# Trading Profit Loss Diagram and Simple Trading Probabilities

## Part 1. Trader Performance

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Quick and easy calculation for profit loss of your trades over time using Python. Track your performance in trading with these simple distributions and probabilities of your trades.

## Part 2. Stock Market Data

Some basic mean reversion problability calculations to get you started using the $SPY ETF. If and how you use it is up to you to figure out.

# Prerequisites

`pip3 install pandas`

pip3 install matplotlib

pip3 install yfinance

pip3 install numpy

pip3 install pandas

pip3 install scipy

pip3 install seaborn

# Part 1. Trader Performance

## The Data

For we use the trader’s data the input `report.csv`

format is as follows of all the trades made:

`CloseTime,Instrument,Profit/Loss`

DD/MM/YYYY,USA 500, 00000.00

DD/MM/YYYY,USA 500, -00000.00

## The Result

Results for profit_loss.py shows all the trades over time.

profit_loss_distributions.py shows a normal bell curve of all the trader’s trades. Most of my trades are immediately stopped out just below $0.

trader_prob_of_profit.py shows the odds of a trader being profitable on a trade. I am a profitable trader with 143% yoy and yet in stark contrast my odds of profit on a single trade are 30:70. More importantly, I split these odds into six categories. While I am doing significantly better on larger trades, I am significantly underperforming on smaller trades, and that is where I need to improve. This could best be remedied by moving my stop-loss orders just above breakeven, rather than just below.

Prob of profit: 29.411764705882355 Prob of loss: 70.27863777089783