Skip to content

fix: division by zero in RSI calculation and README formatting #226

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
hazeone merged 3 commits into from
Oct 29, 2025
Merged

fix: division by zero in RSI calculation and README formatting #226

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
f133178
docs: fix missing bullet point in Traditional Chinese README
Mirza-Samad-Ahmed-Baig Oct 28, 2025
f50496d
fix: prevent division by zero in RSI calculation
Mirza-Samad-Ahmed-Baig Oct 28, 2025
325a077
style: format code to fix style checks
Mirza-Samad-Ahmed-Baig Oct 28, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
4 changes: 2 additions & 2 deletions README.zh_Hant.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -63,9 +63,9 @@ ValueCell 是一個社群驅動的多智能體金融應用平台。


## 多智能體系統
- **DeepResearch Agent**：獲取並分析股票的 SEC 文件，輸出準確的數據與可解釋的總結
- **DeepResearch Agent**：獲取並分析股票的 SEC 文件,輸出準確的數據與可解釋的總結
- **Auto Trading Agent**：支援多種加密資產與 AI 自動交易策略
**Trading Agents**: 專責市場分析、情緒分析、新聞分析與基本面分析的智能體協同運作
- **Trading Agents**: 專責市場分析、情緒分析、新聞分析與基本面分析的智能體協同運作
- **AI-Hedge-Fund**：智能體協作提供全面的金融洞見
- **其他智能體**：更多智能體正在規劃中…

Expand Down
3 changes: 2 additions & 1 deletion python/valuecell/agents/auto_trading_agent/market_data.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -111,7 +111,8 @@ def _calculate_rsi(df: pd.DataFrame, period: int = 14):
delta = df["Close"].diff()
gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
rs = gain / loss
# Avoid division by zero: if loss is 0, RSI = 100 (maximum strength)
rs = gain / loss.replace(0, float("inf"))
df["rsi"] = 100 - (100 / (1 + rs))

@staticmethod
Expand Down