建立你的交易回測系統
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# TEJ獨家開發的輕量化 Zipline 回測引擎，最少僅需輸入您的策略建構式 pipeline 即可回測，亦可客製化各項參數
from zipline.algo.pipeline_algo import *

algo = TargetPercentPipeAlgo(
start_session=start_dt,
end_session=end_dt,
capital_base=1e6, 
tradeday=tradeday,
max_leverage=0.80,
slippage_model=slippage.VolumeShareSlippage(volume_limit=0.15, price_impact=0.01),　
pipeline=compute_signals,
analyze=analyze
)

results = algo.run()

#Pipeline策略構建式
from zipline.pipeline.data import TQDataSet, TWEquityPricing
from zipline.pipeline.factors import SimpleMovingAverage
from zipline.pipeline import Pipeline
def compute_signals():
  ind = TQDataSet.Industry.latest
  volume = TQDataSet.Volume_1000_Shares.latest
  mv = TQDataSet.Market_Cap_Dollars.latest
  close = TWEquityPricing.close.latest
  mv_filters = mv.percentile_between(80, 100)
  #計算每期前20%市值公司中，近20天收盤價的移動平均
  ma20 = SimpleMovingAverage(
    inputs=[TWEquityPricing.close],
    window_length=20,
    mask=mv_filters
    )

　return Pipeline(columns={
　　'close':close,
　　'volume':volume,
　　'ind':ind,
　　'mv':mv,
　　'ma20':ma20 
　　},
　)

compute_signals().show_graph(format='svg')

# 使用 extract_rets_pos_txn_from_zipline 將 Zipline-tej 回測出的結果進行拆分，用於後續各種績效報表中
from pyfolio.utils import extract_rets_pos_txn_from_zipline
returns, positions, transactions = extract_rets_pos_txn_from_zipline(results)

#pyfolio.tears.create_full_tear_sheet一行程式產出完整的視覺化績效報表
pyfolio.tears.create_full_tear_sheet(returns=returns,
positions=positions,
transactions=transactions,
benchmark_rets=benchmark_rets
)

# 觀察策略在各種歷史事件如2008年金融海嘯、2015年中國股災、2020年新冠疫情下的穩健性
from pyfolio.tears import create_interesting_times_tear_sheet
create_interesting_times_tear_sheet(returns, benchmark_rets)

# 檢驗個股是否有成交量太低造成出清困難的問題
pf.capacity.days_to_liquidate_positions(
positions,
market_data,
max_bar_consumption=0.2,
capital_base=1e6,
mean_volume_window=5,
).loc['20130430':]