Stock Maximize (Python)

Your algorithms have become so good at predicting the market that you now know what the share price of Latency Critical Trading Inc. (LCT) will be for the next (F) minutes. Each minute, your high-frequency trading platform allows you to:

1. Buy one share of LCT.
2. Sell one or more shares of LCT that you own (you can never sell share(s) without having bought them first).
3. Not make any transaction at all.

What is the maximum profit you can obtain with an optimum trading strategy?

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Input:

The first line contains the number of test cases (T). (T) test cases follow, each consisting of two lines:

1. The first line of each test case contains a number (N).
2. The next line contains (N) integers, denoting the predicted price (P1 through PN) of LCT shares for the next (F) minutes.

T  
N1  
P1 P2 P3 P4 ... PN1  
...  
Nn  
P1 P2 P3 P4 ... PNn  

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Output:

For each test case, output the maximum profit you can make with the given prices, one value per line:

PNL1
PNL2
...
PNLT

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Sector Quantity and Percentages

Using the same data file as in the first 2 problems:
where the first 5 rows are:

symbol	side	qty	limitPrice	userData	finalPrice	sector
0 SCOR.PA	BUY	11583	20.38	tag[dma-euronext_par,mlp_id	1300647,time	15:31:58.800000,pe
1 WLN.PA	SELL	4489	41.59	tag[dma-euronext_par,mlp_id	1950270,time	15:31:58.800000,pe
2 LIGHT.AS	BUY	6230	34.82	tag[dma-euronext_aex,mlp_id	20378477,time	15:31:58.800000,pe
3 VIRB.PA	SELL	798	354.5	tag[dma-euronext_par,mlp_id	1262099,time	15:31:58.800000,pe
4 FOUG.PA	SELL	1	88.34	tag[dma-euronext_par,mlp_id	1271925,time	15:31:58.800000,pe

Task:
Compute the total quantity (qty), notional (limitPrice * qty), and % notional by sector, making sure you remove orders where the limit price is missing.

Output:
Return an integer array (rounded up) containing:

1. Total quantity for the Technology sector
2. Total notional for the Technology sector
3. % Notional for the Technology sector

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