def test_get_stock_price(data_loader):
obj = strategies.BackTest(
stock_id="0056",
start_date="2018-01-01",
end_date="2019-01-01",
trader_fund=500000.0,
fee=0.001425,
data_loader=data_loader,
# strategy=ContinueHolding,
)
assert isinstance(obj.stock_price, pd.DataFrame)
def test_bias(data_loader):
obj = strategies.BackTest(
stock_id="0056",
start_date="2018-01-01",
end_date="2019-01-01",
trader_fund=500000.0,
fee=0.001425,
strategy=strategies.Bias,
data_loader=data_loader,
)
obj.simulate()
assert int(obj.final_stats.MeanProfit) == 984
assert int(obj.final_stats.MaxLoss) == -863
assert int(obj.final_stats.FinalProfit) == 2845
assert obj.final_stats["MeanProfitPer"] == 0.20
assert obj.final_stats["FinalProfitPer"] == 0.57
assert obj.final_stats["MaxLossPer"] == -0.17
assert obj.trade_detail.to_dict("r")[1] == {
"EverytimeProfit": 0.0,
"RealizedProfit": 0.0,
"UnrealizedProfit": 0.0,
"board_lot": 1000.0,
"date": "2018-02-05",
"hold_cost": 0.0,
"hold_volume": 0.0,
"signal": 0.0,
"stock_id": "0056",
"trade_price": 26.1,
"trader_fund": 500000.0,
"EverytimeTotalProfit": 500000.0,
"CashEarningsDistribution": 0.0,
"StockEarningsDistribution": 0.0,
}
assert obj.compare_market_detail.to_dict("r")[-1] == {
"CumDailyReturn": -0.39843,
"CumTaiExDailyReturn": -0.0963,
"date": "2018-12-28",
}
assert obj.compare_market_stats["AnnualTaiexReturnPer"] == -9.6
assert obj.compare_market_stats["AnnualReturnPer"] == 0.57
def test_continue_holding(data_loader):
obj = strategies.BackTest(
stock_id="0056",
start_date="2018-01-01",
end_date="2019-01-01",
trader_fund=500000.0,
fee=0.001425,
strategy=strategies.ContinueHolding,
data_loader=data_loader,
)
obj.simulate()
assert int(obj.final_stats.MeanProfit) == 2810
assert int(obj.final_stats.MaxLoss) == -9663
assert int(obj.final_stats.FinalProfit) == 3407
assert obj.final_stats["MeanProfitPer"] == 0.56
assert obj.final_stats["FinalProfitPer"] == 0.68
assert obj.final_stats["MaxLossPer"] == -1.93
assert obj.trade_detail.to_dict("r")[1] == {
"stock_id": "0056",
"date": "2018-01-03",
"EverytimeProfit": -96.83,
"RealizedProfit": 0.0,
"UnrealizedProfit": -96.83,
"board_lot": 1000,
"hold_cost": 25.18583875,
"hold_volume": 1000,
"signal": 1,
"trade_price": 25.15,
"trader_fund": 474814.16125,
"EverytimeTotalProfit": 474717.33125,
"CashEarningsDistribution": 0.0,
"StockEarningsDistribution": 0.0,
}
assert obj.compare_market_detail.to_dict("r")[-1] == {
"CumDailyReturn": -0.61003,
"CumTaiExDailyReturn": -0.0963,
"date": "2018-12-28",
}
assert obj.compare_market_stats["AnnualTaiexReturnPer"] == -9.6
assert obj.compare_market_stats["AnnualReturnPer"] == 0.68
def test_max_min_period_bias(data_loader):
obj = strategies.BackTest(
stock_id="0056",
start_date="2018-01-01",
end_date="2019-01-01",
trader_fund=500000.0,
fee=0.001425,
strategy=strategies.MaxMinPeriodBias,
data_loader=data_loader,
)
obj.simulate()
assert int(obj.final_stats.MeanProfit) == 0
assert int(obj.final_stats.MaxLoss) == 0
assert int(obj.final_stats.FinalProfit) == 0
assert obj.final_stats["MeanProfitPer"] == 0
assert obj.final_stats["FinalProfitPer"] == 0
assert obj.final_stats["MaxLossPer"] == 0
def test_ma_crossover(data_loader):
obj = strategies.BackTest(
stock_id="0056",
start_date="2018-01-01",
end_date="2019-01-01",
trader_fund=500000.0,
fee=0.001425,
strategy=strategies.MaCrossOver,
data_loader=data_loader,
)
obj.simulate()
assert int(obj.final_stats.MeanProfit) == -381
assert int(obj.final_stats.MaxLoss) == -1230
assert int(obj.final_stats.FinalProfit) == -1230
assert obj.final_stats["MeanProfitPer"] == -0.08
assert obj.final_stats["FinalProfitPer"] == -0.25
assert obj.final_stats["MaxLossPer"] == -0.25
