Python Trading Bot WITH OPTION TRADING MULTILAGE STRATEGYS
Budget: ₹600 – ₹1,500 INR
I need a robust, well-structured Python bot that can both run live logic and back-test the very same logic inside a stock market simulator. The focus is strictly OPTION TRADING MULTILAGE STRATEGYS stock-market data; forex or crypto TRADING
Here is what I’m after:
# SUPORT COPY TRADING MULTI USER, MULTI BROKER
• Core script(s) that read clear entry/exit rules, place simulated orders, and record every fill, P&L, draw-down, and equity curve.
• Back-testing layer tied to a stock market simulator such as Backtrader, QuantConnect, Zipline, or any other mature framework you are comfortable with—so long as it supports multi-year historical data, realistic fills, and commission/slippage models.
• Clean separation between strategy logic and engine so I can slot in new strategies without rewriting the plumbing.
• Output reports in CSV/Excel plus a quick matplotlib/Plotly chart showing cumulative return versus benchmark.
• Brief README that explains setup, how to add a new strategy, and how to launch a back-test or a paper-trade session.
Acceptance criteria:
1. I point the bot at a sample strategy file and a date range, hit “run”, and receive an equity curve that matches a known reference within tolerances.
2. All major functions covered by unit tests.
3. Code passes PEP8 and runs on Python 3.11 in a fresh virtual env.
If that checklist is clear and you’re fluent with pandas, NumPy, TA-Lib (or your own indicator code), and a proven stock market simulator, then let’s move forward.
Here is what I’m after:
# SUPORT COPY TRADING MULTI USER, MULTI BROKER
• Core script(s) that read clear entry/exit rules, place simulated orders, and record every fill, P&L, draw-down, and equity curve.
• Back-testing layer tied to a stock market simulator such as Backtrader, QuantConnect, Zipline, or any other mature framework you are comfortable with—so long as it supports multi-year historical data, realistic fills, and commission/slippage models.
• Clean separation between strategy logic and engine so I can slot in new strategies without rewriting the plumbing.
• Output reports in CSV/Excel plus a quick matplotlib/Plotly chart showing cumulative return versus benchmark.
• Brief README that explains setup, how to add a new strategy, and how to launch a back-test or a paper-trade session.
Acceptance criteria:
1. I point the bot at a sample strategy file and a date range, hit “run”, and receive an equity curve that matches a known reference within tolerances.
2. All major functions covered by unit tests.
3. Code passes PEP8 and runs on Python 3.11 in a fresh virtual env.
If that checklist is clear and you’re fluent with pandas, NumPy, TA-Lib (or your own indicator code), and a proven stock market simulator, then let’s move forward.