diff --git a/Result-Reports/Policies.md b/Result-Reports/Policies.md
index 5b851e0..0e947af 100644
--- a/Result-Reports/Policies.md
+++ b/Result-Reports/Policies.md
@@ -10,61 +10,113 @@ Determining 2 thresholds for battery charging and discharging. Charging when the
 Training data: 01-01-2020 - 31-12-2022
 Test data: 01-01-2023 - 12-12-2023
 
-|    |   Charge treshold |   Discharge treshold |   Charging Cost |   Discharging Profit |   Total Profit |   Charge cycles |   Mean charging price |   Mean discharging price |
-|---:|------------------:|---------------------:|----------------:|---------------------:|---------------:|----------------:|----------------------:|-------------------------:|
-|  0 |               150 |                  100 |        184072   |          1.03303e+06 |         848962 |        1884.62  |              32.4889  |                  332.002 |
-|  0 |               150 |                    0 |        184072   |          1.03303e+06 |         848962 |        1884.62  |              32.4889  |                  332.002 |
-|  0 |               150 |                 -100 |        184072   |          1.03303e+06 |         848962 |        1884.62  |              32.4889  |                  332.002 |
-|  0 |               150 |                  -50 |        184072   |          1.03303e+06 |         848962 |        1884.62  |              32.4889  |                  332.002 |
-|  0 |               150 |                   50 |        184072   |          1.03303e+06 |         848962 |        1884.62  |              32.4889  |                  332.002 |
-|  0 |               150 |                  150 |        182152   |          1.02598e+06 |         843824 |        1860.12  |              32.4889  |                  333.447 |
-|  0 |               200 |                  150 |        248326   |          1.0773e+06  |         828976 |        1695.5   |              42.9038  |                  367.058 |
-|  0 |               200 |                  100 |        248326   |          1.0773e+06  |         828976 |        1695.5   |              42.9038  |                  367.058 |
-|  0 |               200 |                   50 |        248326   |          1.0773e+06  |         828976 |        1695.5   |              42.9038  |                  367.058 |
-|  0 |               200 |                    0 |        248326   |          1.0773e+06  |         828976 |        1695.5   |              42.9038  |                  367.058 |
-|  0 |               200 |                  -50 |        248326   |          1.0773e+06  |         828976 |        1695.5   |              42.9038  |                  367.058 |
-|  0 |               200 |                 -100 |        248326   |          1.0773e+06  |         828976 |        1695.5   |              42.9038  |                  367.058 |
-|  0 |               200 |                  200 |        247560   |          1.07529e+06 |         827727 |        1690     |              42.9038  |                  367.44  |
-|  0 |               150 |                  200 |        151145   |     937599           |         786454 |        1514.88  |              32.4889  |                  367.44  |
-|  0 |               100 |                 -100 |         85591.8 |     865530           |         779938 |        1978.12  |              19.9739  |                  283.851 |
-|  0 |               100 |                   50 |         85591.8 |     865530           |         779938 |        1978.12  |              19.9739  |                  283.851 |
-|  0 |               100 |                    0 |         85591.8 |     865530           |         779938 |        1978.12  |              19.9739  |                  283.851 |
-|  0 |               100 |                  -50 |         85591.8 |     865530           |         779938 |        1978.12  |              19.9739  |                  283.851 |
-|  0 |               100 |                  100 |         84872.7 |     864043           |         779171 |        1966.62  |              19.9739  |                  284.61  |
-|  0 |               100 |                  150 |         63807.1 |     802437           |         738630 |        1502.5   |              19.9739  |                  333.447 |
-|  0 |                50 |                   50 |        -35789.2 |     689455           |         725244 |        2831     |              -9.65396 |                  204.943 |
-|  0 |                50 |                 -100 |        -35776.3 |     688933           |         724710 |        2833.88  |              -9.65396 |                  204.79  |
-|  0 |                50 |                    0 |        -35776.3 |     688933           |         724710 |        2833.88  |              -9.65396 |                  204.79  |
-|  0 |                50 |                  -50 |        -35776.3 |     688933           |         724710 |        2833.88  |              -9.65396 |                  204.79  |
-|  0 |               100 |                  200 |         49469.3 |     721380           |         671911 |        1196.12  |              19.9739  |                  367.44  |
-|  0 |                50 |                  100 |        -46110.5 |     592694           |         638805 |        1417.5   |              -9.65396 |                  284.61  |
-|  0 |                50 |                  150 |        -45858.6 |     531472           |         577330 |        1007.5   |              -9.65396 |                  333.447 |
-|  0 |                50 |                  200 |        -43738   |     473478           |         517216 |         779.25  |              -9.65396 |                  367.44  |
-|  0 |                 0 |                  100 |        -97804.3 |     341072           |         438876 |         761.5   |             -73.2011  |                  284.61  |
-|  0 |                 0 |                   50 |       -121468   |     314671           |         436139 |         993.625 |             -73.2011  |                  204.943 |
-|  0 |                 0 |                  150 |        -85919   |     341046           |         426964 |         635.75  |             -73.2011  |                  333.447 |
-|  0 |                 0 |                  200 |        -75822.2 |     328188           |         404010 |         541.25  |             -73.2011  |                  367.44  |
-|  0 |                 0 |                    0 |       -135954   |     259915           |         395869 |        1110.12  |             -73.2011  |                  146.436 |
-|  0 |                 0 |                 -100 |       -136936   |     251758           |         388694 |        1117.5   |             -73.2011  |                  145.226 |
-|  0 |                 0 |                  -50 |       -136936   |     251758           |         388694 |        1117.5   |             -73.2011  |                  145.226 |
-|  0 |               -50 |                  150 |        -97889.1 |     219525           |         317414 |         409.312 |            -127.845   |                  333.447 |
-|  0 |               -50 |                  100 |       -108274   |     208273           |         316548 |         458.312 |            -127.845   |                  284.61  |
-|  0 |               -50 |                  200 |        -87698.7 |     223235           |         310933 |         363.625 |            -127.845   |                  367.44  |
-|  0 |               -50 |                   50 |       -124664   |     184027           |         308691 |         525.688 |            -127.845   |                  204.943 |
-|  0 |               -50 |                    0 |       -131679   |     158118           |         289797 |         551.188 |            -127.845   |                  146.436 |
-|  0 |               -50 |                  -50 |       -138246   |     126218           |         264464 |         576.5   |            -127.845   |                  136.356 |
-|  0 |               -50 |                 -100 |       -138429   |     125059           |         263488 |         577.625 |            -127.845   |                  136.262 |
-|  0 |              -100 |                  200 |        -76499.7 |     137174           |         213674 |         219.625 |            -179.829   |                  367.44  |
-|  0 |              -100 |                  150 |        -82202.7 |     128215           |         210418 |         237.125 |            -179.829   |                  333.447 |
-|  0 |              -100 |                  100 |        -86319.5 |     117325           |         203645 |         250.25  |            -179.829   |                  284.61  |
