strand-package: strand: a framework for investment strategy simulation
In strand: A Framework for Investment Strategy Simulation
Description
Author(s)
Examples
Description
The strand package provides a framework for performing discrete (share-level)
simulations of investment strategies. Simulated portfolios optimize exposure
to an input signal subject to constraints such as position size and factor
exposure.
For an introduction to running simulations using the package, see
vignette("strand"). For details on available methods see the
documentation for the Simulation class.
Author(s)
Jeff Enos jeffrey.enos@gmail.com and David Kane dave.kane@gmail.com
Examples
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# Load up sample data
data(sample_secref)
data(sample_pricing)
data(sample_inputs)
# Load sample configuration
config <- example_strategy_config()
# Override config file end date to run a one-week sim
config$to <- as.Date("2020-06-05")
# Create the Simulation object and run
sim <- Simulation$new(config,
raw_input_data = sample_inputs,
raw_pricing_data = sample_pricing,
security_reference_data = sample_secref)
sim$run()
# Print overall statistics
sim$overallStatsDf()
# Access tabular result data
head(sim$getSimSummary())
head(sim$getSimDetail())
head(sim$getPositionSummary())
head(sim$getInputStats())
head(sim$getOptimizationSummary())
head(sim$getExposures())
# Plot results
## Not run:
sim$plotPerformance()
sim$plotMarketValue()
sim$plotCategoryExposure("sector")
sim$plotFactorExposure(c("value", "size"))
sim$plotNumPositions()
## End(Not run)
Example output
Item Gross Net
1 Total P&L 53,576 51,516
2 Total Return on GMV (%) 2.9 2.7
3 Annualized Return on GMV (%) 144.3 136.1
4 Annualized Vol (%) 5.8 6.4
5 Annualized Sharpe 24.86 21.21
6 Max Drawdown (%) 0.8 0.8
7 Avg GMV 1,666,104
8 Avg NMV 17,740
9 Avg Count 164
10 Avg Daily Turnover 376,989
11 Holding Period (months) 0.4
# A tibble: 6 x 25
strategy market_fill_nmv transfer_fill_n… start_nmv end_nmv market_order_gmv
<chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 joint 2734. 0 0 2734. 1017629.
2 strateg… 2734. 0 0 2734. 1017629.
3 joint -675. 0 2734. 8418. 496977.
4 strateg… -675. 0 2734. 8418. 496977.
5 joint -3530. 0 8418. 19101. 243155.
6 strateg… -3530. 0 8418. 19101. 243155.
# … with 19 more variables: market_fill_gmv <dbl>, transfer_fill_gmv <dbl>,
# end_gmv <dbl>, position_pnl <dbl>, trading_pnl <dbl>, gross_pnl <dbl>,
# net_pnl <dbl>, trade_costs <dbl>, financing_costs <dbl>,
# fill_rate_pct <dbl>, end_lmv <dbl>, end_smv <dbl>, start_lmv <dbl>,
# start_smv <dbl>, end_num <int>, end_num_long <int>, end_num_short <int>,
# num_investable <int>, sim_date <date>
# A tibble: 6 x 38
id strategy shares order_shares int_shares ext_shares market_order_sh…
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <int>
1 AAL strateg… 0 -952 0 0 -952
2 ABBV strateg… 0 -108 0 0 -108
3 ADM strateg… 0 254 0 0 254
4 ADSK strateg… 0 -48 0 0 -48
5 AIG strateg… 0 333 0 0 333
6 ALLE strateg… 0 -100 0 0 -100
