Merge pull request #14 from SaguaroCapital/dev

Check `get_bid`/`get_ask` index, additional precompile

Project.toml

@@ -1,7 +1,7 @@
 name = "SaguaroTrader"
 uuid = "26277856-a3f7-4646-aaac-f090473ab108"
 authors = ["Tyler Thomas <[email protected]>"]
-version = "0.1.1"
+version = "0.1.2"
 
 [deps]
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"

src/data/daily_bar_csv.jl

@@ -254,21 +254,21 @@ struct CSVDailyBarSource <: DataSource
     end
 end
 
-function get_bid(ds::CSVDailyBarSource, dt::DateTime, asset::Symbol)
+function get_bid(ds::CSVDailyBarSource, dt::DateTime, asset::Symbol)::Float64
     df_bid_ask = ds.dict_asset_dfs[asset]
-    ix = searchsortedfirst(df_bid_ask.timestamp::Vector{DateTime}, dt)::Union{Nothing,Int64}
-    if isnothing(ix) || (ix == 1)
-        return float(NaN)::Float64
+    ix = searchsortedfirst(df_bid_ask.timestamp::Vector{DateTime}, dt)::Int64
+    if (ix == 1) || (ix > size(df_bid_ask, 1))
+        return NaN
     else
         return @inbounds df_bid_ask[ix, :Bid]::Float64
     end
 end
 
-function get_ask(ds::CSVDailyBarSource, dt::DateTime, asset::Symbol)
+function get_ask(ds::CSVDailyBarSource, dt::DateTime, asset::Symbol)::Float64
     df_bid_ask = ds.dict_asset_dfs[asset]
-    ix = searchsortedfirst(df_bid_ask.timestamp::Vector{DateTime}, dt)::Union{Nothing,Int64}
-    if isnothing(ix) || (ix == 1)
-        return float(NaN)::Float64
+    ix = searchsortedfirst(df_bid_ask.timestamp::Vector{DateTime}, dt)::Int64
+    if (ix == 1) || (ix > size(df_bid_ask, 1))
+        return NaN
     else
         return @inbounds df_bid_ask[ix, :Ask]::Float64
     end

src/precompile.jl

@@ -6,42 +6,82 @@ using SnoopPrecompile
     portfolio_id = "test_port"
     start_dt = DateTime(2020, 1, 1)
     end_dt = DateTime(2020, 12, 31)
+    signal_weights = Dict(Equity(:AMD) => 1.0, Equity(:INTC) => 1.0)
+    rolling_signal_weights = Dict(
+        DateTime(2020) => Dict(Equity(:AMD) => 1.0, Equity(:INTC) => 1.0),
+        DateTime(2021) => Dict(Equity(:INTC) => 1.0),
+        DateTime(2022) => Dict(Equity(:AMD) => 1.0),
+    )
 
     @precompile_all_calls begin
         # all calls in this block will be precompiled, regardless of whether
         # they belong to your package or not (on Julia 1.8 and higher)
 