-|  0 |              -100 |                   50 |        -94055.9 |      99443.5         |         193499 |         269.875 |            -179.829   |                  204.943 |
-|  0 |              -100 |                    0 |        -95713.7 |      87279.5         |         182993 |         274.625 |            -179.829   |                  146.436 |
-|  0 |              -100 |                  -50 |        -97722.9 |      71560.6         |         169283 |         280.375 |            -179.829   |                  136.356 |
-|  0 |              -100 |                 -100 |       -101133   |      45851.2         |         146985 |         289.375 |            -179.829   |                  130.542 |
+|    |   Charge threshold |   Discharge threshold |   Charging Cost |   Discharging Profit |   Total Profit |   Charge cycles |   Mean charging price |   Mean discharging price |
+|---:|-------------------:|----------------------:|----------------:|---------------------:|---------------:|----------------:|----------------------:|-------------------------:|
+|  0 |                150 |                   175 |       165982    |     989322           |         823340 |        1701.62  |              32.4889  |                  348.51  |
+|  0 |                175 |                   200 |       190060    |          1.00145e+06 |         811389 |        1594     |              37.0705  |                  367.44  |
+|  0 |                125 |                   150 |       132766    |     939145           |         806378 |        1729.88  |              27.7746  |                  333.447 |
+|  0 |                200 |                   225 |       219726    |          1.01789e+06 |         798159 |        1493.12  |              42.9038  |                  390.49  |
+|  0 |                150 |                   200 |       151145    |     937599           |         786454 |        1514.88  |              32.4889  |                  367.44  |
+|  0 |                125 |                   175 |       122067    |     904885           |         782818 |        1584.62  |              27.7746  |                  348.51  |
+|  0 |                175 |                   225 |       173050    |     950270           |         777219 |        1415     |              37.0705  |                  390.49  |
+|  0 |                100 |                   125 |        69876.4  |     829454           |         759577 |        1662.88  |              19.9739  |                  315.1   |
+|  0 |                150 |                   225 |       136563    |     886279           |         749716 |        1339.12  |              32.4889  |                  390.49  |
+|  0 |                125 |                   200 |       109873    |     853728           |         743855 |        1402.5   |              27.7746  |                  367.44  |
+|  0 |                100 |                   150 |        63807.1  |     802437           |         738630 |        1502.5   |              19.9739  |                  333.447 |
+|  0 |                100 |                   175 |        56405.8  |     770173           |         713768 |        1366.75  |              19.9739  |                  348.51  |
+|  0 |                 75 |                   100 |        14153.7  |     724660           |         710506 |        1684.5   |              11.3023  |                  284.61  |
+|  0 |                125 |                   225 |        97524.1  |     804468           |         706944 |        1231.88  |              27.7746  |                  390.49  |
+|  0 |                 25 |                    50 |       -56542.1  |     637628           |         694171 |        2696.12  |             -16.9846  |                  204.943 |
+|  0 |                 75 |                   125 |         8003.32 |     688780           |         680776 |        1407.38  |              11.3023  |                  315.1   |
+|  0 |                100 |                   200 |        49469.3  |     721380           |         671911 |        1196.12  |              19.9739  |                  367.44  |
+|  0 |                 50 |                    75 |       -47575.2  |     614964           |         662539 |        1752     |              -9.65396 |                  254.211 |
+|  0 |                 75 |                   150 |         5147.22 |     661439           |         656292 |        1254.62  |              11.3023  |                  333.447 |
+|  0 |                 50 |                   100 |       -46110.5  |     592694           |         638805 |        1417.5   |              -9.65396 |                  284.61  |
+|  0 |                100 |                   225 |        42046.6  |     677560           |         635514 |        1042.88  |              19.9739  |                  390.49  |
+|  0 |                 75 |                   175 |          850.49 |     631552           |         630702 |        1129     |              11.3023  |                  348.51  |
+|  0 |                 25 |                    75 |       -63342.8  |     561356           |         624699 |        1637     |             -16.9846  |                  254.211 |
+|  0 |                 50 |                   125 |       -46686.3  |     556263           |         602950 |        1150.5   |              -9.65396 |                  315.1   |
+|  0 |                 25 |                   100 |       -61448.6  |     537683           |         599132 |        1306.5   |             -16.9846  |                  284.61  |
+|  0 |                 75 |                   200 |        -1767.94 |     590094           |         591862 |         981.5   |              11.3023  |                  367.44  |
+|  0 |                 50 |                   150 |       -45858.6  |     531472           |         577330 |        1007.5   |              -9.65396 |                  333.447 |
+|  0 |                 25 |                   125 |       -61228.7  |     500861           |         562089 |        1045.62  |             -16.9846  |                  315.1   |
+|  0 |                 75 |                   225 |        -3475.71 |     555727           |         559203 |         855.875 |              11.3023  |                  390.49  |
+|  0 |                 50 |                   175 |       -46084.1  |     506348           |         552432 |         900.875 |              -9.65396 |                  348.51  |
+|  0 |                 25 |                   150 |       -60177.2  |     476823           |         537000 |         906.75  |             -16.9846  |                  333.447 |
+|  0 |                 50 |                   200 |       -43738    |     473478           |         517216 |         779.25  |              -9.65396 |                  367.44  |
+|  0 |                 25 |                   175 |       -59466.7  |     453988           |         513455 |         808.25  |             -16.9846  |                  348.51  |
+|  0 |                 50 |                   225 |       -40529.8  |     452651           |         493181 |         693.875 |              -9.65396 |                  390.49  |
+|  0 |                 25 |                   200 |       -55766.8  |     426055           |         481822 |         699.875 |             -16.9846  |                  367.44  |
+|  0 |                 25 |                   225 |       -51953    |     407484           |         459437 |         623.875 |             -16.9846  |                  390.49  |
+|  0 |                  0 |                    75 |      -107853    |     332344           |         440197 |         851.375 |             -73.2011  |                  254.211 |
+|  0 |                  0 |                   100 |       -97804.3  |     341072           |         438876 |         761.5   |             -73.2011  |                  284.61  |
+|  0 |                  0 |                    50 |      -121468    |     314671           |         436139 |         993.625 |             -73.2011  |                  204.943 |
+|  0 |                  0 |                   125 |       -91087.2  |     342646           |         433734 |         686.125 |             -73.2011  |                  315.1   |
+|  0 |                  0 |                    25 |      -126590    |     305984           |         432574 |        1041.75  |             -73.2011  |                  193.87  |
+|  0 |                  0 |                   150 |       -85919    |     341046           |         426964 |         635.75  |             -73.2011  |                  333.447 |
+|  0 |                  0 |                   175 |       -82378.2  |     338310           |         420688 |         600.625 |             -73.2011  |                  348.51  |
