# Paired trading
> 來源：https://uqer.io/community/share/54895a8df9f06c31c3950ca0
##配對交易
策略思路
尋找走勢相關且股價相近的一對股票，根據其價格變動買賣
策略實現
歷史前五日的Pearson相關系數若大于給定的閾值則觸發買賣操作
```py
from scipy.stats.stats import pearsonr
start = datetime(2013, 1, 1)
end = datetime(2014, 12, 1)
benchmark = 'HS300'
universe = ['000559.XSHE', '600126.XSHG']
capital_base = 1e6
corlen = 5
def initialize(account):
add_history('hist', corlen)
account.cutoff = 0.9
account.prev_prc1 = 0
account.prev_prc2 = 0
account.prev_prcb = 0
def handle_data(account, data):
stk1 = universe[0]
stk2 = universe[1]
prc1 = data[stk1]['closePrice']
prc2 = data[stk2]['closePrice']
prcb = data['HS300']['return']
px1 = account.hist[stk1]['closePrice'].values
px2 = account.hist[stk2]['closePrice'].values
pxb = account.hist['HS300']['return'].values
corval, pval = pearsonr(px1, px2)
mov1, mov2 = adj(prc1, prc2, prcb, account.prev_prc1, account.prev_prc2, account.prev_prcb)
amount =1e4 / prc2
if (mov1 > 0) and (abs(corval) > account.cutoff):
order(stk2, amount)
elif (mov1 < 0) and (abs(corval) > account.cutoff):
if (account.position.stkpos.get(stk2, 0) > amount):
order(stk2, -amount)
else:
order_to(stk2, 0)
amount =1e4 / prc1
if (mov2 > 0) and (abs(corval) > account.cutoff):
order(stk1, amount)
elif (mov2 < 0) and (abs(corval) > account.cutoff):
if (account.position.stkpos.get(stk1, 0) > amount):
order(stk1, -amount)
else:
order_to(stk1, 0)
account.prev_prc1 = prc1
account.prev_prc2 = prc2
account.prev_prcb = prcb
def dmv(curr, prev):
delta = curr / prev - 1
return delta
def adj(x, y, base, prev_x, prev_y, prev_base):
dhs = dmv(base, prev_base)
dx = dmv(x, prev_x) - dhs
dy = dmv(y, prev_y) - dhs
return (dx, dy)
```
```py
min(bt.cash)
232096.85369499651
```
```py
import pandas as pd
import numpy as np
from datetime import datetime
import quartz
import quartz.backtest as qb
import quartz.performance as qp
from quartz.api import *
from scipy.stats.stats import pearsonr
start = datetime(2013, 1, 1) # 回測起始時間
end = datetime(2014, 12, 1) # 回測結束時間
benchmark = 'HS300' # 使用滬深 300 作為參考標準
capital_base = 1e6 # 起始資金
corlen = 5
def initialize(account): # 初始化虛擬賬戶狀態
add_history('hist', corlen)
account.cutoff = 0.9
account.prev_prc1 = 0
account.prev_prc2 = 0
account.prev_prcb = 0
def handle_data(account, data): # 每個交易日的買入賣出指令
stk1 = universe[0]
stk2 = universe[1]
prc1 = data[stk1]['closePrice']
prc2 = data[stk2]['closePrice']
prcb = data['HS300']['return']
px1 = account.hist[stk1]['closePrice'].values
px2 = account.hist[stk2]['closePrice'].values
pxb = account.hist['HS300']['return'].values
corval, pval = pearsonr(px1, px2)
mov1, mov2 = adj(prc1, prc2, prcb, account.prev_prc1, account.prev_prc2, account.prev_prcb)
