29 | Marks highs and lows (peaks) of the column values inside the given DataFrame.
30 | Marked points are indicated by the value '1' inside their corresponding, newly added, '``col``High' and '``col``Low' columns.
31 |
32 | Args:
33 | df: DataFrame from which the column values are selected and to which marked points columns are added.
34 | col: Column name of which values are selected inside the given DataFrame.
35 | begin_with_high: Indicates whether the first peak is high or low.
36 | window_size: Window size for the algorithm to consider.
37 | Too low value will mark too many peaks, whereas, too high value will mark too little peaks.
38 | ignore_last_rows: Amount of trailing DataFrame rows for which highs and lows should not be marked.
39 |
40 | Returns:
41 | Modified input DataFrame with columns, indicating the marked points, added.
42 | """
43 | col_high = col + 'High'
44 | col_low = col + 'Low'
45 |
46 | assert col in df.columns, f'The column name "{col}" (col) could not be found inside the given DataFrame (df)'
47 | assert col_high not in df.columns, 'The DataFrame (df) already contains the "High" column - bug prone'
48 | assert col_low not in df.columns, 'The DataFrame (df) already contains the "Low" column - bug prone'
49 | assert window_size > 0, 'Value of the window_size argument must be at least 1'
50 |
51 | df[col_high] = 0
52 | df[col_low] = 0
53 |
54 | searching_high = begin_with_high
55 | current_index = df.index[0]
56 |
57 | while True:
58 | window = df.loc[current_index : current_index + window_size, col]
59 |
60 | if sum(~np.isnan(window)) == 0 and window.shape[0] > 1:
61 | current_index += window.shape[0]
62 | continue
63 |
64 | if window.shape[0] <= 1:
65 | break
66 |
67 | window_index = window.idxmax() if searching_high else window.idxmin()
68 |
69 | if window_index == current_index:
70 | df.loc[window_index, col_high if searching_high else col_low] = 1
71 | searching_high = not searching_high
72 | window_index = window_index + 1
73 |
74 | current_index = window_index
75 |
76 | df.loc[df.shape[0] - ignore_last_rows :, (col_high, col_low)] = 0
77 |
78 | # stabilize the algorithm until a next major update
79 | df.loc[df['Date'] >= '2023-07-01', (col_high, col_low)] = 0
80 | return df
81 |
82 |
83 | def mark_days_since(df: pd.DataFrame, cols: list[str]) -> pd.DataFrame:
84 | """
85 | This function takes a DataFrame and a list of column names
86 | and calculates the number of days since the last value of 1 for each column in the list.
87 |
88 | The resulting DataFrame will have new columns for each input column, with the column name prefixed by 'DaysSince'.
89 | The value in these new columns will be the number of days since the last value of 1 in the corresponding input column.
90 |
91 | Args:
92 | df: The input DataFrame.
93 | cols: The list of columns in the DataFrame to calculate the days since the last value of 1.
94 |
95 | Returns:
96 | The modified DataFrame with the new columns added.
97 | """
98 | for col in cols:
99 | indexes = df.loc[df[col] == 1].index
100 | df[f'DaysSince{col}'] = df.index.to_series().apply(
101 | lambda v: min([v - index if index <= v else np.nan for index in indexes]) # noqa: B023
102 | )
103 |
104 | return df
105 |
106 |
107 | def add_common_markers(df: pd.DataFrame, ax: Axes, price_line: bool = True) -> None:
108 | """
109 | This function adds common markers to a plot.
110 |
111 | Args:
112 | df: The DataFrame containing the data to be plotted.
113 | ax: The Axes object to be plotted on.
114 | price_line: If True, a line plot of the 'PriceLogInterp' column will be added to the Axes. Default is True.
115 |
116 | Returns:
117 | None
118 | """
119 | if price_line:
120 | sns.lineplot(data=df, x='Date', y='PriceLogInterp', alpha=0.4, color='orange', ax=ax)
121 |
122 | for _, row in df[df['Halving'] == 1].iterrows():
123 | days_since_epoch = (row['Date'] - datetime(1970, 1, 1)).days
124 | ax.axvline(x=days_since_epoch, color='navy', linestyle=':')
125 |
126 | for _, row in df[df['PriceHigh'] == 1].iterrows():
127 | days_since_epoch = (row['Date'] - datetime(1970, 1, 1)).days
128 | ax.axvline(x=days_since_epoch, color='green', linestyle=':')
129 |
130 | for _, row in df[df['PriceLow'] == 1].iterrows():
131 | days_since_epoch = (row['Date'] - datetime(1970, 1, 1)).days
132 | ax.axvline(x=days_since_epoch, color='red', linestyle=':')
133 |
134 |
135 | def split_df_on_index_gap(df: pd.DataFrame, min_gap: int = 1):
136 | """
137 | Split a Pandas DataFrame on gaps in the index values.
138 |
139 | Args:
140 | df: The DataFrame to split.
141 | min_gap: The minimum gap size in the index values to split on.
142 |
143 | Returns:
144 | A list of DataFrames split on the specified gaps in the index values.
