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Add Adanos Finance Sentiment API research notebook #833

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2 changes: 1 addition & 1 deletion docs/docs/getting-started/resources.md
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Expand Up @@ -17,6 +17,7 @@ Here's a collection of resources to get started.
- [Running Telegram signal bot](https://nbviewer.jupyter.org/github/polakowo/vectorbt/blob/master/examples/TelegramSignals.ipynb)
- [Porting RSI strategy from backtrader](https://nbviewer.jupyter.org/github/polakowo/vectorbt/blob/master/examples/PortingBTStrategy.ipynb)
- [Pairs trading (vs backtrader)](https://nbviewer.jupyter.org/github/polakowo/vectorbt/blob/master/examples/PairsTrading.ipynb)
- [Researching external stock sentiment with Adanos](https://nbviewer.jupyter.org/github/polakowo/vectorbt/blob/master/examples/AdanosSentimentResearch.ipynb)

Note: you must run the notebook to play with the widgets.

Expand Down Expand Up @@ -48,4 +49,3 @@ Note: you must run the notebook to play with the widgets.
- For questions on Numba and other parts, the best place to go to is [StackOverflow](https://stackoverflow.com/)
- If you have general questions, start a new [GitHub Discussion](https://github.com/polakowo/vectorbt/discussions)
- If you found what appears to be a bug, please [create a new issue](https://github.com/polakowo/vectorbt/issues)

192 changes: 192 additions & 0 deletions examples/AdanosSentimentResearch.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Researching external stock sentiment with Adanos and vectorbt\n",
"\n",
"This notebook shows how to use the Adanos Finance Sentiment API as an external confirmation layer in a vectorbt research workflow.\n",
"\n",
"We will:\n",
"\n",
"- fetch source-level sentiment for a basket of stocks\n",
"- build simple composites such as `average_buzz` and `bullish_avg`\n",
"- combine those values with a price trend filter from vectorbt\n",
"- rank candidates for deeper analysis\n",
"\n",
"Replace `ADANOS_API_KEY` with your own key before running the notebook.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import requests\n",
"import pandas as pd\n",
"import vectorbt as vbt\n",
"\n",
"ADANOS_API_KEY = \"YOUR_ADANOS_API_KEY\"\n",
"TICKERS = [\"TSLA\", \"NVDA\", \"AAPL\", \"MSFT\"]\n",
"LOOKBACK_DAYS = 7\n",
"\n",
"ADANOS_ENDPOINTS = {\n",
" \"reddit\": \"https://api.adanos.org/reddit/stocks/v1/stock/{ticker}\",\n",
" \"x\": \"https://api.adanos.org/x/stocks/v1/stock/{ticker}\",\n",
" \"news\": \"https://api.adanos.org/news/stocks/v1/stock/{ticker}\",\n",
" \"polymarket\": \"https://api.adanos.org/polymarket/stocks/v1/stock/{ticker}\",\n",
"}\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def classify_alignment(bullish_values):\n",
" spread = max(bullish_values) - min(bullish_values)\n",
" if spread <= 15:\n",
" return \"Aligned\"\n",
" if spread <= 30:\n",
" return \"Mixed\"\n",
" return \"Wide divergence\"\n",
"\n",
"\n",
"def fetch_source_signal(session, source, ticker):\n",
" response = session.get(\n",
" ADANOS_ENDPOINTS[source].format(ticker=ticker),\n",
" params={\"days\": LOOKBACK_DAYS},\n",
" timeout=15,\n",
" )\n",
" response.raise_for_status()\n",
" payload = response.json()\n",
"\n",
" volume = payload.get(\"trade_count\")\n",
" if volume is None:\n",
" volume = payload.get(\"mentions\", 0)\n",
"\n",
" return {\n",
" \"source\": source,\n",
" \"buzz\": float(payload.get(\"buzz_score\", 0.0)),\n",
" \"bullish_pct\": float(payload.get(\"bullish_pct\", 0.0)),\n",
" \"trend\": str(payload.get(\"trend\", \"stable\")).lower(),\n",
" \"volume\": int(volume or 0),\n",
" }\n",
"\n",
"\n",
"def build_sentiment_row(ticker):\n",
" session = requests.Session()\n",
" session.headers[\"X-API-Key\"] = ADANOS_API_KEY\n",
"\n",
" signals = []\n",
" for source in ADANOS_ENDPOINTS:\n",
" try:\n",
" signals.append(fetch_source_signal(session, source, ticker))\n",
" except requests.RequestException as exc:\n",
" print(f\"Skipping {ticker} {source}: {exc}\")\n",
"\n",
" if not signals:\n",
" return None\n",
"\n",
" buzz_values = [signal[\"buzz\"] for signal in signals]\n",
" bullish_values = [signal[\"bullish_pct\"] for signal in signals]\n",
" trends = [signal[\"trend\"] for signal in signals]\n",
"\n",
" return {\n",
" \"ticker\": ticker,\n",
" \"coverage\": len(signals),\n",
" \"average_buzz\": round(sum(buzz_values) / len(buzz_values), 1),\n",
" \"bullish_avg\": round(sum(bullish_values) / len(bullish_values), 1),\n",
" \"rising_sources\": sum(1 for trend in trends if trend == \"rising\"),\n",
" \"falling_sources\": sum(1 for trend in trends if trend == \"falling\"),\n",
" \"source_alignment\": classify_alignment(bullish_values),\n",
" }\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"rows = [build_sentiment_row(ticker) for ticker in TICKERS]\n",
"sentiment_df = pd.DataFrame([row for row in rows if row is not None]).set_index(\"ticker\")\n",
"sentiment_df.sort_values([\"average_buzz\", \"bullish_avg\"], ascending=False)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"close = vbt.YFData.download(sentiment_df.index.tolist(), start=\"2024-01-01\").get(\"Close\")\n",
"ma50 = vbt.MA.run(close, 50).ma\n",
"\n",
"price_snapshot = pd.DataFrame(\n",
" {\n",
" \"close\": close.iloc[-1],\n",
" \"ma50\": ma50.iloc[-1],\n",
" \"one_month_return_pct\": ((close.iloc[-1] / close.iloc[-21]) - 1).round(4) * 100,\n",
" }\n",
")\n",
"price_snapshot[\"above_50dma\"] = price_snapshot[\"close\"] > price_snapshot[\"ma50\"]\n",
"price_snapshot\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"research_df = sentiment_df.join(price_snapshot[[\"above_50dma\", \"one_month_return_pct\"]])\n",
"research_df = research_df.sort_values(\n",
" [\"above_50dma\", \"average_buzz\", \"bullish_avg\"],\n",
" ascending=[False, False, False],\n",
")\n",
"research_df\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How to use this output\n",
"\n",
"A simple workflow is:\n",
"\n",
"- focus first on symbols with `above_50dma = True`\n",
"- prefer higher `average_buzz` and `bullish_avg`\n",
"- be careful with `Wide divergence` across sources\n",
"- treat this as a ranking layer before deeper backtests or live strategy changes\n",
"\n",
"This keeps Adanos in the role it fits best inside vectorbt: an external research input that helps prioritize which setups deserve attention.\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
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},
"nbformat": 4,
"nbformat_minor": 2
}