#amount = int( 0.08 * capital_base / prc2)
amount =1e4 / prc2
if (mov1 > 0) and (abs(corval) > account.cutoff):
order(stk2, amount)
elif (mov1 < 0) and (abs(corval) > account.cutoff):
if (account.position.stkpos.get(stk2, 0) > amount):
order(stk2, -amount)
else:
order_to(stk2, 0)
#amount = int(0.08 * capital_base / prc1)
amount =1e4 / prc1
if (mov2 > 0) and (abs(corval) > account.cutoff):
order(stk1, amount)
elif (mov2 < 0) and (abs(corval) > account.cutoff):
if (account.position.stkpos.get(stk1, 0) > amount):
order(stk1, -amount)
else:
order_to(stk1, 0)
account.prev_prc1 = prc1
account.prev_prc2 = prc2
account.prev_prcb = prcb
def dmv(curr, prev):
delta = curr / prev - 1
return delta
def adj(x, y, base, prev_x, prev_y, prev_base):
dhs = dmv(base, prev_base)
dx = dmv(x, prev_x) - dhs
dy = dmv(y, prev_y) - dhs
return (dx, dy)
pool_raw = pd.read_csv("po.pair.2012.csv")
pool = []
for i in range(len(pool_raw)):
s1, s2 = pool_raw.loc[i].tolist()
if [s2, s1] not in pool:
pool.append([s1, s2])
outfile = []
for i, universe in enumerate(pool):
print i
try:
bt = qb.backtest(start, end, benchmark, universe, capital_base, initialize = initialize, handle_data = handle_data)
perf = qp.perf_parse(bt)
outfile.append(universe + [perf["annualized_return"], perf["sharpe"]])
except:
pass
keys = ['stock1', 'stock2', 'annualized_return', 'sharpe']
outdict = {}
outfile = zip(*sorted(outfile, key=lambda x:x[2], reverse=True))
for i,k in enumerate(keys):
outdict[k] = outfile[i]
outdict = pd.DataFrame(outdict).loc[:, keys]
outdict
['000066.XSHE', '000707.XSHE']
['000066.XSHE', '600117.XSHG']
['000066.XSHE', '600126.XSHG']
['000066.XSHE', '600819.XSHG']
['000089.XSHE', '600035.XSHG']
['000089.XSHE', '600037.XSHG']
['000089.XSHE', '600595.XSHG']
['000159.XSHE', '000967.XSHE']
['000159.XSHE', '600595.XSHG']
['000417.XSHE', '000541.XSHE']
['000417.XSHE', '000685.XSHE']
['000417.XSHE', '600875.XSHG']
['000425.XSHE', '000528.XSHE']
['000507.XSHE', '600391.XSHG']
['000541.XSHE', '000987.XSHE']
['000541.XSHE', '600330.XSHG']
['000541.XSHE', '600883.XSHG']
['000554.XSHE', '000707.XSHE']
['000559.XSHE', '600026.XSHG']
['000559.XSHE', '600126.XSHG']
['000559.XSHE', '600477.XSHG']
['000559.XSHE', '600581.XSHG']
['000559.XSHE', '601666.XSHG']
['000635.XSHE', '000707.XSHE']
['000635.XSHE', '600068.XSHG']
['000635.XSHE', '600117.XSHG']
['000635.XSHE', '600188.XSHG']
['000635.XSHE', '600295.XSHG']
['000635.XSHE', '600550.XSHG']
['000635.XSHE', '600819.XSHG']
['000635.XSHE', '601168.XSHG']
['000635.XSHE', '601233.XSHG']
['000650.XSHE', '600261.XSHG']
['000683.XSHE', '000936.XSHE']