-        # This was just taking from test/backtest.jl
-        # Better precompile should be done, but this significantly reduced latency
-        # Especially 
-
-        assets = [Equity(:AMD), Equity(:INTC), Equity(:NVDA)]
-        universe = StaticUniverse(assets)
-        # This brings the CSVDailyBarSource test from 5s to 0.3s on v1.9-rc1
-        # ds = CSVDailyBarSource("./test/test_data") #TODO: should we just hardcode an example df?
-        # dh = BacktestDataHandler([ds])
-        # port_optimizer = EqualWeightPortfolioOptimizer(dh)
-        # broker = SimulatedBroker(
-        #     DateTime(2020), SimulatedExchange(DateTime(2020)), dh; initial_cash=100_000.0
-        # )
-        # create_portfolio!(broker, 75_000; portfolio_id=portfolio_id)
-        # order_sizer = DollarWeightedOrderSizer(0.01)
-        signal_weights = Dict(Equity(:AMD) => 1.0, Equity(:INTC) => 1.0)
+        #####################################################################
+        # Universe
+        static_assets = [Equity(:AMD), Equity(:INTC), Equity(:NVDA), Cash()]
+        uni = StaticUniverse(static_assets)
+
+        # dynamic universe
+        dynamic_assets = Dict(
+            Equity(:AMD) => start_dt,
+            Equity(:INTC) => start_dt,
+            Equity(:NVDA) => start_dt,
+            Cash() => start_dt,
+        )
+        uni = DynamicUniverse(dynamic_assets)
+        _get_assets(uni, end_dt)
+
+        #####################################################################
+        # Order
+        ord1 = Order(start_dt, 100.0, Equity(:AMD))
+        ord2 = Order(start_dt, 100.0, Equity(:AMD))
+        equal_orders(ord1, ord2)
+
+        #####################################################################
+        # Rebalance
+        BuyAndHoldRebalance(Date(2022, 1, 1))
+        WeeklyRebalance(Date(2022, 1, 1), Date(2022, 3, 1))
+        MonthlyRebalance(Date(2022, 1, 1), Date(2022, 3, 1))
+
+        #####################################################################
+        # Exchange
+        is_open(SimulatedExchange(start_dt), end_dt)
+        is_open(AlwaysOpenExchange(start_dt), end_dt)
+
+        #####################################################################
+        # Fee Model
+        calculate_fee(ZeroFeeModel(), 100.0, 10.0)
+        calculate_fee(PercentFeeModel(; tax_pct=0.15, fee_pct=0.10), 100.0, 10.0)
+
+        #####################################################################
+        # Data
+
+        #####################################################################
+        # Portfolio
+        create_subscription(DateTime(2022, 1, 1), 50.0, 100.0)
+        create_withdrawal(DateTime(2022, 1, 1), 50.0, 100.0)
+        create_asset_transaction(DateTime(2022, 1, 1), 50.0, 100.0, 1) # buy
+        create_asset_transaction(DateTime(2022, 1, 1), 50.0, 100.0, -1) # sell
+
+        port = Portfolio(DateTime(2022, 1, 1), 100_000.0, "USD")
+        total_market_value(port)
+        total_equity(port)
+        total_unrealized_pnl(port)
+        total_realized_pnl(port)
+        total_pnl(port)
+        subscribe_funds!(port, DateTime(2022, 2, 1), 50_000.0)
+        withdraw_funds!(port, DateTime(2022, 2, 2), 50_000.0)
+        txn = Transaction(Equity(:AMD), 10.0, DateTime(2022, 2, 2), 50.0, 0.1)
+        transact_asset!(port, txn)
+
+        #####################################################################
+        # Alpha Model
         alpha_model = FixedSignalsAlphaModel(signal_weights)
-        # port_optimizer = FixedWeightPortfolioOptimizer(dh)
-        rebalance = DailyRebalance(Date(start_dt), Date(end_dt))
-
-        # trading_session = BacktestTradingSession(
-        #     start_dt,
-        #     end_dt,
-        #     universe,
-        #     broker,
-        #     alpha_model,
-        #     rebalance,
-        #     portfolio_id,
-        #     order_sizer,
-        #     port_optimizer,
-        # )
-        # run!(trading_session)
+        alpha_model(DateTime(2022, 1, 1))
+        alpha_model = SingleSignalAlphaModel(StaticUniverse(static_assets), 1.0)
+        alpha_model(DateTime(2022, 1, 1))
+        alpha_model = RollingSignalsAlphaModel(rolling_signal_weights)
+        alpha_model(DateTime(2022, 1, 1))
     end
 end

test/portfolio.jl

@@ -69,17 +69,6 @@ end
     @test pe.type == "sell_asset"
 end
 
-@testset "PortfolioEvent" begin
-    pe = create_subscription(DateTime(2022, 1, 1), 50.0, 100.0)
-    @test pe.type == "subscription"
-    pe = create_withdrawal(DateTime(2022, 1, 1), 50.0, 100.0)
-    @test pe.type == "withdrawal"
-    pe = create_asset_transaction(DateTime(2022, 1, 1), 50.0, 100.0, 1)
-    @test pe.type == "buy_asset"
-    pe = create_asset_transaction(DateTime(2022, 1, 1), 50.0, 100.0, -1)
-    @test pe.type == "sell_asset"
-end
-
 @testset "Portfolio" begin
     port = Portfolio(DateTime(2022, 1, 1), 100_000.0, "USD")
     @test total_market_value(port) == 0.0