+|  0 |                  0 |                   200 |       -75822.2  |     328188           |         404010 |         541.25  |             -73.2011  |                  367.44  |
+|  0 |                  0 |                   225 |       -69488.7  |     320776           |         390265 |         494.75  |             -73.2011  |                  390.49  |
+|  0 |                -25 |                   125 |      -102495    |     269615           |         372110 |         534.062 |            -105.73    |                  315.1   |
+|  0 |                -25 |                   100 |      -108934    |     262527           |         371462 |         574.938 |            -105.73    |                  284.61  |
+|  0 |                -25 |                    75 |      -118553    |     252453           |         371006 |         626.062 |            -105.73    |                  254.211 |
+|  0 |                -25 |                   150 |       -97080    |     271209           |         368289 |         503.438 |            -105.73    |                  333.447 |
+|  0 |                -25 |                    50 |      -129918    |     236112           |         366030 |         683.562 |            -105.73    |                  204.943 |
+|  0 |                -25 |                   175 |       -92889.5  |     272520           |         365409 |         480.438 |            -105.73    |                  348.51  |
+|  0 |                -25 |                    25 |      -132518    |     227910           |         360428 |         695.562 |            -105.73    |                  193.87  |
+|  0 |                -25 |                   200 |       -85482.7  |     268638           |         354120 |         438.5   |            -105.73    |                  367.44  |
+|  0 |                -25 |                   225 |       -79253.2  |     264052           |         343305 |         403.75  |            -105.73    |                  390.49  |
+|  0 |                -25 |                     0 |      -139125    |     199831           |         338956 |         724.312 |            -105.73    |                  146.436 |
+|  0 |                -50 |                   125 |      -102665    |     216740           |         319405 |         431.562 |            -127.845   |                  315.1   |
+|  0 |                -50 |                   175 |       -94272.2  |     223434           |         317706 |         393.188 |            -127.845   |                  348.51  |
+|  0 |                -50 |                   150 |       -97889.1  |     219525           |         317414 |         409.312 |            -127.845   |                  333.447 |
+|  0 |                -50 |                   100 |      -108274    |     208273           |         316548 |         458.312 |            -127.845   |                  284.61  |
+|  0 |                -50 |                    75 |      -115707    |     198325           |         314032 |         488.438 |            -127.845   |                  254.211 |
+|  0 |                -50 |                   200 |       -87698.7  |     223235           |         310933 |         363.625 |            -127.845   |                  367.44  |
+|  0 |                -50 |                    50 |      -124664    |     184027           |         308691 |         525.688 |            -127.845   |                  204.943 |
+|  0 |                -50 |                    25 |      -126605    |     177834           |         304440 |         533.062 |            -127.845   |                  193.87  |
+|  0 |                -50 |                   225 |       -81706.8  |     221510           |         303217 |         337.75  |            -127.845   |                  390.49  |
+|  0 |                -50 |                     0 |      -131679    |     158118           |         289797 |         551.188 |            -127.845   |                  146.436 |
+|  0 |                -50 |                   -25 |      -135234    |     140945           |         276180 |         565     |            -127.845   |                  139.469 |
+|  0 |                -75 |                   175 |       -90764.9  |     184846           |         275611 |         325.812 |            -146.153   |                  348.51  |
+|  0 |                -75 |                   125 |       -98076.6  |     176722           |         274799 |         355.062 |            -146.153   |                  315.1   |
+|  0 |                -75 |                   150 |       -93979.3  |     179914           |         273893 |         338.188 |            -146.153   |                  333.447 |
+|  0 |                -75 |                   100 |      -103131    |     169294           |         272425 |         375.062 |            -146.153   |                  284.61  |
+|  0 |                -75 |                   200 |       -85036.8  |     186067           |         271104 |         302.375 |            -146.153   |                  367.44  |
+|  0 |                -75 |                    75 |      -109431    |     159779           |         269210 |         395.938 |            -146.153   |                  254.211 |
+|  0 |                -75 |                   225 |       -79978.8  |     185752           |         265731 |         282.75  |            -146.153   |                  390.49  |
+|  0 |                -75 |                    50 |      -116483    |     147147           |         263630 |         421.062 |            -146.153   |                  204.943 |
+|  0 |                -75 |                    25 |      -117886    |     141847           |         259734 |         424.812 |            -146.153   |                  193.87  |
+|  0 |                -75 |                     0 |      -120983    |     126071           |         247054 |         435.188 |            -146.153   |                  146.436 |
+|  0 |                -75 |                   -25 |      -123261    |     111930           |         235190 |         443.625 |            -146.153   |                  139.469 |
+|  0 |                -75 |                   -50 |      -124708    |     101672           |         226380 |         449.25  |            -146.153   |                  136.356 |
+|  0 |               -100 |                   200 |       -76499.7  |     137174           |         213674 |         219.625 |            -179.829   |                  367.44  |
+|  0 |               -100 |                   175 |       -79830.5  |     132966           |         212797 |         230.125 |            -179.829   |                  348.51  |
+|  0 |               -100 |                   225 |       -72449.9  |     138631           |         211081 |         207.625 |            -179.829   |                  390.49  |
+|  0 |               -100 |                   150 |       -82202.7  |     128215           |         210418 |         237.125 |            -179.829   |                  333.447 |
+|  0 |               -100 |                   125 |       -84192.7  |     123976           |         208169 |         243.625 |            -179.829   |                  315.1   |
+|  0 |               -100 |                   100 |       -86319.5  |     117325           |         203645 |         250.25  |            -179.829   |                  284.61  |
+|  0 |               -100 |                    75 |       -90219.7  |     109153           |         199373 |         259.25  |            -179.829   |                  254.211 |
+|  0 |               -100 |                    50 |       -94055.9  |      99443.5         |         193499 |         269.875 |            -179.829   |                  204.943 |
+|  0 |               -100 |                    25 |       -94464.1  |      96073.2         |         190537 |         270.875 |            -179.829   |                  193.87  |
+|  0 |               -100 |                     0 |       -95713.7  |      87279.5         |         182993 |         274.625 |            -179.829   |                  146.436 |
+|  0 |               -100 |                   -25 |       -97020.3  |      78656.5         |         175677 |         278.25  |            -179.829   |                  139.469 |
+|  0 |               -100 |                   -50 |       -97722.9  |      71560.6         |         169283 |         280.375 |            -179.829   |                  136.356 |
+|  0 |               -100 |                   -75 |       -98604.6  |      63373.8         |         161978 |         282.625 |            -179.829   |                  134.012 |
 