['000683.XSHE', '600595.XSHG']
['000685.XSHE', '000988.XSHE']
['000685.XSHE', '601101.XSHG']
['000698.XSHE', '000949.XSHE']
['000707.XSHE', '000911.XSHE']
['000707.XSHE', '000969.XSHE']
['000707.XSHE', '000987.XSHE']
['000707.XSHE', '600117.XSHG']
['000707.XSHE', '600295.XSHG']
['000707.XSHE', '600550.XSHG']
['000707.XSHE', '600831.XSHG']
['000707.XSHE', '601168.XSHG']
['000707.XSHE', '601233.XSHG']
['000708.XSHE', '600327.XSHG']
['000709.XSHE', '601107.XSHG']
['000709.XSHE', '601618.XSHG']
['000717.XSHE', '600282.XSHG']
['000717.XSHE', '600307.XSHG']
['000717.XSHE', '600808.XSHG']
['000761.XSHE', '600320.XSHG']
['000761.XSHE', '600548.XSHG']
['000822.XSHE', '600117.XSHG']
['000830.XSHE', '600068.XSHG']
['000830.XSHE', '600320.XSHG']
['000830.XSHE', '600550.XSHG']
['000877.XSHE', '601519.XSHG']
['000898.XSHE', '600022.XSHG']
['000898.XSHE', '600808.XSHG']
['000911.XSHE', '600550.XSHG']
['000916.XSHE', '600033.XSHG']
['000916.XSHE', '600035.XSHG']
['000916.XSHE', '600126.XSHG']
['000930.XSHE', '600026.XSHG']
['000932.XSHE', '600569.XSHG']
['000933.XSHE', '600348.XSHG']
['000933.XSHE', '600595.XSHG']
['000936.XSHE', '600477.XSHG']
['000937.XSHE', '600348.XSHG']
['000937.XSHE', '600508.XSHG']
['000937.XSHE', '600997.XSHG']
['000937.XSHE', '601001.XSHG']
['000939.XSHE', '600819.XSHG']
['000967.XSHE', '600879.XSHG']
['000969.XSHE', '600831.XSHG']
['000973.XSHE', '600460.XSHG']
['000987.XSHE', '600636.XSHG']
['000987.XSHE', '600827.XSHG']
['000987.XSHE', '601001.XSHG']
['600008.XSHG', '600035.XSHG']
['600012.XSHG', '600428.XSHG']
['600020.XSHG', '600033.XSHG']
['600020.XSHG', '600035.XSHG']
['600026.XSHG', '600068.XSHG']
['600026.XSHG', '600089.XSHG']
['600026.XSHG', '600126.XSHG']
['600026.XSHG', '600307.XSHG']
['600026.XSHG', '600331.XSHG']
['600026.XSHG', '600375.XSHG']
['600026.XSHG', '600581.XSHG']
['600026.XSHG', '600963.XSHG']
['600026.XSHG', '601666.XSHG']
['600026.XSHG', '601898.XSHG']
['600033.XSHG', '600035.XSHG']
['600035.XSHG', '600126.XSHG']
['600035.XSHG', '600269.XSHG']
['600035.XSHG', '600307.XSHG']
['600035.XSHG', '600586.XSHG']
['600037.XSHG', '600327.XSHG']
['600068.XSHG', '600126.XSHG']
['600068.XSHG', '600269.XSHG']
['600068.XSHG', '600320.XSHG']
['600068.XSHG', '600550.XSHG']
['600068.XSHG', '601001.XSHG']
['600068.XSHG', '601666.XSHG']
['600089.XSHG', '600581.XSHG']
['600100.XSHG', '600117.XSHG']
['600117.XSHG', '600295.XSHG']
['600117.XSHG', '600339.XSHG']
['600117.XSHG', '601168.XSHG']
['600117.XSHG', '601233.XSHG']
['600126.XSHG', '600282.XSHG']
['600126.XSHG', '600327.XSHG']
['600126.XSHG', '600569.XSHG']
['600126.XSHG', '600581.XSHG']
['600126.XSHG', '600808.XSHG']
