mirror of
https://github.com/kennethreitz/coinbin.org.git
synced 2026-07-22 05:09:31 +00:00
Claude
678722d4d4
Brings the long-dead service back to a bootable state. Addresses the modernization epic (#30) and its work items: - #31 scraper: replace the removed CoinMarketCap `views/all` HTML scrape with a JSON price API (CoinGecko by default, no key required). Keeps the get_coins() return shape and MWT cache; handles non-unique symbols by keeping the highest-market-cap entry. - #32 runtime: drop the removed `gaiohttp` gunicorn worker / `aiohttp<2.0`; Procfile now uses `gthread`. - #33 deps/runtime: migrate off unmaintained libs — flask-cache -> flask-caching, drop flask-common, graphene 1/2 -> graphene 3, raven -> sentry-sdk, Python 3.6 -> 3.11+. Replace flask-graphql (incompatible with graphene 3) with a direct schema.execute() view. Add a JSON provider so Flask 3 can serialize Decimal. Make DB/forecast imports lazy so the app boots without them; only force HTTPS outside DEBUG. - #34 ingestion: add ingest.py worker and committed schema.sql for the api_coin price-history table. - #35 forecasting: migrate fbprophet -> prophet behind a FORECASTS_ENABLED flag with lazy, optional imports. Dependency management moved to uv: pyproject.toml + uv.lock, replacing the 2017-era Pipfile/Pipfile.lock. README and app.json updated. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01UMDxE5JVcuzpKyiHercWaa
108 lines
3.3 KiB
Python
108 lines
3.3 KiB
Python
import os
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from scraper import Coin, MWT, convert_to_decimal
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PERIODS = 30
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GRAPH_PERIODS = 365
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# Forecasting pulls in a heavy, hard-to-build stack (prophet + cmdstanpy,
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# pandas, numpy, matplotlib, mpld3) and needs the price-history database to be
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# populated, so it is opt-in. Enable with FORECASTS_ENABLED=1 once those
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# dependencies are installed (see the [forecast] extras in the Pipfile).
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FORECASTS_ENABLED = os.environ.get('FORECASTS_ENABLED', '').lower() in ('1', 'true', 'yes')
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_DISABLED_MESSAGE = {
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'enabled': False,
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'message': 'Forecasting is currently disabled on this instance.',
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}
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def _build_predictions(coin, render=False):
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"""The real forecasting routine; only imported when enabled."""
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import records
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import maya
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import numpy as np
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# Matplotlib must pick a headless backend before pyplot is imported.
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import matplotlib
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matplotlib.use('agg')
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import mpld3
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from prophet import Prophet
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c = Coin(coin)
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q = "SELECT date as ds, value as y from api_coin WHERE name=:coin"
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db = records.Database()
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rows = db.query(q, coin=c.name)
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df = rows.export('df')
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df['y_orig'] = df['y'] # to save a copy of the original data..you'll see why shortly.
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# log-transform y
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df['y'] = np.log(df['y'])
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model = Prophet(weekly_seasonality=True, yearly_seasonality=True)
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model.fit(df)
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periods = PERIODS if not render else GRAPH_PERIODS
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future_data = model.make_future_dataframe(periods=periods, freq='d')
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forecast_data = model.predict(future_data)
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if render:
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matplotlib.pyplot.gcf()
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fig = model.plot(forecast_data, xlabel='Date', ylabel='log($)')
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return mpld3.fig_to_html(fig)
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forecast_data_orig = forecast_data # make sure we save the original forecast data
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forecast_data_orig['yhat'] = np.exp(forecast_data_orig['yhat'])
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forecast_data_orig['yhat_lower'] = np.exp(forecast_data_orig['yhat_lower'])
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forecast_data_orig['yhat_upper'] = np.exp(forecast_data_orig['yhat_upper'])
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df['y_log'] = df['y'] # copy the log-transformed data to another column
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df['y'] = df['y_orig'] # copy the original data to 'y'
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d = forecast_data_orig['yhat'].to_dict()
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predictions = []
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for i, k in enumerate(list(d.keys())[-PERIODS:]):
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w = maya.when('{} days from now'.format(i + 1))
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predictions.append({
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'when': w.slang_time(),
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'timestamp': w.iso8601(),
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'usd': convert_to_decimal(d[k]),
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})
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return predictions
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@MWT(timeout=300)
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def get_predictions(coin, render=False):
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"""Returns a list of predictions, unless render is True.
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Otherwise, returns rendered HTML.
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When forecasting is disabled (the default) or its optional dependencies are
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missing, returns a small explanatory payload instead of raising.
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"""
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if not FORECASTS_ENABLED:
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if render:
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return '<p>Forecasting is currently disabled on this instance.</p>'
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return _DISABLED_MESSAGE
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try:
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return _build_predictions(coin, render=render)
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except ImportError:
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if render:
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return '<p>Forecasting dependencies are not installed.</p>'
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return {
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'enabled': False,
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'message': 'Forecasting dependencies are not installed.',
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}
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if __name__ == '__main__':
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print(get_predictions('btc'))
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