 ## Optimal policy
 Charge threshold: 150 €/MWh \
-Discharge threshold: 100 €/MWh
+Discharge threshold: 175 €/MWh
 
-Profit on test data: € 169846.84 \
-Charge cycles: 1403
\ No newline at end of file
+Profit on test data: € 359548 \
+Charge cycles: 855.938
+
+
+# Smarter Policy using predicted NRV
+Test data: 01-01-2023 until 08-10–2023
+
+| Policy | Threshold Step Size (€) | Total Profit (€) | Charge Cycles |
+|--------|---------------------|--------------|---------------|
+| Baseline (charge: 150, discharge: 175) | 25 | 251202.59 | 725 |
+| Policy using predicted NRV | 50 | 325362.81 | 856 |
+| Policy using predicted NRV  | 25 | 334058.65 | 862 |
\ No newline at end of file
diff --git a/src/data/dataset.py b/src/data/dataset.py
index 1e87a3b..cf4ff89 100644
--- a/src/data/dataset.py
+++ b/src/data/dataset.py
@@ -207,4 +207,11 @@ class NrvDataset(Dataset):
         return torch.stack(features), torch.stack(targets)
 
     def get_idx_for_date(self, date: datetime.date):
-        return self.datetime[self.datetime.dt.date == date].index[0]
\ No newline at end of file
+        # check if the date is in the valid indices
+        if date not in self.datetime.dt.date.unique():
+            raise ValueError(f"Date {date} not in dataset.")
+        
+        idx = self.datetime[self.datetime.dt.date == date].index[0]
+
+        valid_idx = self.valid_indices.index(idx)
+        return valid_idx
\ No newline at end of file
diff --git a/src/policies/policy_tester.ipynb b/src/policies/policy_tester.ipynb
index 36a92f5..e715588 100644
--- a/src/policies/policy_tester.ipynb
+++ b/src/policies/policy_tester.ipynb
@@ -2,13 +2,15 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n",
       "Index(['datetime', 'nrv', 'load_forecast', 'total_load', 'wind_forecast',\n",
       "       'wind_history', 'nominal_net_position', 'quarter', 'day_of_week'],\n",
       "      dtype='object')\n",
@@ -35,6 +37,12 @@
     "import datetime\n",
     "from scipy.interpolate import CubicSpline\n",
     "import matplotlib.patches as mpatches\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# auto reload\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
     "\n",
     "task = Task.get_task(task_id='21ea327daf4b48a7967376743d6054ad')\n",
     "# get the data_features configuration\n",
@@ -82,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -120,7 +128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -422,6 +430,459 @@
     "    plot_for_date(datetime.datetime(2023, month, day))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Determine threshold based on predictions\n",
+    "def get_predicted_NRV(date):\n",
+    "    idx = test_loader.dataset.get_idx_for_date(date.date())\n",
+    "    initial, _, samples, target = auto_regressive(test_loader.dataset, [idx]*1000, 96)\n",
+    "    samples = samples.cpu().numpy()\n",
+    "    target = target.cpu().numpy()\n",
+    "\n",
+    "    # inverse using data_processor\n",
+    "    samples = data_processor.inverse_transform(samples)\n",
+    "    target = data_processor.inverse_transform(target)\n",
+    "\n",
+    "    mean = samples.mean(axis=0)\n",
+    "    return initial.cpu().numpy()[0][-1], mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training range: 01-01-2020 - 31-12-2022\n",
+      "Test range: 01-01-2023 - 12-12-2023\n"
+     ]
+    }
+   ],
+   "source": [
+    "from src.utils.imbalance_price_calculator import ImbalancePriceCalculator\n",
+    "ipc = ImbalancePriceCalculator()\n",
+    "\n",
+    "from src.policies.simple_baseline import BaselinePolicy, Battery\n",
+    "\n",
+    "imbalance_prices = pd.read_csv('../../data/imbalance_prices.csv', sep=';')\n",
+    "imbalance_prices[\"DateTime\"] = pd.to_datetime(imbalance_prices['DateTime'], utc=True)\n",
+    "# sort ascending\n",
+    "imbalance_prices = imbalance_prices.sort_values(by=['DateTime'])\n",
+    "\n",
+    "battery = Battery(2, 1)\n",
+    "baseline_policy = BaselinePolicy(battery, data_path=\"../../\")\n",
+    "\n",
+    "def get_imbalance_prices(date):\n",
+    "    imbalance_prices_day = imbalance_prices[imbalance_prices[\"DateTime\"].dt.date == date]\n",
+    "    return imbalance_prices_day['Positive imbalance price'].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "\n",
+    "def plot_predicted_imbalance_prices(date):\n",
+    "    initial, mean_nrv = get_predicted_NRV(date)\n",
+    "    real_imbalance_prices = get_imbalance_prices(date.date())\n",
+    "    reconstructed_imbalance_price = ipc.get_imbalance_prices_2023_for_date(date, [initial] + list(mean_nrv))\n",
+    "\n",
+    "    time_steps = np.arange(0, 96)\n",
+    "    fig, ax = plt.subplots(figsize=(20, 10))\n",
+    "    ax.plot(time_steps, real_imbalance_prices, label=\"Real imbalance prices\", linewidth=3)\n",
+    "    ax.plot(time_steps, reconstructed_imbalance_price, label=\"Predicted imbalance prices\", linewidth=3)\n",
+    "    ax.legend()\n",
+    "    ax.set_title(f\"Prediction for {date.date()}\")\n",
+    "    ax.set_xlabel(\"Time steps\")\n",
+    "    ax.set_ylabel(\"Imbalance price [EUR/MWh]\")\n",
+    "\n",
+    "    plt.show()\n",
+    "\n",
+    "def get_optimal_thresholds(date) -> pd.DataFrame:\n",
+    "\n",
+    "    charge_thresholds = np.arange(-100, 250, 50)\n",
+    "    discharge_thresholds = np.arange(-100, 250, 50)\n",
+    "\n",
+    "    initial, mean_nrv = get_predicted_NRV(date)\n",
+    "    real_imbalance_prices = get_imbalance_prices(date.date())\n",
+    "    reconstructed_imbalance_price = ipc.get_imbalance_prices_2023_for_date(date, [initial] + list(mean_nrv))\n",
+    "\n",
+    "    # get the optimal thresholds\n",
+    "    return baseline_policy.get_optimal_thresholds(reconstructed_imbalance_price, charge_thresholds, discharge_thresholds)\n",
+    "\n",
+    "\n",
+    "def simulate(date, charge_threshold, discharge_threshold):\n",
+    "    initial, mean_nrv = get_predicted_NRV(date)\n",
+    "    reconstructed_imbalance_price = ipc.get_imbalance_prices_2023_for_date(date, [initial] + list(mean_nrv))\n",
+    "    \n",
+    "    real_imbalance_prices = get_imbalance_prices(date.date())\n",
+    "\n",
+    "    reconstructed_profit = baseline_policy.simulate(reconstructed_imbalance_price, charge_threshold, discharge_threshold)\n",
+    "    real_profit = baseline_policy.simulate(real_imbalance_prices, charge_threshold, discharge_threshold)\n",
+    "\n",
+    "    return reconstructed_profit, real_profit\n",
+    "\n",
+    "def get_next_day_profits_for_date(date, global_charging_threshold: float = None, global_discharging_threshold:float = None, print_results=True):\n",
+    "    initial, mean_nrv = get_predicted_NRV(date)\n",
+    "    reconstructed_imbalance_price = ipc.get_imbalance_prices_2023_for_date(date, [initial] + list(mean_nrv))\n",
+    "    \n",
+    "    real_imbalance_prices = get_imbalance_prices(date.date())\n",
+    "\n",
+    "    charge_thresholds = np.arange(-100, 250, 25)\n",
+    "    discharge_thresholds = np.arange(-100, 250, 25)\n",
+    "\n",
+    "    if print_results:\n",
+    "        print(\"Determining optimal thresholds...\")\n",
+    "\n",
+    "    # determine day ahead thresholds\n",
+    "    df = baseline_policy.get_optimal_thresholds(reconstructed_imbalance_price, charge_thresholds, discharge_thresholds, tqdm=False)\n",
+    "\n",
+    "    # get best ones, sort descending \"Total Profit\"\n",
+    "    df = df.sort_values(by=['Total Profit'], ascending=False)\n",
+    "    next_day_charge_threshold = df[\"Charge threshold\"].values[0]\n",
+    "    next_day_discharge_threshold = df[\"Discharge threshold\"].values[0]\n",
+    "\n",
+    "    if print_results:\n",
+    "        print(f\"Next day charge threshold: {next_day_charge_threshold}\")\n",
+    "        print(f\"Next day discharge threshold: {next_day_discharge_threshold}\\n\")\n",
+    "\n",
+    "    # get the profit for the day ahead thresholds on the real imbalance prices\n",
+    "    next_day_profit, next_day_charge_cycles = baseline_policy.simulate(real_imbalance_prices, next_day_charge_threshold, next_day_discharge_threshold)\n",
+    "    if global_charging_threshold is not None and global_discharging_threshold is not None:\n",