['600126.XSHG', '600963.XSHG']
['600160.XSHG', '600449.XSHG']
['600160.XSHG', '601216.XSHG']
['600160.XSHG', '601311.XSHG']
['600188.XSHG', '600295.XSHG']
['600188.XSHG', '601001.XSHG']
['600231.XSHG', '600282.XSHG']
['600269.XSHG', '601618.XSHG']
['600282.XSHG', '600307.XSHG']
['600282.XSHG', '600569.XSHG']
['600282.XSHG', '600808.XSHG']
['600282.XSHG', '600963.XSHG']
['600307.XSHG', '600581.XSHG']
['600307.XSHG', '600808.XSHG']
['600307.XSHG', '600963.XSHG']
['600320.XSHG', '600548.XSHG']
['600320.XSHG', '601600.XSHG']
['600330.XSHG', '600883.XSHG']
['600330.XSHG', '601268.XSHG']
['600331.XSHG', '600581.XSHG']
['600348.XSHG', '600508.XSHG']
['600348.XSHG', '600997.XSHG']
['600348.XSHG', '601001.XSHG']
['600368.XSHG', '600527.XSHG']
['600375.XSHG', '600581.XSHG']
['600391.XSHG', '601100.XSHG']
['600449.XSHG', '601311.XSHG']
['600449.XSHG', '601519.XSHG']
['600460.XSHG', '601908.XSHG']
['600477.XSHG', '600581.XSHG']
['600508.XSHG', '600546.XSHG']
['600508.XSHG', '600997.XSHG']
['600522.XSHG', '600973.XSHG']
['600550.XSHG', '600831.XSHG']
['600569.XSHG', '600808.XSHG']
['600569.XSHG', '600963.XSHG']
['600581.XSHG', '600963.XSHG']
['600581.XSHG', '601001.XSHG']
['600581.XSHG', '601168.XSHG']
['600581.XSHG', '601666.XSHG']
['600586.XSHG', '601268.XSHG']
['600595.XSHG', '601001.XSHG']
['600595.XSHG', '601168.XSHG']
['600595.XSHG', '601666.XSHG']
['600688.XSHG', '600871.XSHG']
['600785.XSHG', '600827.XSHG']
['600808.XSHG', '600963.XSHG']
['600827.XSHG', '601001.XSHG']
['600875.XSHG', '601001.XSHG']
['600883.XSHG', '601268.XSHG']
['601001.XSHG', '601101.XSHG']
['601001.XSHG', '601168.XSHG']
['601001.XSHG', '601666.XSHG']
['601101.XSHG', '601666.XSHG']
['601168.XSHG', '601666.XSHG']
```
| | stock1 | stock2 | annualized_return | sharpe |
| --- | --- |
| 0 | 000761.XSHE | 600548.XSHG | 0.489473 | 2.411514 |
| 1 | 000708.XSHE | 600327.XSHG | 0.447337 | 2.021270 |
| 2 | 600126.XSHG | 600327.XSHG | 0.438380 | 1.946916 |
| 3 | 000554.XSHE | 000707.XSHE | 0.431123 | 1.331038 |
| 4 | 000939.XSHE | 600819.XSHG | 0.409471 | 1.919758 |
| 5 | 600026.XSHG | 600963.XSHG | 0.408791 | 1.681338 |
| 6 | 600037.XSHG | 600327.XSHG | 0.395624 | 1.691877 |
| 7 | 600808.XSHG | 600963.XSHG | 0.391988 | 1.724114 |
| 8 | 000559.XSHE | 600126.XSHG | 0.389043 | 1.413595 |
| 9 | 000761.XSHE | 600320.XSHG | 0.384325 | 1.807262 |
| 10 | 600126.XSHG | 600963.XSHG | 0.378064 | 1.662569 |
| 11 | 600126.XSHG | 600808.XSHG | 0.375825 | 1.513791 |
| 12 | 000936.XSHE | 600477.XSHG | 0.375135 | 1.707097 |
| 13 | 000930.XSHE | 600026.XSHG | 0.372924 | 1.524350 |
| 14 | 600320.XSHG | 600548.XSHG | 0.372499 | 2.083496 |
