Bot Trading Algorithm
Massimo Maffioletti
April 06, 2019
1 Introduction
The portfolio is managed by a (ro)bot trading algorithm, to test the trading strategies and the implementation of reinforcement learning. The algorithm autonomously buys and sells according to specific situations and conditions.
The buying and selling strategies are monitored and improved day by day.
The portfolio is composed by stocks traded on the Frankfurt Stock Exchange and the data are downloaded from Quandl.com, which is a platform for financial, economic and alternative data that serves investment professionals.
The portfolio was set up on 2018-02-12 and it is updated until the last trading day.
2 Bot Activity
Report activity of the Bot trading algorithm:
## [1] "On 2019-04-05, the Bot Algorithm did not buy or sell stocks."Â
Performances of the portfolio:
| 2019-04-05 | AmountInvested | Delta | %Return |
|---|---|---|---|
| 771.31 | 938.83 | -167.52 | -19.66 |
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On 2019-04-05, the portfolio is composed by the following stocks:
| Ticks | NumberShares | lastPrice | BuyPrice | Delta |
|---|---|---|---|---|
| DUE | 4 | 39.21 | 40.11 | -3.60 |
| GMM | 3 | 36.35 | 63.6 | -81.75 |
| GWI1 | 390 | 0.40 | 0.51 | -42.90 |
| PFV | 1 | 149.00 | 154.3 | -5.30 |
| TEG | 5 | 21.30 | 15.04 | 31.30 |
| ZO1 | 1 | 94.50 | 180.6 | -86.10 |
3 Trading history of the Portfolio
The following table displays the history of the stocks composing the portfolio:
| Stock | numberShares | BuyDate | PriceBuy | SellDate | PriceSell |
|---|---|---|---|---|---|
| EON | 10 | 2018-02-12 | 8.02 | 2018-05-30 | 9.08 |
| SBS | 3 | 2018-02-12 | 70.5 | 2018-03-16 | 70.3 |
| TEG | 5 | 2018-02-12 | 15.04 | ||
| FME | 2 | 2018-02-12 | 85.28 | 2018-03-06 | 83.44 |
| AIR | 2 | 2018-02-22 | 97.71 | 2018-03-05 | 93.89 |
| AIXA | 14 | 2018-02-23 | 13.65 | 2018-04-05 | 14.62 |
| SKYD | 13 | 2018-03-06 | 15.37 | 2018-03-07 | 15.3 |
| AB1 | 4319 | 2018-03-08 | 0.05 | 2018-03-19 | 0.04 |
| B5A | 8 | 2018-03-12 | 23.85 | 2018-03-21 | 20.2 |
| RWE | 10 | 2018-03-16 | 19.82 | 2018-03-27 | 19.68 |
| MOR | 2 | 2018-03-19 | 83.9 | 2018-03-28 | 84.15 |
| SGL | 16 | 2018-03-21 | 12.23 | 2018-08-15 | 11.95 |
| WAC | 7 | 2018-03-27 | 27.68 | 2018-04-11 | 27.8 |
| ADS | 1 | 2018-04-05 | 197.8 | 2018-04-26 | 202.3 |
| HOT | 1 | 2018-04-11 | 150 | 2018-05-07 | 151.3 |
| RHM | 1 | 2018-04-12 | 117.05 | 2018-05-09 | 117.1 |
| WCH | 1 | 2018-04-26 | 143.7 | 2018-05-14 | 151.4 |
| WDI | 1 | 2018-05-07 | 117.65 | 2018-05-24 | 126.65 |
| ZO1 | 1 | 2018-05-08 | 180.6 | ||
| SIX2 | 1 | 2018-05-14 | 114.2 | 2018-08-21 | 115.4 |
| PFV | 1 | 2018-05-24 | 150.4 | 2018-06-08 | 154.3 |
| WDI | 1 | 2018-05-30 | 129.25 | 2018-06-18 | 147.5 |
| PFV | 1 | 2018-06-08 | 154.3 | ||
| GMM | 3 | 2018-06-18 | 63.6 | ||
| SY1 | 2 | 2018-08-15 | 80.4 | 2018-09-03 | 80.46 |
| WDI | 1 | 2018-08-21 | 184.6 | 2018-09-11 | 186.25 |
| NEM | 1 | 2018-09-11 | 143.8 | 2018-09-20 | 146.2 |
| DEX | 22 | 2018-09-12 | 8.86 | 2018-09-21 | 8.94 |
| DUE | 4 | 2018-09-21 | 40.11 | ||
| QSC | 137 | 2018-09-24 | 1.46 | 2018-10-24 | 1.6 |
| JEN | 7 | 2018-10-24 | 25.92 | 2018-11-09 | 27.76 |
| P1Z | 10 | 2018-11-09 | 18.44 | 2019-01-22 | 18.68 |
| GSC1 | 7 | 2019-01-22 | 25.8 | 2019-02-26 | 25.9 |
| GWI1 | 390 | 2019-02-26 | 0.51 |
4 Outlook of Portfolio
The table shows the portfolio values day by day.