+    "        global_profit, global_charge_cycles = baseline_policy.simulate(real_imbalance_prices, global_charging_threshold, global_discharging_threshold)\n",
+    "    else:\n",
+    "        return next_day_profit, next_day_charge_cycles\n",
+    "    return (next_day_profit, next_day_charge_cycles), (global_profit, global_charge_cycles)\n",
+    "\n",
+    "def next_day_test_set():\n",
+    "    next_day_total_charge_cycles = 0\n",
+    "    next_day_total_profit = 0\n",
+    "\n",
+    "    global_total_charge_cycles = 0\n",
+    "    global_total_profit = 0\n",
+    "\n",
+    "    # get all dates in test set\n",
+    "    dates = baseline_policy.test_data[\"DateTime\"].dt.date.unique()\n",
+    "\n",
+    "    # dates back to datetime\n",
+    "    dates = pd.to_datetime(dates)\n",
+    "\n",
+    "    for date in tqdm(dates):\n",
+    "        try:\n",
+    "            (next_day_profit, charge_cycles), (global_profit, global_charge_cycles) = get_next_day_profits_for_date(date, 150, 175, print_results=False)\n",
+    "            next_day_total_profit += next_day_profit\n",
+    "            next_day_total_charge_cycles += charge_cycles\n",
+    "\n",
+    "            global_total_profit += global_profit\n",
+    "            global_total_charge_cycles += global_charge_cycles\n",
+    "        except:\n",
+    "            print(f\"Error for date {date}\")\n",
+    "            continue\n",
+    "\n",
+    "    return (next_day_total_profit, next_day_total_charge_cycles), (global_total_profit, global_total_charge_cycles)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(96,)\n"
+     ]
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "random_month = np.random.randint(1, 12)\n",
+    "random_day = np.random.randint(1, 29)\n",
+    "date = datetime.datetime(2023, random_month, random_day)\n",
+    "plot_predicted_imbalance_prices(date)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|          | 0/346 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 27%|██▋       | 94/346 [09:11<24:37,  5.86s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error for date 2023-04-05 00:00:00\n",
+      "Error for date 2023-04-06 00:00:00\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 49%|████▉     | 170/346 [16:23<17:12,  5.87s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error for date 2023-06-20 00:00:00\n",
+      "Error for date 2023-06-21 00:00:00\n",
+      "Error for date 2023-06-22 00:00:00\n",
+      "Error for date 2023-06-23 00:00:00\n",
+      "Error for date 2023-06-24 00:00:00\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 64%|██████▎   | 220/346 [20:45<12:26,  5.92s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error for date 2023-08-09 00:00:00\n",
+      "Error for date 2023-08-10 00:00:00\n",
+      "Error for date 2023-08-11 00:00:00\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 65%|██████▍   | 224/346 [20:51<06:18,  3.10s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error for date 2023-08-13 00:00:00\n",
+      "Error for date 2023-08-14 00:00:00\n",
+      "Error for date 2023-08-15 00:00:00\n",
+      "Error for date 2023-08-16 00:00:00\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 71%|███████   | 246/346 [22:37<09:40,  5.81s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error for date 2023-09-04 00:00:00\n",
+      "Error for date 2023-09-05 00:00:00\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 73%|███████▎  | 252/346 [23:00<07:33,  4.82s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error for date 2023-09-10 00:00:00\n",
+      "Error for date 2023-09-11 00:00:00\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 77%|███████▋  | 266/346 [24:10<07:42,  5.78s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error for date 2023-09-24 00:00:00\n",
+      "Error for date 2023-09-25 00:00:00\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 78%|███████▊  | 270/346 [24:22<05:14,  4.14s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error for date 2023-09-28 00:00:00\n",
+      "Error for date 2023-09-29 00:00:00\n",
+      "Error for date 2023-09-30 00:00:00\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 81%|████████  | 280/346 [25:03<05:36,  5.10s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error for date 2023-10-08 00:00:00\n",
+      "Error for date 2023-10-09 00:00:00\n",
+      "Error for date 2023-10-10 00:00:00\n",
+      "Error for date 2023-10-11 00:00:00\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 89%|████████▉ | 309/346 [25:20<00:19,  1.90it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error for date 2023-10-14 00:00:00\n",
+      "Error for date 2023-10-15 00:00:00\n",
+      "Error for date 2023-10-16 00:00:00\n",
+      "Error for date 2023-10-17 00:00:00\n",
+      "Error for date 2023-10-18 00:00:00\n",
+      "Error for date 2023-10-19 00:00:00\n",
+      "Error for date 2023-10-20 00:00:00\n",
+      "Error for date 2023-10-21 00:00:00\n",
+      "Error for date 2023-10-22 00:00:00\n",
+      "Error for date 2023-10-23 00:00:00\n",
+      "Error for date 2023-10-24 00:00:00\n",
+      "Error for date 2023-10-25 00:00:00\n",
+      "Error for date 2023-10-26 00:00:00\n",
+      "Error for date 2023-10-27 00:00:00\n",
+      "Error for date 2023-10-28 00:00:00\n",
+      "Error for date 2023-10-29 00:00:00\n",
+      "Error for date 2023-10-30 00:00:00\n",
+      "Error for date 2023-10-31 00:00:00\n",
+      "Error for date 2023-11-01 00:00:00\n",
+      "Error for date 2023-11-02 00:00:00\n",
+      "Error for date 2023-11-03 00:00:00\n",
+      "Error for date 2023-11-04 00:00:00\n",
+      "Error for date 2023-11-05 00:00:00\n",
+      "Error for date 2023-11-06 00:00:00\n",
+      "Error for date 2023-11-07 00:00:00\n",
+      "Error for date 2023-11-08 00:00:00\n",
+      "Error for date 2023-11-09 00:00:00\n",
+      "Error for date 2023-11-10 00:00:00\n",
+      "Error for date 2023-11-11 00:00:00\n",
+      "Error for date 2023-11-12 00:00:00\n",
+      "Error for date 2023-11-13 00:00:00\n",
+      "Error for date 2023-11-14 00:00:00\n",
+      "Error for date 2023-11-15 00:00:00\n",
+      "Error for date 2023-11-16 00:00:00\n",
+      "Error for date 2023-11-17 00:00:00\n",
+      "Error for date 2023-11-18 00:00:00\n",
+      "Error for date 2023-11-19 00:00:00\n",
+      "Error for date 2023-11-20 00:00:00\n",
+      "Error for date 2023-11-21 00:00:00\n",
+      "Error for date 2023-11-22 00:00:00\n",
+      "Error for date 2023-11-23 00:00:00\n",
+      "Error for date 2023-11-24 00:00:00\n",
+      "Error for date 2023-11-25 00:00:00\n",
+      "Error for date 2023-11-26 00:00:00\n",
+      "Error for date 2023-11-27 00:00:00\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 346/346 [25:21<00:00,  4.40s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error for date 2023-11-28 00:00:00\n",
+      "Error for date 2023-11-29 00:00:00\n",
+      "Error for date 2023-11-30 00:00:00\n",
+      "Error for date 2023-12-01 00:00:00\n",
+      "Error for date 2023-12-02 00:00:00\n",
+      "Error for date 2023-12-03 00:00:00\n",
+      "Error for date 2023-12-04 00:00:00\n",
+      "Error for date 2023-12-05 00:00:00\n",
+      "Error for date 2023-12-06 00:00:00\n",
+      "Error for date 2023-12-07 00:00:00\n",
+      "Error for date 2023-12-08 00:00:00\n",
+      "Error for date 2023-12-09 00:00:00\n",
+      "Error for date 2023-12-10 00:00:00\n",
+      "Error for date 2023-12-11 00:00:00\n",
+      "Error for date 2023-12-12 00:00:00\n",
+      "Next day profit: (334058.6550000004, 861.9375)\n",
+      "Global profit: (251202.59, 724.125)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "next_day_profit, global_profit = next_day_test_set()\n",
+    "\n",
+    "print(f\"Next day profit: {next_day_profit}\")\n",
+    "print(f\"Global profit: {global_profit}\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/src/policies/simple_baseline.py b/src/policies/simple_baseline.py
index e0ff4e3..2386919 100644
--- a/src/policies/simple_baseline.py
+++ b/src/policies/simple_baseline.py
@@ -57,7 +57,8 @@ class Battery:
         self.charge_cycles = 0
 