def test_short_sale_margin_purchase_ratio(data_loader):
obj = strategies.BackTest(
stock_id="0056",
start_date="2018-01-01",
end_date="2019-01-01",
trader_fund=500000.0,
fee=0.001425,
strategy=strategies.ShortSaleMarginPurchaseRatio,
data_loader=data_loader,
)
obj.simulate()
assert int(obj.final_stats.MeanProfit) == 12946
assert int(obj.final_stats.MaxLoss) == -14706
assert int(obj.final_stats.FinalProfit) == 22576
assert obj.final_stats["MeanProfitPer"] == 2.59
assert obj.final_stats["FinalProfitPer"] == 4.52
assert obj.final_stats["MaxLossPer"] == -2.94
def test_institutional_investors_follower(data_loader):
obj = strategies.BackTest(
stock_id="0056",
start_date="2018-01-01",
end_date="2019-01-01",
trader_fund=500000.0,
fee=0.001425,
strategy=strategies.InstitutionalInvestorsFollower,
data_loader=data_loader,
)
obj.simulate()
assert int(obj.final_stats.MeanProfit) == 6021
assert int(obj.final_stats.MaxLoss) == -15410
assert int(obj.final_stats.FinalProfit) == 10699
assert obj.final_stats["MeanProfitPer"] == 1.2
assert obj.final_stats["FinalProfitPer"] == 2.14
assert obj.final_stats["MaxLossPer"] == -3.08
def test_kd(data_loader):
obj = strategies.BackTest(
stock_id="0056",
start_date="2018-01-01",
end_date="2019-01-01",
trader_fund=500000.0,
fee=0.001425,
strategy=strategies.Kd,
data_loader=data_loader,
)
obj.simulate()
assert int(obj.final_stats.MeanProfit) == 2356
assert int(obj.final_stats.MaxLoss) == -1425
assert int(obj.final_stats.FinalProfit) == 6196
assert obj.final_stats["MeanProfitPer"] == 0.47
assert obj.final_stats["FinalProfitPer"] == 1.24
assert obj.final_stats["MaxLossPer"] == -0.29
def test_naive_kd(data_loader):
obj = strategies.BackTest(
stock_id="0056",
start_date="2018-01-01",
end_date="2019-01-01",
trader_fund=500000.0,
fee=0.001425,
strategy=strategies.NaiveKd,
data_loader=data_loader,
)
obj.simulate()
assert int(obj.final_stats.MeanProfit) == 5418
assert int(obj.final_stats.MaxLoss) == -2094
assert int(obj.final_stats.FinalProfit) == 15033
assert obj.final_stats["MeanProfitPer"] == 1.08
assert obj.final_stats["FinalProfitPer"] == 3.01
assert obj.final_stats["MaxLossPer"] == -0.42
def test_macd_crossover_add_strategy(data_loader):
obj = strategies.BackTest(
stock_id="0056",
start_date="2018-01-01",
end_date="2019-01-01",
trader_fund=500000.0,
fee=0.001425,
# strategy=strategies.MacdCrossOver,
data_loader=data_loader,
)
obj.add_strategy(strategies.MacdCrossOver)
obj.simulate()
assert int(obj.final_stats.MeanProfit) == 1347
assert int(obj.final_stats.MaxLoss) == -397
assert int(obj.final_stats.FinalProfit) == 3232
assert obj.final_stats["MeanProfitPer"] == 0.27
assert obj.final_stats["FinalProfitPer"] == 0.65
assert obj.final_stats["MaxLossPer"] == -0.08
def test_bias_add_strategy(data_loader):
obj = strategies.BackTest(
stock_id="0056",
start_date="2018-01-01",
end_date="2019-01-01",
trader_fund=500000.0,
fee=0.001425,
# strategy=Bias,
data_loader=data_loader,
)
obj.add_strategy(strategies.Bias)
obj.simulate()
assert int(obj.final_stats.MeanProfit) == 984
assert int(obj.final_stats.MaxLoss) == -863
assert int(obj.final_stats.FinalProfit) == 2845
assert obj.final_stats["MeanProfitPer"] == 0.20
assert obj.final_stats["FinalProfitPer"] == 0.57
assert obj.final_stats["MaxLossPer"] == -0.17
assert obj.trade_detail.to_dict("r")[1] == {
"EverytimeProfit": 0.0,
"RealizedProfit": 0.0,
"UnrealizedProfit": 0.0,
"board_lot": 1000.0,
"date": "2018-02-05",
"hold_cost": 0.0,
"hold_volume": 0.0,
"signal": 0.0,
"stock_id": "0056",
"trade_price": 26.1,
"trader_fund": 500000.0,
"EverytimeTotalProfit": 500000.0,
"CashEarningsDistribution": 0.0,
"StockEarningsDistribution": 0.0,
}