Day 1 — 环境安装，测试

1. 安装 Python 3.10+

2. 安装 VSCode + Python 插件

3. 新建一个文件夹：crypto_quant

4. 创建虚拟环境

   python -m venv env
   

5. 激活虚拟环境

   • Windows

     env\\Scripts\\activate
     

   • macOS

     source env/bin/activate
     

6. 安装必要库

   pip install numpy pandas matplotlib ccxt
   

7. 创建 test_ccxt.py

import ccxt
import pandas as pd

exchange = ccxt.binance()

ohlcv = exchange.fetch_ohlcv('BTC/USDT', timeframe='1h', limit=100)

df = pd.DataFrame(ohlcv, columns=['timestamp','open','high','low','close','volume'])
df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')

print(df.head())

1. 在指令行输入

   python test_ccxt.py
   

📅 Day 2 — Python 基础 + 手写 SMA 函数

# --- Python Basics (Day 2) ---

# 变量 & 类型
a = 10
b = 3.5
name = "crypto"
is_ok = True

print(a, b, name, is_ok)

# list
nums = [1, 2, 3, 4, 5]
print("nums:", nums)

# dict
person = {"name": "bebeca", "role": "quant learner"}
print("person:", person)

# for loop
for i in range(5):
    print("loop:", i)

# if
x = 7
if x > 5:
    print("x 大于 5")
else:
    print("x 不大于 5")

# 函数
def square(n):
    return n * n

print("square(5) =", square(5))

# --- 手写 SMA 函数 ---
def SMA(values, window):
    """
    values: list or array
    window: int
    """
    if len(values) < window:
        return None

    return sum(values[-window:]) / window

# 测试：
close_prices = [10, 11, 12, 13, 14, 15]
print("SMA(3):", SMA(close_prices, 3))

📅 Day 3 — Pandas 入门 + 载入 CSV

# --- Day 3: Pandas Basics ---

import pandas as pd

# 示例数据 DataFrame
df = pd.DataFrame({
    "open": [100, 102, 101, 105],
    "high": [103, 104, 102, 106],
    "low":  [99, 101, 100, 103],
    "close":[102, 101, 105, 104],
})

print("head:")
print(df.head())

print("\\ndescribe:")
print(df.describe())

# 取列
print("\\nclose column:")
print(df["close"])

# 简单图
df["close"].plot(title="Example Close Price")

请新建一个 Python 文件：generate_data.py

import pandas as pd
import numpy as np

# 1. 设置随机种子，保证你和我生成的“随机数”是一样的，方便对答案
np.random.seed(42)

# 2. 生成 100 个交易日期
dates = pd.date_range(start="2023-01-01", periods=100, freq="D")

# 3. 模拟股价波动 (随机游走)
# 假设初始价格 100，每天涨跌幅在 -2% 到 +2% 之间
returns = np.random.uniform(low=-0.02, high=0.02, size=100)
price_path = 100 * (1 + returns).cumprod() # 计算累积乘积得到价格路径

# 4. 基于收盘价，随机生成 High/Low/Open
# Open: 在 Close 附近波动
# High: 一定比 Open 和 Close 都高
# Low:  一定比 Open 和 Close 都低
data = {
    "close": price_path,
    "open":  price_path * (1 + np.random.uniform(-0.005, 0.005, 100)),
    "high":  price_path * (1 + np.random.uniform(0.005, 0.015, 100)),
    "low":   price_path * (1 - np.random.uniform(0.005, 0.015, 100)),
    "volume": np.random.randint(1000, 5000, 100) # 成交量 1000-5000 之间
}

# 5. 组装成 DataFrame
df = pd.DataFrame(data, index=dates)

# 6. 保存为 CSV 文件
df.to_csv("sample_kline.csv")

print("✅ 成功生成文件: sample_kline.csv")
print("前 3 行数据预览：")
print(df.head(3))