 class BaselinePolicy():
-    def __init__(self, battery: Battery):
+    def __init__(self, battery: Battery, data_path: str = ""):
+        self.data_path = data_path
         self.battery = battery
         self.train_data = self.load_imbalance_prices(train=True)        
         self.test_data = self.load_imbalance_prices(train=False)
@@ -67,7 +68,7 @@ class BaselinePolicy():
         print(f"Test range: {self.test_data.iloc[0]['DateTime'].strftime('%d-%m-%Y')} - {self.test_data.iloc[-1]['DateTime'].strftime('%d-%m-%Y')}")
 
     def load_imbalance_prices(self, train: bool = True):
-        imbalance_prices = pd.read_csv('data/imbalance_prices.csv', parse_dates=True, sep=";")
+        imbalance_prices = pd.read_csv(self.data_path + 'data/imbalance_prices.csv', parse_dates=True, sep=";")
         imbalance_prices = imbalance_prices[['DateTime', 'Positive imbalance price']]
         imbalance_prices['DateTime'] = pd.to_datetime(imbalance_prices['DateTime'], utc=True)
         if train:
@@ -78,15 +79,15 @@ class BaselinePolicy():
         imbalance_prices = imbalance_prices.sort_values(by=['DateTime'], ascending=True)
         return imbalance_prices
     
