Successful Projects:

Analyze stock broker reports of past long/short trades, constructed of multiple sub-accounts, currencies, exchanges, with fund inflow, outflow, and dividends.

Analyze pitfalls, over/under allocation, bad trade habits, root cause analysis, visual display of many aspects of historical trades such as equity curves, drawdowns, and pnl distribution.

Fetch historical quotes from https://cryptowat.ch/, in different time frames, use the historical quotes to train 3 machine learning models( LogisticRegression, RandomForestClassifier, KNeighborsClassifier) to categorize price pattern, determine the price next move and make a trade.

Run Algorithm to plan future trades for the next day, implement a scheduling engine to execute the trades according to time, pattern, thresholds,… conditions, make sure the trade is executed, log and monitor the trade until successful termination, use Interactive Brokers or Robinhood as the target broker.

Research, backtest and live trade a number of approaches to build an algorithm to trade equity, bonds, credit, and currencies, backtest 10 years of historical data, machine learning algorithms to timely enter and exit the market, manage open positions and close trades, develop profitable algorithms to trade the underlying assets with high expectancy and target function of Sharpe ration above 2.0, use position and day trading techniques.

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