Ashare (Open-source Minimalist Real-time A-Share Market Data API)

🛠 Tools and Programming
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Official Introduction

Real-time stock market data API for Chinese A-shares, with the simplest encapsulation. Includes daily lines, intraday minute-level data, all formatted into DataFrame, suitable for research, quantitative analysis, and automated trading systems.

The market data system integrates dual-core support from Sina Finance and Tencent Finance, with automatic failover between them. This greatly reduces the workload for quantitative researchers, allowing greater focus on strategy and model development.

Features

  • Lightweight core library: The entire project consists of just one file, Ashare.py. No installation or configuration required. You can freely customize it and use it instantly via from Ashare import *.

  • Dual-engine encapsulation: Provides real-time market data from both Sina Finance and Tencent Finance, including any historical daily, weekly, monthly, minute, and hourly data. Has been running stably for years.

  • Primary + backup dual-core design: Automatic hot standby and seamless switching. Suitable even as a live trading data source in quantitative systems.

  • All data cleaned and standardized into DataFrame format, making it extremely convenient to analyze and process using pandas.

  • What advantages does Ashare have over other market data libraries (e.g., tushare)? — Simplicity, lightweight, portability, and open-source transparency.

  • Complex data fetching, splitting, and integration logic is fully encapsulated into a single function: get_price(). Just review the examples below and you’ll understand how easy it is.

  • Can be used in any scenario requiring quantitative research or analysis.

First, a Simple Example Demo1.py

from Ashare import *

# Stock codes support multiple formats: Tongdaxin, Tonghuashun, JoinQuant
# sh000001 (000001.XSHG)    sz399006 (399006.XSHE)   sh600519 (600519.XSHG)

df = get_price('sh000001', frequency='1d', count=5)      # Default: get last 5 days of daily data up to today
print('SSE Index Daily Data\n', df)

df = get_price('000001.XSHG', frequency='1d', count=5, end_date='2021-04-30')  # Specify end date for historical data
print('SSE Index Historical Data\n', df)

df = get_price('000001.XSHG', frequency='1w', count=5, end_date='2018-06-15')  # Supports '1d' (daily), '1w' (weekly), '1M' (monthly)
print('SSE Index Weekly Historical Data\n', df)

df = get_price('sh600519', frequency='15m', count=5)     # Intraday minute data: supports '1m','5m','15m','30m','60m'
print('Kweichow Moutai 15-Minute Data\n', df)

df = get_price('600519.XSHG', frequency='60m', count=6)  # Minute-level real-time data
print('Kweichow Moutai 60-Minute Data\n', df)

# SSE Index Daily Data ----------------------------------------------------
           open    close     high      low       volume
2021-06-07  3597.14  3599.54  3600.38  3581.90  303718677.0
2021-06-08  3598.75  3580.11  3621.52  3563.25  304491470.0
2021-06-09  3576.80  3591.40  3598.71  3572.64  298323296.0
2021-06-10  3587.53  3610.86  3624.34  3584.13  318174808.0
2021-06-11  3614.11  3589.75  3614.40  3587.15  360554970.0


# Kweichow Moutai 60-Minute Data ----------------------------------------------------
                    open    close     high      low    volume
2021-06-10 14:00:00  2237.00  2224.16  2245.00  2222.00   4541.53
2021-06-10 15:00:00  2222.21  2238.48  2240.34  2222.21   4146.88
2021-06-11 10:30:00  2239.00  2220.00  2244.00  2197.86  12030.00
2021-06-11 11:30:00  2220.01  2210.18  2231.80  2200.18   4868.00
2021-06-11 14:00:00  2210.10  2223.35  2224.48  2206.01   4544.00
2021-06-11 15:00:00  2223.33  2178.81  2226.80  2178.81  12529.00

Another Example Using MyTT Demo2.py

# Stock Market Data Fetching and Visualization - 2
from Ashare import *          # Stock data library    https://github.com/mpquant/Ashare
from MyTT import *            # MyTT technical indicator library (based on Tongdaxin/Mai language)  https://github.com/mpquant/MyTT

# Multiple code formats supported: Tongdaxin, Tonghuashun, JoinQuant
# sh000001 (000001.XSHG)    sz399006 (399006.XSHE)   sh600519 (600519.XSHG)

df = get_price('000001.XSHG', frequency='1d', count=120)      # Get last 120 days of daily data
print('SSE Index Daily Data\n', df.tail(5))

# ------- Now that we have the data, let's proceed -------
CLOSE = df.close.values;         OPEN = df.open.values           # Define base data sequences (any sequence input works)
HIGH = df.high.values;           LOW = df.low.values             # e.g., CLOSE=list(df.close) would work the same

MA5 = MA(CLOSE, 5)                                # 5-day moving average
MA10 = MA(CLOSE, 10)                              # 10-day moving average
up, mid, lower = BOLL(CLOSE)                      # Bollinger Bands

# ------------------------- Plotting -------------------------
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator

plt.figure(figsize=(15, 8))
plt.plot(CLOSE, label='SHZS')
plt.plot(up, label='UP')
plt.plot(mid, label='MID')
plt.plot(lower, label='LOW')
plt.plot(MA10, label='MA10', linewidth=0.5, alpha=0.7)
plt.legend()
plt.show()
boll

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