-    def get_train_score(self, charge_treshold, discharge_treshold):
-        return self.get_score(self.train_data, charge_treshold, discharge_treshold)
+    def get_train_score(self, charge_threshold, discharge_threshold):
+        return self.get_score(self.train_data, charge_threshold, discharge_threshold)
     
-    def get_test_score(self, charge_treshold, discharge_treshold):
-        return self.get_score(self.test_data, charge_treshold, discharge_treshold)
+    def get_test_score(self, charge_threshold, discharge_threshold):
+        return self.get_score(self.test_data, charge_threshold, discharge_threshold)
 
-    # if price is below charging treshold (cheap charging) -> charge battery: total_profit -= charge * price
-    # if price is above treshold -> discharge battery: total_profit += discharge * price
-    def get_score(self, df, charge_treshold, discharge_treshold):
+    # if price is below charging threshold (cheap charging) -> charge battery: total_profit -= charge * price
+    # if price is above threshold -> discharge battery: total_profit += discharge * price
+    def get_score(self, df, charge_threshold, discharge_threshold):
         self.battery.reset()
         total_charging_cost = 0
         total_discharging_profit = 0
@@ -96,41 +97,141 @@ class BaselinePolicy():
         number_of_discharges = 0
     
         for index, row in df.iterrows():
-            if row['Positive imbalance price'] < charge_treshold:
+            if row['Positive imbalance price'] < charge_threshold:
                 total_charging_cost += self.battery.charge() * row['Positive imbalance price']
                 mean_charging_price += row['Positive imbalance price']
                 number_of_charges += 1
-            elif row['Positive imbalance price'] > discharge_treshold:
+            elif row['Positive imbalance price'] > discharge_threshold:
                 total_discharging_profit += self.battery.discharge() * row['Positive imbalance price']
                 mean_discharging_price += row['Positive imbalance price']
                 number_of_discharges += 1
 
         return total_charging_cost, total_discharging_profit, self.battery.charge_cycles, mean_charging_price / number_of_charges, mean_discharging_price / number_of_discharges
 
-    def treshold_scores(self, charge_tresholds, discharge_tresholds):
-        df = pd.DataFrame(columns=["Charge treshold", "Discharge treshold", "Charging Cost", "Discharging Profit", "Total Profit", "Charge cycles", "Mean charging price", "Mean discharging price"])
-        df_test = pd.DataFrame(columns=["Charge treshold", "Discharge treshold", "Charging Cost", "Discharging Profit", "Total Profit", "Charge cycles", "Mean charging price", "Mean discharging price"])
-        for charge_treshold, discharge_treshold in tqdm(itertools.product(charge_tresholds, discharge_tresholds)):
-                total_charging_cost, total_discharge_profit, charge_cycles, mean_charging_price, mean_discharging_price = self.get_train_score(charge_treshold, discharge_treshold)
-                df = pd.concat([df, pd.DataFrame([[charge_treshold, discharge_treshold, total_charging_cost, total_discharge_profit, total_discharge_profit - total_charging_cost, charge_cycles, mean_charging_price, mean_discharging_price]], columns=["Charge treshold", "Discharge treshold", "Charging Cost", "Discharging Profit", "Total Profit", "Charge cycles", "Mean charging price", "Mean discharging price"])])
+    def threshold_scores(self, charge_thresholds, discharge_thresholds):
+        df = pd.DataFrame(columns=["Charge threshold", "Discharge threshold", "Charging Cost", "Discharging Profit", "Total Profit", "Charge cycles", "Mean charging price", "Mean discharging price"])
+        df_test = pd.DataFrame(columns=["Charge threshold", "Discharge threshold", "Charging Cost", "Discharging Profit", "Total Profit", "Charge cycles", "Mean charging price", "Mean discharging price"])
 
-                total_charging_cost, total_discharge_profit, charge_cycles, mean_charging_price, mean_discharging_price = self.get_test_score(charge_treshold, discharge_treshold)
-                df_test = pd.concat([df_test, pd.DataFrame([[charge_treshold, discharge_treshold, total_charging_cost, total_discharge_profit, total_discharge_profit - total_charging_cost, charge_cycles, mean_charging_price, mean_discharging_price]], columns=["Charge treshold", "Discharge treshold", "Charging Cost", "Discharging Profit", "Total Profit", "Charge cycles", "Mean charging price", "Mean discharging price"])])
+        threshold_pairs = itertools.product(charge_thresholds, discharge_thresholds)
+        threshold_pairs = filter(lambda x: x[0] < x[1], threshold_pairs)
+
+        for charge_threshold, discharge_threshold in tqdm(threshold_pairs):
+                total_charging_cost, total_discharge_profit, charge_cycles, mean_charging_price, mean_discharging_price = self.get_train_score(charge_threshold, discharge_threshold)
+                df = pd.concat([df, pd.DataFrame([[charge_threshold, discharge_threshold, total_charging_cost, total_discharge_profit, total_discharge_profit - total_charging_cost, charge_cycles, mean_charging_price, mean_discharging_price]], columns=["Charge threshold", "Discharge threshold", "Charging Cost", "Discharging Profit", "Total Profit", "Charge cycles", "Mean charging price", "Mean discharging price"])])
+
+                total_charging_cost, total_discharge_profit, charge_cycles, mean_charging_price, mean_discharging_price = self.get_test_score(charge_threshold, discharge_threshold)
+                df_test = pd.concat([df_test, pd.DataFrame([[charge_threshold, discharge_threshold, total_charging_cost, total_discharge_profit, total_discharge_profit - total_charging_cost, charge_cycles, mean_charging_price, mean_discharging_price]], columns=["Charge threshold", "Discharge threshold", "Charging Cost", "Discharging Profit", "Total Profit", "Charge cycles", "Mean charging price", "Mean discharging price"])])
 
         df = df.sort_values(by=['Total Profit'], ascending=False)
 