| 15 | 000507.XSHE | 600391.XSHG | 0.365637 | 1.813873 |
| 16 | 000559.XSHE | 601666.XSHG | 0.350235 | 0.925901 |
| 17 | 600012.XSHG | 600428.XSHG | 0.327834 | 1.722317 |
| 18 | 000916.XSHE | 600033.XSHG | 0.327795 | 1.406093 |
| 19 | 600035.XSHG | 600126.XSHG | 0.326167 | 1.442674 |
| 20 | 600827.XSHG | 601001.XSHG | 0.322705 | 0.957791 |
| 21 | 000717.XSHE | 600808.XSHG | 0.320737 | 1.293439 |
| 22 | 000559.XSHE | 600477.XSHG | 0.306670 | 1.218095 |
| 23 | 000685.XSHE | 000988.XSHE | 0.302593 | 1.692933 |
| 24 | 000683.XSHE | 000936.XSHE | 0.301804 | 1.550496 |
| 25 | 000559.XSHE | 600026.XSHG | 0.295510 | 1.279449 |
| 26 | 600269.XSHG | 601618.XSHG | 0.294215 | 1.486413 |
| 27 | 600026.XSHG | 600126.XSHG | 0.293884 | 1.441490 |
| 28 | 600068.XSHG | 600126.XSHG | 0.289457 | 1.261351 |
| 29 | 000159.XSHE | 600595.XSHG | 0.288982 | 0.946365 |
| 30 | 600020.XSHG | 600033.XSHG | 0.288243 | 1.489764 |
| 31 | 600126.XSHG | 600569.XSHG | 0.287607 | 1.371374 |
| 32 | 000635.XSHE | 600819.XSHG | 0.285135 | 1.364688 |
| 33 | 600068.XSHG | 600320.XSHG | 0.273513 | 1.262845 |
| 34 | 600785.XSHG | 600827.XSHG | 0.272658 | 0.842093 |
| 35 | 000089.XSHE | 600595.XSHG | 0.269903 | 1.256524 |
| 36 | 000898.XSHE | 600808.XSHG | 0.269717 | 1.074201 |
| 37 | 000717.XSHE | 600282.XSHG | 0.267478 | 1.270872 |
| 38 | 600282.XSHG | 600808.XSHG | 0.266402 | 1.181157 |
| 39 | 000916.XSHE | 600035.XSHG | 0.264325 | 1.079520 |
| 40 | 000089.XSHE | 600037.XSHG | 0.264201 | 1.467101 |
| 41 | 600026.XSHG | 600068.XSHG | 0.263959 | 1.107977 |
| 42 | 600026.XSHG | 600331.XSHG | 0.261025 | 0.977858 |
| 43 | 600020.XSHG | 600035.XSHG | 0.260176 | 1.119975 |
| 44 | 600569.XSHG | 600963.XSHG | 0.260006 | 1.154372 |
| 45 | 600307.XSHG | 600963.XSHG | 0.258488 | 1.322409 |
| 46 | 000898.XSHE | 600022.XSHG | 0.258246 | 1.100292 |
| 47 | 600282.XSHG | 600963.XSHG | 0.257496 | 1.175741 |
| 48 | 600307.XSHG | 600808.XSHG | 0.256071 | 1.062023 |
| 49 | 600126.XSHG | 600282.XSHG | 0.255657 | 1.318676 |
| 50 | 600033.XSHG | 600035.XSHG | 0.255634 | 1.055682 |
| 51 | 000709.XSHE | 601618.XSHG | 0.253129 | 1.062565 |
| 52 | 600026.XSHG | 600307.XSHG | 0.253119 | 0.985825 |
| 53 | 600026.XSHG | 600375.XSHG | 0.250793 | 1.063874 |
| 54 | 000066.XSHE | 600126.XSHG | 0.247493 | 1.469341 |
| 55 | 000830.XSHE | 600320.XSHG | 0.247001 | 1.370327 |
| 56 | 600320.XSHG | 601600.XSHG | 0.246534 | 0.966634 |
| 57 | 000717.XSHE | 600307.XSHG | 0.245805 | 1.202750 |
| 58 | 000417.XSHE | 000685.XSHE | 0.245031 | 1.189700 |
| 59 | 600330.XSHG | 600883.XSHG | 0.243437 | 1.086147 |
| ... | ... | ... | ... |
```
174 rows × 4 columns
```
```py
a = list(outfile[2])
