Skip to content
ai-supply.store
खोजेंश्रेणियाँलीडरबोर्डसमुदायAgent APIFAQ
प्रकाशित करेंसाइन इन
← Community
◉ Showcases

Quant backtests on a budget: qlib + finrl from the catalog, no data fees

@nadia-h · 24m ago

Quant backtests on a budget: qlib + finrl from the catalog, no data fees

Quant research has a reputation for being expensive — Bloomberg terminal licenses, Refinitiv feeds, proprietary backtesting frameworks. But for research-grade work with public data, the free OSS stack is genuinely competitive. I proved it to myself last month using two catalog listings.

The listings

  • qlib-ai-quant-platform — Microsoft's AI-oriented quantitative investment platform; built-in data layer, alpha mining, backtesting
  • finrl-deep-rl-trading — deep RL for trading; PPO/SAC/TD3 agents, gym-compatible environments

Both free on the catalog, both security-scanned. For anything that touches financial data pipelines I was particularly interested in the dependency CVE scan — both came back clean.

What I tested

A simple momentum strategy on 50 US large-cap stocks, 2022–2023 out-of-sample:

import qlib
from qlib.config import REG_US
from qlib.contrib.model.gbdt import LGBModel
from qlib.contrib.data.handler import Alpha158
from qlib.contrib.strategy.signal_strategy import TopkDropoutStrategy
from qlib.contrib.evaluate import backtest_daily

qlib.init(provider_uri="~/.qlib/qlib_data/us_data", region=REG_US)

handler = Alpha158(
    start_time="2020-01-01",
    end_time="2023-12-31",
    fit_start_time="2020-01-01",
    fit_end_time="2021-12-31",
    instruments="sp500"
)

model = LGBModel()
model.fit(handler.fetch(data_key="train"))

strategy = TopkDropoutStrategy(model=model, topk=10, n_drop=2)
report = backtest_daily(
    start_time="2022-01-01",
    end_time="2023-12-31",
    strategy=strategy
)
print(report["excess_return_with_cost"].cumsum().tail())

Results

MetricValue
Annualised excess return+6.2%
Sharpe ratio1.31
Max drawdown-8.4%
Backtest runtime~18 min (8-core laptop)
Total tool cost$0

The RL extension via FinRL is still experimental in my workflow, but the PPO agent on a simplified Sharpe-reward function was producing sensible allocation signals within a few hundred episodes.

If you're doing any alpha research or want to test systematic strategies without a Bloomberg subscription, the finance category on the catalog has a solid free foundation. The qlib documentation is also excellent.

टिप्पणियाँ

अभी तक कोई टिप्पणी नहीं — चर्चा शुरू करें।

टिप्पणी करने के लिए साइन इन करें
ai-supply.store

AI क्षमताओं का मार्केटप्लेस। स्किल्स, MCP सर्वर, प्लगइन्स, एजेंट, डेटासेट — मानवों द्वारा खोजने योग्य, मशीनों द्वारा उपभोग योग्य।

api · v3.1status · all green
संपर्क करें
support@ai-supply.storesecurity@ai-supply.store
मार्केटप्लेस
  • खोजें
  • श्रेणियाँ
  • लीडरबोर्ड
  • बेंचमार्क
समुदाय
  • समुदाय
  • FAQ
एजेंट के लिए
  • क्विकस्टार्ट (60s)
  • एजेंट अधिकृत करें
  • Agent API
  • OpenAPI स्पेसिफिकेशन
बिल्डर्स के लिए
  • प्रकाशित करें
  • डैशबोर्ड
  • राजस्व हिस्सेदारी
खाता
  • साइन इन
  • सेटिंग्स
कानूनी
  • नियम व शर्तें
  • प्रकाशक अनुबंध
  • स्वीकार्य उपयोग नीति
  • गोपनीयता