快速开始¶
欢迎使用 AKQuant！让我们用最快的速度运行一个最简单的策略。
1. 安装¶
打开终端，运行：
2. 极简示例¶
这个策略非常简单：
- 当收盘价 > 开盘价 (阳线) -> 买入
- 当收盘价 < 开盘价 (阴线) -> 卖出
import akshare as ak
from akquant import Strategy, run_backtest
# 1. 准备数据 (这里利用 AKShare 来获取数据，需要通过 pip install akshare 来进行安装)
df = ak.stock_zh_a_daily(symbol="sh600000", start_date="20250101", end_date="20260212")
# 2. 策略定义
class MyStrategy(Strategy):
# 3. 策略逻辑
def on_bar(self, bar):
# 获取当前持仓
current_pos = self.get_position(bar.symbol)
# 1. 当收盘价 > 开盘价 (阳线) -> 买入
if current_pos == 0 and bar.close > bar.open:
self.buy(bar.symbol, 100) # 买入 100 股
# 2. 当收盘价 < 开盘价 (阴线) -> 卖出
elif current_pos > 0 and bar.close < bar.open:
self.close_position(bar.symbol) # 卖出所有持仓
# 3. 运行回测
print("开始回测...")
result = run_backtest(
data=df, # 输入数据
strategy=MyStrategy, # 输入策略
initial_cash=100000.0, # 初始资金
symbol="sh600000" # 交易的股票代码
)
# 4. 查看结果
print(result) # 跟 print(result.metrics_df) 等效
运行结果中，你会看到该策略的完整绩效评价指标：
开始回测...
2026-02-12 00:58:53 | INFO | Running backtest via run_backtest()...
BacktestResult:
Value
name
start_time 2025-01-02 00:00:00+08:00
end_time 2026-02-11 00:00:00+08:00
duration 405 days, 0:00:00
total_bars 271
trade_count 67.0
initial_market_value 100000.0
end_market_value 99100.68204
total_pnl -188.0
unrealized_pnl 0.0
total_return_pct -0.899318
annualized_return -0.008109
volatility 0.002453
total_profit 584.0
total_loss -772.0
total_commission 711.31796
max_drawdown 913.30785
max_drawdown_pct 0.91318
win_rate 26.865672
loss_rate 73.134328
winning_trades 18.0
losing_trades 49.0
avg_pnl -2.80597
avg_return_pct -0.172318
avg_trade_bars 2.014925
avg_profit 32.444444
avg_profit_pct 2.818291
avg_winning_trade_bars 4.055556
avg_loss -15.755102
avg_loss_pct -1.270909
avg_losing_trade_bars 1.265306
largest_win 120.0
largest_win_pct 10.178117
largest_win_bars 7.0
largest_loss -70.0
largest_loss_pct -5.380477
largest_loss_bars 1.0
max_wins 2.0
max_losses 9.0
sharpe_ratio -3.305093
sortino_ratio -3.92213
profit_factor 0.756477
ulcer_index 0.004666
upi -1.737695
equity_r2 0.932224
std_error 70.552942
calmar_ratio -0.887949
exposure_time_pct 49.815498
var_95 -0.000281
var_99 -0.000625
cvar_95 -0.000434
cvar_99 -0.00071
sqn -0.708177
kelly_criterion -0.086485
可以通过 print(result.positions_df) 来查看详细的持仓指标。
long_shares short_shares close equity market_value margin \
0 100.0 0.0 10.27 1027.0 1027.0 1027.0
1 100.0 0.0 10.30 1030.0 1030.0 1030.0
2 100.0 0.0 10.19 1019.0 1019.0 1019.0
3 100.0 0.0 10.21 1021.0 1021.0 1021.0
4 100.0 0.0 10.21 1021.0 1021.0 1021.0
.. ... ... ... ... ... ...
130 100.0 0.0 11.03 1103.0 1103.0 1103.0
131 100.0 0.0 10.04 1004.0 1004.0 1004.0
132 100.0 0.0 10.23 1023.0 1023.0 1023.0
133 100.0 0.0 10.12 1012.0 1012.0 1012.0
134 100.0 0.0 10.18 1018.0 1018.0 1018.0
unrealized_pnl symbol date
0 16.0 sh600000 2025-01-07 00:00:00+08:00
1 19.0 sh600000 2025-01-08 00:00:00+08:00
2 8.0 sh600000 2025-01-09 00:00:00+08:00
3 5.0 sh600000 2025-01-15 00:00:00+08:00
4 5.0 sh600000 2025-01-16 00:00:00+08:00
.. ... ... ...
130 -7.0 sh600000 2026-01-20 00:00:00+08:00
131 -10.0 sh600000 2026-01-30 00:00:00+08:00
132 8.0 sh600000 2026-02-05 00:00:00+08:00
133 -3.0 sh600000 2026-02-06 00:00:00+08:00
134 -1.0 sh600000 2026-02-10 00:00:00+08:00
[135 rows x 9 columns]
可以通过 print(result.orders_df) 来查看详细的订单指标。
id symbol side order_type \
0 fe400570-5971-4307-afff-13d47b627148 sh600000 buy market
1 111b9549-e363-439d-a79e-85c7e9f70295 sh600000 sell market
2 27967cc5-7e67-4f4a-af9e-78263d25a6e8 sh600000 buy market
3 c69e6a33-f157-464f-8dba-237abafa1dde sh600000 sell market
4 1e3ed424-eebc-49bd-ae9d-62b2cb779347 sh600000 buy market
.. ... ... ... ...