# … with 31 more variables: transfer_order_shares <int>,
# market_fill_shares <int>, transfer_fill_shares <int>, end_int_shares <int>,
# end_ext_shares <int>, fill_shares <int>, end_shares <dbl>,
# start_price <dbl>, end_price <dbl>, dividend <dbl>, distribution <dbl>,
# investable <lgl>, delisting <lgl>, value <dbl>, position_pnl <dbl>,
# trading_pnl <dbl>, trade_costs <dbl>, financing_costs <dbl>,
# gross_pnl <dbl>, net_pnl <dbl>, market_order_gmv <dbl>,
# market_fill_nmv <dbl>, market_fill_gmv <dbl>, transfer_fill_nmv <dbl>,
# transfer_fill_gmv <dbl>, start_nmv <dbl>, end_nmv <dbl>, end_gmv <dbl>,
# max_pos_lmv <dbl>, max_pos_smv <dbl>, sim_date <date>
# A tibble: 6 x 9
# Groups: id [3]
id strategy gross_pnl net_pnl average_market_… total_trading trade_costs
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 OXY joint 5389 5377 12406 10661 11
2 OXY strateg… 5389 5377 12406 10661 11
3 UAL joint 4621 4610 12461 10517 11
4 UAL strateg… 4621 4610 12461 10517 11
5 PVH joint 3329 3317 11973 10476 10
6 PVH strateg… 3329 3317 11973 10476 10
# … with 2 more variables: financing_costs <dbl>, days_in_portfolio <int>
# A tibble: 0 x 0
strategy start_smv start_lmv order_gmv end_smv end_lmv start_value
1 strategy_1 0.0 0.0 1001199.3 -501006.5 500192.8 0
2 joint 0.0 0.0 1001199.3 -501006.5 500192.8 0
3 strategy_1 -507447.7 510181.3 490646.0 -753446.6 754828.4 1568970
4 joint -507447.7 510181.3 490646.0 -753446.6 754828.4 1568970
5 strategy_1 -759726.1 768143.9 236344.8 -880010.5 884204.3 2013139
6 joint -759726.1 768143.9 236344.8 -880010.5 884204.3 2013139
end_value start_size end_size sim_date
1 1538089 0.000 -9737.629 2020-06-01
2 1538089 0.000 -9737.629 2020-06-01
3 1985717 -10962.781 -9719.187 2020-06-02
4 1985717 -10962.781 -9719.187 2020-06-02
5 2177176 -8034.767 -9810.801 2020-06-03
6 2177176 -8034.767 -9810.801 2020-06-03
strategy weight_divisor sector_Communication Services
1 strategy_1 1e+06 0.02025934
2 joint 1e+06 0.02025934
3 strategy_1 1e+06 0.02060105
4 joint 1e+06 0.02060105
5 strategy_1 1e+06 0.02209832
6 joint 1e+06 0.02209832
sector_Consumer Discretionary sector_Consumer Staples sector_Energy
1 -0.01926901 -0.02034710 0.02062658
2 -0.01926901 -0.02034710 0.02062658
3 -0.01981910 -0.01914917 0.02094451
4 -0.01981910 -0.01914917 0.02094451
5 -0.01799718 -0.02046579 0.02211464
6 -0.01799718 -0.02046579 0.02211464
sector_Financials sector_Health Care sector_Industrials
1 0.02090055 -0.01989795 -0.02034090
2 0.02090055 -0.01989795 -0.02034090
3 0.02204757 -0.02024579 -0.01983724
4 0.02204757 -0.02024579 -0.01983724
5 0.02670573 -0.02068899 -0.01887225
6 0.02670573 -0.02068899 -0.01887225
sector_Information Technology sector_Materials sector_Real Estate
1 -0.01992691 0.02008254 0.00027883
2 -0.01992691 0.02008254 0.00027883
3 -0.01973070 0.01643217 0.00712367
4 -0.01973070 0.01643217 0.00712367
5 -0.01727644 0.01510427 0.00791443
6 -0.01727644 0.01510427 0.00791443
sector_Utilities value size sim_date
1 0.02036772 1.567603 -0.01531873 2020-06-01
2 0.02036772 1.567603 -0.01531873 2020-06-01
3 0.02005078 2.014189 -0.01322212 2020-06-02
4 0.02005078 2.014189 -0.01322212 2020-06-02
5 0.02046454 2.249470 -0.01827253 2020-06-03
6 0.02046454 2.249470 -0.01827253 2020-06-03
strand documentation built on Nov. 20, 2020, 1:08 a.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Author(s) Examples
Description
The strand package provides a framework for performing discrete (share-level) simulations of investment strategies. Simulated portfolios optimize exposure to an input signal subject to constraints such as position size and factor exposure.