'percentage of outperform HS300: %f' % (1.*len([x for x in a if x>0.117]) / len(a))
'percentage of outperform HS300: 0.741379'
```
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- 1.3 財報閱讀 ? [米缸量化讀財報] 資產負債表-投資相關資產
- 1.4 股東分析
- 技術分析入門 【2】 —— 大家搶籌碼（06年至12年版）
- 技術分析入門 【2】 —— 大家搶籌碼（06年至12年版）— 更新版
- 誰是中國A股最有錢的自然人
- 1.5 宏觀研究
- 【干貨包郵】手把手教你做宏觀擇時
- 宏觀研究：從估值角度看當前市場
- 追尋“國家隊”的足跡
- 二 套利
- 2.1 配對交易
- HS300ETF套利（上）
- 【統計套利】配對交易
- 相似公司股票搬磚
- Paired trading
- 2.2 期現套利 ? 通過股指期貨的期現差與 ETF 對沖套利
- 三 事件驅動
- 3.1 盈利預增
- 盈利預增事件
- 事件驅動策略示例——盈利預增
- 3.2 分析師推薦 ? 分析師的金手指？
- 3.3 牛熊轉換
- 歷史總是相似 牛市還在延續
- 歷史總是相似 牛市已經見頂？
- 3.4 熔斷機制 ? 股海拾貝之 [熔斷錯殺股]
- 3.5 暴漲暴跌 ? [實盤感悟] 遇上暴跌我該怎么做？
- 3.6 兼并重組、舉牌收購 ? 寶萬戰-大戲開幕
- 四 技術分析
- 4.1 布林帶
- 布林帶交易策略
- 布林帶回調系統-日內
- Conservative Bollinger Bands
- Even More Conservative Bollinger Bands
- Simple Bollinger Bands
- 4.2 均線系統
- 技術分析入門 —— 雙均線策略
- 5日線10日線交易策略
- 用5日均線和10日均線進行判斷 --- 改進版
- macross
- 4.3 MACD
- Simple MACD
- MACD quantization trade
- MACD平滑異同移動平均線方法
- 4.4 阿隆指標 ? 技術指標阿隆（ Aroon ）全解析
- 4.5 CCI ? CCI 順勢指標探索
- 4.6 RSI
- 重寫 rsi
- RSI指標策略
- 4.7 DMI ? DMI 指標體系的構建及簡單應用
- 4.8 EMV ? EMV 技術指標的構建及應用
- 4.9 KDJ ? KDJ 策略
- 4.10 CMO
- CMO 策略模仿練習 1
- CMO策略模仿練習2
- [技術指標] CMO
- 4.11 FPC ? FPC 指標選股
- 4.12 Chaikin Volatility
- 嘉慶離散指標測試
- 4.13 委比 ? 實時計算委比
- 4.14 封單量
- 按照封單跟流通股本比例排序，剔除6月上市新股，前50
- 漲停股票封單統計
- 實時計算漲停板股票的封單資金與總流通市值的比例
- 4.15 成交量 ? 決戰之地， IF1507 ！
- 4.16 K 線分析 ? 尋找夜空中最亮的星
- 五 量化模型
- 5.1 動量模型
- Momentum策略
- 【小散學量化】-2-動量模型的簡單實踐
- 一個追漲的策略(修正版)
- 動量策略（momentum driven）
- 動量策略（momentum driven）——修正版
- 最經典的Momentum和Contrarian在中國市場的測試
- 最經典的Momentum和Contrarian在中國市場的測試-yanheven改進
- ［策略］基于勝率的趨勢交易策略
- 策略探討（更新）：價量結合+動量反轉
- 反向動量策略（reverse momentum driven）
- 輕松跑贏大盤 - 主題Momentum策略
- Contrarian strategy
- 5.2 Joseph Piotroski 9 F-Score Value Investing Model · 基本面選股系統：Piotroski F-Score ranking system
- 5.3 SVR · 使用SVR預測股票開盤價 v1.0
- 5.4 決策樹、隨機樹
- 決策樹模型（固定模型）
- 基于Random Forest的決策策略
- 5.5 鐘擺理論 · 鐘擺理論的簡單實現——完美躲過股災和精準抄底
- 5.6 海龜模型
- simple turtle
- 俠之大者 一起賺錢
- 5.7 5217 策略 · 白龍馬的新手策略
- 5.8 SMIA · 基于歷史狀態空間相似性匹配的行業配置 SMIA 模型—取交集
- 5.9 神經網絡
- 神經網絡交易的訓練部分
- 通過神經網絡進行交易
- 5.10 PAMR · PAMR ： 基于均值反轉的投資組合選擇策略 - 修改版
- 5.11 Fisher Transform · Using Fisher Transform Indicator
- 5.12 分型假說， Hurst 指數 · 分形市場假說，一個聽起來很美的假說
- 5.13 變點理論 · 變點策略初步
- 5.14 Z-score Model
- Zscore Model Tutorial
- 信用債風險模型初探之：Z-Score Model
- user-defined package
- 5.15 機器學習 · Machine Learning 學習筆記（一） by OTreeWEN
- 5.16 DualTrust 策略和布林強盜策略
- 5.17 卡爾曼濾波
- 5.18 LPPL anti-bubble model
- 今天大盤熔斷大跌，后市如何—— based on LPPL anti-bubble model
- 破解股市泡沫之謎——對數周期冪率（LPPL）模型
- 六 大數據模型
- 6.1 市場情緒分析
- 通聯情緒指標策略
- 互聯網+量化投資 大數據指數手把手
- 6.2 新聞熱點
- 如何使用優礦之“新聞熱點”？
- 技術分析【3】—— 眾星拱月，眾口鑠金?