145 | """
146 | begin_idx = None
147 | end_idx = None
148 |
149 | for i, _ in df.iterrows():
150 | if begin_idx is None:
151 | begin_idx = i
152 | end_idx = i
153 | elif (i - end_idx) <= min_gap:
154 | end_idx = i
155 | else:
156 | yield df.loc[begin_idx:end_idx]
157 | begin_idx = i
158 | end_idx = i
159 |
160 | if begin_idx is not None:
161 | yield df.loc[begin_idx:end_idx]
162 |
163 |
164 | def format_percentage(val: float, suffix: str = ' %') -> str:
165 | """
166 | Formats a percentage value (0.0 - 1.0) in the standardized way.
167 | Returned value has a constant width and a trailing '%' sign.
168 |
169 | Args:
170 | val: Percentage value to be formatted.
171 | suffix: String to be appended to the result.
172 |
173 | Returns:
174 | Formatted percentage value with a constant width and trailing '%' sign.
175 |
176 | Examples:
177 | >>> print(format_percentage(0.359))
178 | str(' 36 %')
179 |
180 | >>> print(format_percentage(1.1))
181 | str('110 %')
182 | """
183 |
184 | return f'{ceil(val * 100): >3d}{suffix}'
185 |
186 |
187 | def get_color(val: float) -> str:
188 | """
189 | Maps a percentage value (0.0 - 1.0) to its corresponding color.
190 | The color is used to indicate whether the value is low (0.0) or high (1.0).
191 | Returned value is a valid sty-package color string.
192 |
193 | Args:
194 | val: Percentage value to be mapped into a color.
195 |
196 | Returns:
197 | Valid sty-package color string.
198 | """
199 |
200 | config = [
201 | bg.da_red,
202 | 0.3,
203 | bg.da_yellow,
204 | 0.65,
205 | bg.da_green,
206 | 0.85,
207 | bg.da_cyan,
208 | 0.97,
209 | bg.da_magenta,
210 | ]
211 |
212 | bin_index = np.digitize([round(val, 2)], config[1::2])[0]
213 | return config[::2][bin_index]
214 |
215 |
216 | async def send_error_notification(exception: Exception) -> bool:
217 | """
218 | This function sends a notification to a Telegram chat with details of the provided exception.
219 |
220 | Args:
221 | exception: The exception to be reported.
222 |
223 | Returns:
224 | A boolean indicating whether the notification was sent successfully.
225 | """
226 | telegram_token = os.getenv('TELEGRAM_TOKEN')
227 | telegram_chat_id = os.getenv('TELEGRAM_CHAT_ID')
228 | if not telegram_token or not telegram_chat_id:
229 | return False
230 |
231 | async with telegram.Bot(telegram_token) as bot:
232 | await bot.send_message(
233 | telegram_chat_id,
234 | f'🚨 An error has occurred: {exception!s}\n'
235 | f'\n'
236 | f'🔍️ Stack trace\n'
237 | f'{"".join(traceback.format_exception(exception))}',
238 | parse_mode='HTML',
239 | )
240 | return True
241 |
--------------------------------------------------------------------------------
/uv.lock:
--------------------------------------------------------------------------------
1 | version = 1
2 | revision = 1
3 | requires-python = ">=3.13, <4"
4 |
5 | [[package]]
6 | name = "anyio"
7 | version = "4.9.0"
8 | source = { registry = "https://pypi.org/simple" }
9 | dependencies = [
10 | { name = "idna" },
11 | { name = "sniffio" },
12 | ]
13 | sdist = { url = "https://files.pythonhosted.org/packages/95/7d/4c1bd541d4dffa1b52bd83fb8527089e097a106fc90b467a7313b105f840/anyio-4.9.0.tar.gz", hash = "sha256:673c0c244e15788651a4ff38710fea9675823028a6f08a5eda409e0c9840a028", size = 190949 }
14 | wheels = [
15 | { url = "https://files.pythonhosted.org/packages/a1/ee/48ca1a7c89ffec8b6a0c5d02b89c305671d5ffd8d3c94acf8b8c408575bb/anyio-4.9.0-py3-none-any.whl", hash = "sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c", size = 100916 },
16 | ]
17 |
18 | [[package]]
19 | name = "brotli"
20 | version = "1.1.0"
21 | source = { registry = "https://pypi.org/simple" }
22 | sdist = { url = "https://files.pythonhosted.org/packages/2f/c2/f9e977608bdf958650638c3f1e28f85a1b075f075ebbe77db8555463787b/Brotli-1.1.0.tar.gz", hash = "sha256:81de08ac11bcb85841e440c13611c00b67d3bf82698314928d0b676362546724", size = 7372270 }
23 | wheels = [
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36 | ]
37 |
38 | [[package]]
39 | name = "brotlicffi"
40 | version = "1.1.0.0"
41 | source = { registry = "https://pypi.org/simple" }
42 | dependencies = [
43 | { name = "cffi" },
44 | ]
45 | sdist = { url = "https://files.pythonhosted.org/packages/95/9d/70caa61192f570fcf0352766331b735afa931b4c6bc9a348a0925cc13288/brotlicffi-1.1.0.0.tar.gz", hash = "sha256:b77827a689905143f87915310b93b273ab17888fd43ef350d4832c4a71083c13", size = 465192 }
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