For an introduction to running simulations using the package, see
vignette("strand"). For details on available methods see the
documentation for the Simulation class.
Author(s)
Jeff Enos jeffrey.enos@gmail.com and David Kane dave.kane@gmail.com
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | # Load up sample data
data(sample_secref)
data(sample_pricing)
data(sample_inputs)
# Load sample configuration
config <- example_strategy_config()
# Override config file end date to run a one-week sim
config$to <- as.Date("2020-06-05")
# Create the Simulation object and run
sim <- Simulation$new(config,
raw_input_data = sample_inputs,
raw_pricing_data = sample_pricing,
security_reference_data = sample_secref)
sim$run()
# Print overall statistics
sim$overallStatsDf()
# Access tabular result data
head(sim$getSimSummary())
head(sim$getSimDetail())
head(sim$getPositionSummary())
head(sim$getInputStats())
head(sim$getOptimizationSummary())
head(sim$getExposures())
# Plot results
## Not run:
sim$plotPerformance()
sim$plotMarketValue()
sim$plotCategoryExposure("sector")
sim$plotFactorExposure(c("value", "size"))
sim$plotNumPositions()
## End(Not run)
|
Example output
Item Gross Net
1 Total P&L 53,576 51,516
2 Total Return on GMV (%) 2.9 2.7
3 Annualized Return on GMV (%) 144.3 136.1
4 Annualized Vol (%) 5.8 6.4
5 Annualized Sharpe 24.86 21.21
6 Max Drawdown (%) 0.8 0.8
7 Avg GMV 1,666,104
8 Avg NMV 17,740
9 Avg Count 164
10 Avg Daily Turnover 376,989
11 Holding Period (months) 0.4
# A tibble: 6 x 25
strategy market_fill_nmv transfer_fill_n… start_nmv end_nmv market_order_gmv
<chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 joint 2734. 0 0 2734. 1017629.
2 strateg… 2734. 0 0 2734. 1017629.
3 joint -675. 0 2734. 8418. 496977.
4 strateg… -675. 0 2734. 8418. 496977.
5 joint -3530. 0 8418. 19101. 243155.
6 strateg… -3530. 0 8418. 19101. 243155.
# … with 19 more variables: market_fill_gmv <dbl>, transfer_fill_gmv <dbl>,
# end_gmv <dbl>, position_pnl <dbl>, trading_pnl <dbl>, gross_pnl <dbl>,
# net_pnl <dbl>, trade_costs <dbl>, financing_costs <dbl>,
# fill_rate_pct <dbl>, end_lmv <dbl>, end_smv <dbl>, start_lmv <dbl>,
# start_smv <dbl>, end_num <int>, end_num_long <int>, end_num_short <int>,
# num_investable <int>, sim_date <date>
# A tibble: 6 x 38
id strategy shares order_shares int_shares ext_shares market_order_sh…
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <int>
1 AAL strateg… 0 -952 0 0 -952
2 ABBV strateg… 0 -108 0 0 -108
3 ADM strateg… 0 254 0 0 254
4 ADSK strateg… 0 -48 0 0 -48
5 AIG strateg… 0 333 0 0 333
6 ALLE strateg… 0 -100 0 0 -100