| 2019-04-05 | 2019-04-04 | 2019-04-03 | 2019-04-02 | 2019-04-01 | 2019-03-29 | 2019-03-28 | 2019-03-27 |
|---|---|---|---|---|---|---|---|
| 771.31 | 754.58 | 753.56 | 736.08 | 726.24 | 710.11 | 710.28 | 693.33 |
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Plot the development of the portfolio.
5 Outlook of stock prices
The table shows day by day the prices and returns of stocks composing the portfolio.
| 2019-04-05 | 2019-04-04 | 2019-04-03 | 2019-04-02 | 2019-04-01 | 2019-03-29 | 2019-03-28 | 2019-03-27 | ||
|---|---|---|---|---|---|---|---|---|---|
| TEG | price | 21.30 | 21.78 | 21.76 | 21.86 | 21.94 | 22.00 | 21.86 | 21.86 |
| %return | -2.23 | 0.09 | -0.46 | -0.37 | -0.27 | 0.64 | 0.00 | -0.46 | |
| ZO1 | price | 94.50 | 100.20 | 101.20 | 100.20 | 98.10 | 101.60 | 98.60 | 99.75 |
| %return | -5.86 | -0.99 | 0.99 | 2.12 | -3.51 | 3.00 | -1.16 | 1.41 | |
| PFV | price | 149.00 | 149.60 | 147.80 | 140.70 | 142.40 | 136.40 | 135.80 | 133.60 |
| %return | -0.40 | 1.21 | 4.92 | -1.20 | 4.30 | 0.44 | 1.63 | -2.66 | |
| GMM | price | 36.35 | 36.10 | 36.40 | 35.30 | 35.95 | 34.70 | 35.26 | 35.48 |
| %return | 0.69 | -0.83 | 3.07 | -1.82 | 3.54 | -1.60 | -0.62 | 1.30 | |
| DUE | price | 39.21 | 38.89 | 39.27 | 37.38 | 36.87 | 34.96 | 34.00 | 34.10 |
| %return | 0.82 | -0.97 | 4.93 | 1.37 | 5.32 | 2.78 | -0.29 | -0.18 | |
| GWI1 | price | 0.40 | 0.34 | 0.33 | 0.33 | 0.31 | 0.30 | 0.32 | 0.28 |
| %return | 16.32 | 1.94 | -0.75 | 7.77 | 2.12 | -5.46 | 14.61 | 2.38 |
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Plot the development of the stock prices in the portfolio.
6 Correlation matrix of stock prices
7 Market Risk
The table below displays the Value at Risk (VaR) and the Expected Shortfall (ES) that the portfolio can experience on 2019-04-08 with a probability at 99%.
The risk measures are calculated following a Monte Carlo simulation with a multivariate Student-t distribution.
| Risk Measures | Values in Euro |
|---|---|
| VaR | 35.71 |
| ES | 50.26 |
7.1 KiloVar calculation
The KiloVaR is a standard and official risk parameter adopted by UniCredit SpA to measure the degree of risk of financial instruments.
It assumes values from 1 to 1000 and lower values express a lower degree of risk.
| Methodology | KiloVaR |
|---|---|
| KiloVar UniCredit | 113/1000 |
| KiloVaR MonteCarlo | 3/1000 |
| KiloVar MonteCarlo NEW | 12/1000 |
Risk classes for the KiloVar parameter:
- ZERO RISK : KiloVar = 0
- LOW RISK : 1 < KiloVaR <= 7
- MIDDLE RISK : 7 < KiloVaR <= 15
- RISKY : 15 < KiloVaR <= 27
- HIGH RISK : 27 < KiloVaR <= 90
- SUPER RISKY : KiloVaR > 90