akquant

from akquant import run_backtest
 
result = run_backtest(
    strategy=MyStrategy,
    data=df,
    symbol="600000",
    initial_cash=500_000.0,
    commission_rate=0.0003,
    stamp_tax_rate=0.001,
    t_plus_one=True,  # A 股 T+1 规则
    warmup_period=20,
    execution_mode="NextOpen",
)

from akquant.config import RiskConfig
 
result = run_backtest(
    ...,
    risk_config=RiskConfig(
        max_position_pct=0.10,  # 单标的持仓不超过 10%
        max_account_drawdown=0.20,  # 最大回撤 20%
        max_daily_loss=0.05,  # 单日亏损 5%
    ),
)

from akquant import run_grid_search, run_walk_forward
 
# 网格搜索
results = run_grid_search(
    strategy=MyStrategy,
    param_grid={"param1": [10, 20, 30]},
    data=df,
    sort_by="sharpe_ratio",
)
 
# 滚动优化（推荐）
wfo_results = run_walk_forward(
    strategy=MyStrategy,
    param_grid={"param1": [10, 20, 30]},
    data=df,
    train_period=250,
    test_period=60,
    metric="sharpe_ratio",
)

class CrossSectionStrategy(Strategy):
    def __init__(self):
        self.universe = ["sh600519", "sz000858", "sh601318"]
 
    def on_start(self):
        self.add_daily_timer("14:55:00", "rebalance")
 
    def on_timer(self, payload):
        if payload != "rebalance":
            return
 
        # 计算所有标分数
        scores = {}
        for symbol in self.universe:
            history = self.get_history(20, symbol, "close")
            scores[symbol] = (history[-1] - history[0]) / history[0]
 
        # 选出最佳标的并调仓
        best = max(scores, key=scores.get)
        self.order_target_percent(0.95, symbol=best)

# macOS
brew install uv
 
# Linux
curl -LsSf https://astral.sh/uv/install.sh | sh
 
# Windows (PowerShell)
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

# 创建项目目录
mkdir my-quant-strategy
cd my-quant-strategy
 
# 初始化项目（创建 pyproject.toml）
uv init
 
# 创建虚拟环境并安装依赖
uv venv
uv add akquant pandas numpy

# 方式一：使用 uv run（推荐）
uv run python my_strategy.py
 
# 方式二：激活虚拟环境后运行
source .venv/bin/activate  # macOS/Linux
# .venv\Scripts\activate   # Windows
python my_strategy.py

# 安装精确版本（从 lock 文件）
uv sync
 
# 添加新依赖
uv add scipy  # 自动更新 lock 文件

my-quant-strategy/
├── .venv/              # 虚拟环境（uv 自动创建）
├── pyproject.toml      # 项目配置
├── uv.lock             # 依赖锁定文件
├── strategies/         # 策略脚本
│   ├── ma_strategy.py
│   └── cross_section.py
└── data/               # 数据文件
    └── stock_data.csv

# 一键创建并运行策略项目
mkdir quant-project && cd quant-project
uv init
uv venv
uv add akquant pandas numpy
 
# 创建策略文件（使用模板）
cat > strategy.py << 'EOF'
from akquant import Strategy, Bar, run_backtest
import pandas as pd
import numpy as np
 
class MyStrategy(Strategy):
    warmup_period = 20
 
    def on_bar(self, bar: Bar):
        closes = self.get_history(20, bar.symbol, "close")
        if len(closes) < 20:
            return
        ma = np.mean(closes)
        pos = self.get_position(bar.symbol)
        if bar.close > ma and pos == 0:
            self.buy(bar.symbol, 100)
        elif bar.close < ma and pos > 0:
            self.sell(bar.symbol, pos)
 
# 准备数据并运行回测
# result = run_backtest(strategy=MyStrategy, data=df, symbol="600000")
EOF
 
# 运行策略
uv run python strategy.py

from akquant import Strategy, Bar
import numpy as np
 
class DualMAStrategy(Strategy):
    warmup_period = 30
 
    def __init__(self, fast=10, slow=20):
        self.fast = fast
        self.slow = slow
        self.warmup_period = slow + 1
 
    def on_bar(self, bar: Bar):
        fast_ma = np.mean(self.get_history(self.fast, bar.symbol, "close"))
        slow_ma = np.mean(self.get_history(self.slow, bar.symbol, "close"))
 
        pos = self.get_position(bar.symbol)
        if fast_ma > slow_ma and pos == 0:
            self.buy(bar.symbol, 100)
        elif fast_ma < slow_ma and pos > 0:
            self.sell(bar.symbol, pos)

from akquant import Strategy, Bar, run_backtest
from akquant.config import RiskConfig
import numpy as np
 
class TrendStrategy(Strategy):
    warmup_period = 20
 
    def __init__(self, ma_window=20, stop_loss=0.05):
        self.ma_window = ma_window
        self.stop_loss = stop_loss
 
    def on_bar(self, bar: Bar):
        ma = np.mean(self.get_history(self.ma_window, bar.symbol, "close"))
        pos = self.get_position(bar.symbol)
 
        if bar.close > ma * 1.02 and pos == 0:
            self.buy(bar.symbol, 100)
        elif bar.close < ma * 0.98 and pos > 0:
            self.sell(bar.symbol, pos)
 
# 运行回测
result = run_backtest(
    strategy=TrendStrategy,
    data=df,
    symbol="600000",
    initial_cash=1_000_000.0,
    risk_config=RiskConfig(
        max_position_pct=0.20,
        max_account_drawdown=0.15,
        stop_loss_threshold=0.85,
    ),
)

from akquant import Strategy, run_backtest
import numpy as np
 
class MomentumRotation(Strategy):
    def __init__(self, lookback=20):
        self.lookback = lookback
        self.universe = ["sh600519", "sz000858", "sh601318"]
        self.warmup_period = lookback + 1
 
    def on_start(self):
        for symbol in self.universe:
            self.subscribe(symbol)
        self.add_daily_timer("14:55:00", "rebalance")
 
    def on_timer(self, payload):
        if payload != "rebalance":
            return
 
        scores = {}
        for symbol in self.universe:
            closes = self.get_history(self.lookback, symbol, "close")
            if len(closes) < self.lookback:
                return
            scores[symbol] = (closes[-1] - closes[0]) / closes[0]
 
        # 选出最佳标的，持仓 95%
        best = max(scores, key=scores.get)
        self.order_target_percent(0.95, symbol=best)