         return df, df_test
+    
+    def get_optimal_thresholds(self, imbalance_prices, charge_thresholds, discharge_thresholds, tqdm=True):
+        df = None
+
+        threshold_pairs = itertools.product(charge_thresholds, discharge_thresholds)
+        threshold_pairs = filter(lambda x: x[0] < x[1], threshold_pairs)
+
+        # disable tqdm if necessary
+        if tqdm:
+            threshold_pairs = tqdm(threshold_pairs)
+
+        for charge_threshold, discharge_threshold in threshold_pairs:
+            self.battery.reset()
+            total_charging_cost = 0
+            total_discharging_profit = 0
+            number_of_charges = 0
+            number_of_discharges = 0
+            mean_charging_price = 0
+            mean_discharging_price = 0
+
+            for index, price in enumerate(imbalance_prices):
+                if price < charge_threshold:
+                    total_charging_cost += self.battery.charge() * price
+                    mean_charging_price += price
+                    number_of_charges += 1
+                elif price > discharge_threshold:
+                    total_discharging_profit += self.battery.discharge() * price
+                    mean_discharging_price += price
+                    number_of_discharges += 1
+
+            if number_of_charges == 0:
+                mean_charging_price = 0
+            else:
+                mean_charging_price /= number_of_charges
+
+            if number_of_discharges == 0:
+                mean_discharging_price = 0
+            else:
+                mean_discharging_price /= number_of_discharges
+
+            new_df = pd.DataFrame([[charge_threshold, discharge_threshold, total_charging_cost, total_discharging_profit, total_discharging_profit - total_charging_cost, self.battery.charge_cycles, mean_charging_price, mean_discharging_price]], columns=["Charge threshold", "Discharge threshold", "Charging Cost", "Discharging Profit", "Total Profit", "Charge cycles", "Mean charging price", "Mean discharging price"])
+            if df is None:
+                df = new_df
+            else:
+                df = pd.concat([df, new_df])
+
+        df = df.sort_values(by=['Total Profit'], ascending=False)
+
+        return df
+    
+    def simulate(self, imbalance_prices, charge_threshold, discharge_threshold):
+        self.battery.reset()
+        total_charging_cost = 0
+        total_discharging_profit = 0
+        number_of_charges = 0
+        number_of_discharges = 0
+        mean_charging_price = 0
+        mean_discharging_price = 0
+
+        # for each timestep also print what is happening
+        for i, price in enumerate(imbalance_prices):
+            if price < charge_threshold:
+                charge = self.battery.charge()
+                total_charging_cost += charge * price
+                mean_charging_price += price
+                number_of_charges += 1
+                # print(f"{i}: Charging battery with {charge}MWh at {price}€/MWh")
+                
+            elif price > discharge_threshold:
+                discharge = self.battery.discharge()
+                total_discharging_profit += discharge * price
+                mean_discharging_price += price
+                number_of_discharges += 1
+                # print(f"{i}: Discharging battery with {discharge}MWh at {price}€/MWh")
+
+        if number_of_charges == 0:
+            mean_charging_price = 0
+        else:
+            mean_charging_price /= number_of_charges
+
+        if number_of_discharges == 0:
+            mean_discharging_price = 0
+        else:
+            mean_discharging_price /= number_of_discharges
+
+        # print(f"Total charging cost: {total_charging_cost}")
+        # print(f"Total discharging profit: {total_discharging_profit}")
+        # print(f"Total profit: {total_discharging_profit - total_charging_cost}")
+        # print(f"Charge cycles: {self.battery.charge_cycles}")
+        # print(f"Mean charging price: {mean_charging_price}")
+        # print(f"Mean discharging price: {mean_discharging_price}")
+
+        return total_discharging_profit - total_charging_cost, self.battery.charge_cycles
+
+        
                 
     
-battery = Battery(2, 1)
-policy = BaselinePolicy(battery)
+# battery = Battery(2, 1)
+# policy = BaselinePolicy(battery)
 
-charge_tresholds = np.arange(-100, 250, 50)
-discharge_tresholds = np.arange(-100, 250, 50)
 
-df, df_test = policy.treshold_scores(charge_tresholds, discharge_tresholds)
-print(df.to_markdown())
+# charge_thresholds = np.arange(-100, 250, 25)
+# discharge_thresholds = np.arange(-100, 250, 25)
 
-print(df_test.to_markdown())
+# df, df_test = policy.threshold_scores(charge_thresholds, discharge_thresholds)
+# print(df.to_markdown())
 
-# print(policy.get_test_score(150, 100))
\ No newline at end of file
+# print(df_test.to_markdown())
+
+# # print(policy.get_test_score(150, 100))
\ No newline at end of file
diff --git a/src/utils/imbalance_price_calculator.py b/src/utils/imbalance_price_calculator.py
index 4e6a924..04aa5a8 100644
--- a/src/utils/imbalance_price_calculator.py
+++ b/src/utils/imbalance_price_calculator.py
@@ -1,6 +1,6 @@
-from datetime import datetime
 import plotly.graph_objects as go
 import numpy as np
+import pytz
 import pandas as pd
 
 incremental_bids = "../../data/incremental_bids.csv"
@@ -66,8 +66,6 @@ class ImbalancePriceCalculator:
         dec_bids = row["bid_ladder_dec"].values[0]
         inc_bids = row["bid_ladder_inc"].values[0]
         
-
-
         # Prepare data for plot
         x_inc_interpolated = [vol for i in range(len(inc_bids) - 1) for vol in [inc_bids[i][0], inc_bids[i+1][0]]]
         y_inc_interpolated = [price for cum_vol, price in inc_bids for _ in (0, 1)]
@@ -90,6 +88,14 @@ class ImbalancePriceCalculator:
 
         fig.show()
 
+    def get_imbalance_prices_2023_for_date(self, date, NRV_predictions):
+        imbalance_prices = []
+        for i in range(1, len(NRV_predictions)):
+            datetime = date + pd.Timedelta(hours=i-1)
+            datetime = pytz.utc.localize(datetime)
+            imbalance_prices.append(self.get_imbalance_price_2023(datetime, NRV_predictions[i-1], NRV_predictions[i]))
+        return [x[1] for x in imbalance_prices]
+
     def get_imbalance_price_2023(self, datetime, NRV_PREV, NRV):
         MIP = self.get_imbalance_price(datetime, abs(NRV))
         MDP = self.get_imbalance_price(datetime, -abs(NRV))