# … with 31 more variables: transfer_order_shares <int>,
# market_fill_shares <int>, transfer_fill_shares <int>, end_int_shares <int>,
# end_ext_shares <int>, fill_shares <int>, end_shares <dbl>,
# start_price <dbl>, end_price <dbl>, dividend <dbl>, distribution <dbl>,
# investable <lgl>, delisting <lgl>, value <dbl>, position_pnl <dbl>,
# trading_pnl <dbl>, trade_costs <dbl>, financing_costs <dbl>,
# gross_pnl <dbl>, net_pnl <dbl>, market_order_gmv <dbl>,
# market_fill_nmv <dbl>, market_fill_gmv <dbl>, transfer_fill_nmv <dbl>,
# transfer_fill_gmv <dbl>, start_nmv <dbl>, end_nmv <dbl>, end_gmv <dbl>,
# max_pos_lmv <dbl>, max_pos_smv <dbl>, sim_date <date>
# A tibble: 6 x 9
# Groups: id [3]
id strategy gross_pnl net_pnl average_market_… total_trading trade_costs
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 OXY joint 5389 5377 12406 10661 11
2 OXY strateg… 5389 5377 12406 10661 11
3 UAL joint 4621 4610 12461 10517 11
4 UAL strateg… 4621 4610 12461 10517 11
5 PVH joint 3329 3317 11973 10476 10
6 PVH strateg… 3329 3317 11973 10476 10
# … with 2 more variables: financing_costs <dbl>, days_in_portfolio <int>
# A tibble: 0 x 0
strategy start_smv start_lmv order_gmv end_smv end_lmv start_value
1 strategy_1 0.0 0.0 1001199.3 -501006.5 500192.8 0
2 joint 0.0 0.0 1001199.3 -501006.5 500192.8 0
3 strategy_1 -507447.7 510181.3 490646.0 -753446.6 754828.4 1568970
4 joint -507447.7 510181.3 490646.0 -753446.6 754828.4 1568970
5 strategy_1 -759726.1 768143.9 236344.8 -880010.5 884204.3 2013139
6 joint -759726.1 768143.9 236344.8 -880010.5 884204.3 2013139
end_value start_size end_size sim_date
1 1538089 0.000 -9737.629 2020-06-01
2 1538089 0.000 -9737.629 2020-06-01
3 1985717 -10962.781 -9719.187 2020-06-02
4 1985717 -10962.781 -9719.187 2020-06-02
5 2177176 -8034.767 -9810.801 2020-06-03
6 2177176 -8034.767 -9810.801 2020-06-03
strategy weight_divisor sector_Communication Services
1 strategy_1 1e+06 0.02025934
2 joint 1e+06 0.02025934
3 strategy_1 1e+06 0.02060105
4 joint 1e+06 0.02060105
5 strategy_1 1e+06 0.02209832
6 joint 1e+06 0.02209832
sector_Consumer Discretionary sector_Consumer Staples sector_Energy
1 -0.01926901 -0.02034710 0.02062658
2 -0.01926901 -0.02034710 0.02062658
3 -0.01981910 -0.01914917 0.02094451
4 -0.01981910 -0.01914917 0.02094451
5 -0.01799718 -0.02046579 0.02211464
6 -0.01799718 -0.02046579 0.02211464
sector_Financials sector_Health Care sector_Industrials
1 0.02090055 -0.01989795 -0.02034090
2 0.02090055 -0.01989795 -0.02034090
3 0.02204757 -0.02024579 -0.01983724
4 0.02204757 -0.02024579 -0.01983724
5 0.02670573 -0.02068899 -0.01887225
6 0.02670573 -0.02068899 -0.01887225
sector_Information Technology sector_Materials sector_Real Estate
1 -0.01992691 0.02008254 0.00027883
2 -0.01992691 0.02008254 0.00027883
3 -0.01973070 0.01643217 0.00712367
4 -0.01973070 0.01643217 0.00712367
5 -0.01727644 0.01510427 0.00791443
6 -0.01727644 0.01510427 0.00791443
sector_Utilities value size sim_date
1 0.02036772 1.567603 -0.01531873 2020-06-01
2 0.02036772 1.567603 -0.01531873 2020-06-01
3 0.02005078 2.014189 -0.01322212 2020-06-02
4 0.02005078 2.014189 -0.01322212 2020-06-02
5 0.02046454 2.249470 -0.01827253 2020-06-03
6 0.02046454 2.249470 -0.01827253 2020-06-03
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