- 七 排名選股系統
- 7.1 小市值投資法
- 學習筆記：可模擬（小市值+便宜 的修改版）
- 市值最小300指數
- 流通市值最小股票（新篩選器版）
- 持有市值最小的10只股票
- 10% smallest cap stock
- 7.2 羊駝策略
- 羊駝策略
- 羊駝反轉策略（修改版）
- 羊駝反轉策略
- 我的羊駝策略，選5只股無腦輪替
- 7.3 低價策略
- 專撿便宜貨(新版quartz)
- 策略原理
- 便宜就是 alpha
- 八 輪動模型
- 8.1 大小盤輪動 · 新手上路 -- 二八ETF擇時輪動策略2.0
- 8.2 季節性策略
- Halloween Cycle
- Halloween cycle 2
- 夏買電，東買煤？
- 歷史的十一月板塊漲幅
- 8.3 行業輪動
- 銀行股輪動
- 申萬二級行業在最近1年、3個月、5個交易日的漲幅統計
- 8.4 主題輪動
- 快速研究主題神器
- recommendation based on subject
- strategy7: recommendation based on theme
- 板塊異動類
- 風險因子（離散類）
- 8.5 龍頭輪動
- Competitive Securities
- Market Competitiveness
- 主題龍頭類
- 九 組合投資
- 9.1 指數跟蹤 · [策略] 指數跟蹤低成本建倉策略
- 9.2 GMVP · Global Minimum Variance Portfolio (GMVP)
- 9.3 凸優化 · 如何在 Python 中利用 CVXOPT 求解二次規劃問題
- 十 波動率
- 10.1 波動率選股 · 風平浪靜 風起豬飛
- 10.2 波動率擇時
- 基于 VIX 指數的擇時策略
- 簡單低波動率指數
- 10.3 Arch/Garch 模型 · 如何使用優礦進行 GARCH 模型分析
- 十一 算法交易
- 11.1 VWAP · Value-Weighted Average Price (VWAP)
- 十二 中高頻交易
- 12.1 order book 分析 · 基于高頻 limit order book 數據的短程價格方向預測—— via multi-class SVM
- 12.2 日內交易 · 大盤日內走勢 (for 擇時)
- 十三 Alternative Strategy
- 13.1 易經、傳統文化 · 老黃歷診股
- 第三部分 基金、利率互換、固定收益類
- 一 分級基金
- “優礦”集思錄——分級基金專題
- 基于期權定價的分級基金交易策略
- 基于期權定價的興全合潤基金交易策略
- 二 基金分析
- Alpha 基金“黑天鵝事件” -- 思考以及原因
- 三 債券
- 債券報價中的小陷阱
- 四 利率互換
- Swap Curve Construction
- 中國 Repo 7D 互換的例子
- 第四部分 衍生品相關
- 一 期權數據
- 如何獲取期權市場數據快照
- 期權高頻數據準備
- 二 期權系列
- [ 50ETF 期權] 1. 歷史成交持倉和 PCR 數據
- 【50ETF期權】 2. 歷史波動率
- 【50ETF期權】 3. 中國波指 iVIX
- 【50ETF期權】 4. Greeks 和隱含波動率微笑
- 【50ETF期權】 5. 日內即時監控 Greeks 和隱含波動率微笑
- 【50ETF期權】 5. 日內即時監控 Greeks 和隱含波動率微笑
- 三 期權分析
- 【50ETF期權】 期權擇時指數 1.0
- 每日期權風險數據整理
- 期權頭寸計算
- 期權探秘1
- 期權探秘2
- 期權市場一周縱覽
- 基于期權PCR指數的擇時策略
- 期權每日成交額PC比例計算
- 四 期貨分析
- 【前方高能！】Gifts from Santa Claus——股指期貨趨勢交易研究