129 f4d338e6-ef74-4658-868a-f19f0e57c3b9 sh600000 sell market
130 419e9504-c6bf-4708-9b62-f9cebac62876 sh600000 buy market
131 0f682e75-fb18-4336-9b40-c821c385ec70 sh600000 sell market
132 52f97d2d-741e-4511-b104-81e99123f870 sh600000 buy market
133 95408a55-9b3d-4620-a752-f74b3272db25 sh600000 sell market
quantity filled_quantity limit_price stop_price avg_price \
0 100.0 100.0 NaN NaN 10.11
1 100.0 100.0 NaN NaN 10.23
2 100.0 100.0 NaN NaN 10.16
3 100.0 100.0 NaN NaN 10.25
4 100.0 100.0 NaN NaN 10.32
.. ... ... ... ... ...
129 100.0 100.0 NaN NaN 10.07
130 100.0 100.0 NaN NaN 10.15
131 100.0 100.0 NaN NaN 10.11
132 100.0 100.0 NaN NaN 10.19
133 100.0 100.0 NaN NaN 10.18
commission status time_in_force created_at
0 5.01011 filled gtc 2025-01-06 00:00:00+08:00
1 5.52173 filled gtc 2025-01-09 00:00:00+08:00
2 5.01016 filled gtc 2025-01-14 00:00:00+08:00
3 5.52275 filled gtc 2025-01-16 00:00:00+08:00
4 5.01032 filled gtc 2025-01-17 00:00:00+08:00
.. ... ... ... ...
129 5.51357 filled gtc 2026-01-30 00:00:00+08:00
130 5.01015 filled gtc 2026-02-04 00:00:00+08:00
131 5.51561 filled gtc 2026-02-06 00:00:00+08:00
132 5.01019 filled gtc 2026-02-09 00:00:00+08:00
133 5.51918 filled gtc 2026-02-10 00:00:00+08:00
[134 rows x 13 columns]
可以通过 print(result.trades_df) 来查看详细的成交指标。
symbol entry_time exit_time entry_price \
0 sh600000 2025-01-07 00:00:00+08:00 2025-01-10 00:00:00+08:00 10.11
1 sh600000 2025-01-15 00:00:00+08:00 2025-01-17 00:00:00+08:00 10.16
2 sh600000 2025-01-20 00:00:00+08:00 2025-01-23 00:00:00+08:00 10.32
3 sh600000 2025-01-24 00:00:00+08:00 2025-02-06 00:00:00+08:00 10.26
4 sh600000 2025-02-07 00:00:00+08:00 2025-02-10 00:00:00+08:00 10.43
.. ... ... ... ...
62 sh600000 2026-01-13 00:00:00+08:00 2026-01-14 00:00:00+08:00 11.72
63 sh600000 2026-01-20 00:00:00+08:00 2026-01-21 00:00:00+08:00 11.10
64 sh600000 2026-01-30 00:00:00+08:00 2026-02-02 00:00:00+08:00 10.14
65 sh600000 2026-02-05 00:00:00+08:00 2026-02-09 00:00:00+08:00 10.15
66 sh600000 2026-02-10 00:00:00+08:00 2026-02-11 00:00:00+08:00 10.19
exit_price quantity side pnl net_pnl return_pct commission \
0 10.23 100.0 Long 12.0 1.46816 1.186944 10.53184
1 10.25 100.0 Long 9.0 -1.53291 0.885827 10.53291
2 10.11 100.0 Long -21.0 -31.52593 -2.034884 10.52593
3 10.38 100.0 Long 12.0 1.46036 1.169591 10.53964
4 10.32 100.0 Long -11.0 -21.53675 -1.054650 10.53675
.. ... ... ... ... ... ... ...
62 11.61 100.0 Long -11.0 -21.60383 -0.938567 10.60383
63 11.03 100.0 Long -7.0 -17.57363 -0.630631 10.57363
64 10.07 100.0 Long -7.0 -17.52371 -0.690335 10.52371
65 10.11 100.0 Long -4.0 -14.52576 -0.394089 10.52576
66 10.18 100.0 Long -1.0 -11.52937 -0.098135 10.52937
duration_bars duration
0 3 3 days
1 2 2 days
2 3 3 days
3 3 13 days
4 1 3 days
.. ... ...
62 1 1 days
63 1 1 days
64 1 3 days
65 2 4 days
66 1 1 days
[67 rows x 13 columns]
3. 进阶学习¶
刚才的例子太简单了？想要学习如何编写真正的量化策略（如双均线、MACD 等）？
👉 请阅读 手把手教程：编写你的第一个量化策略
该教程将详细讲解：
- 如何获取历史数据 (
get_history) - 如何计算技术指标 (MA, RSI)
- 如何止盈止损