Python Algorithmic Crypto-Trading Automation
Budget: $1,500 – $3,000 AUD
I’m looking for an experienced Python algo developer to automate a mechanical Sweep-and-Reclaim trading strategy for crypto. The rules are already defined in a strategy playbook — your job is to implement them exactly, with no discretion.
The project involves building:
A full Python implementation of the strategy
A backtesting engine using the same logic
A Bybit testnet/live trading module
A simple visual plotting interface to verify entries/exits
A config file with adjustable variables (timeframes, filters, risk settings, etc.)
This is not strategy creation — this is purely mechanical execution of existing rules.
CORE REQUIREMENTS
1. Python Strategy Automation
You will implement the logic from my playbook, including:
External structure alignment
Internal structure + iBOS
Discount/premium checks
Liquidity sweep detection
Reclaim candle validation
Momentum/confirmation entry logic
Liquidity-to-target filter
Swing-based stop loss + RR targets
All rules are mechanical and clearly defined (shared after shortlisting).
2. Backtesting Engine
Must use the exact same logic as live trading
Must run on historical candle data (CSV or CCXT)
Must output:
winrate
RR distribution
drawdown
PnL curve
Optional: chart with entries/exits
3. Bybit Integration
Testnet first (fake money)
Then live mode using the same code
Market/limit entries
SL/TP placement
Handling partial fills & order errors
Logging of every signal and trade
I will provide API keys for testnet.
4. Visual Plotting Interface
I need a simple way to see what the bot is doing to confirm accuracy.
A Plotly-based chart is ideal, showing:
Candles
Sweep levels
Reclaim candles
Structure lines
Entries & exits
SL/TP
Session windows
This must be easy to generate for any day or backtest segment.
5. Config File (Adjustable Variables)
A structured YAML/JSON config with adjustable settings, such as:
Symbol (BTCUSDT, ETHUSDT, etc.)
Timeframes (bias TF + entry TF)
Allowed directions (longs/shorts)
Session times
Sweep threshold
Reclaim rules
Confirmation rules
SL & TP parameters
Risk %
Plotting on/off
This allows me to tweak the strategy without editing the source code.
DEVELOPER REQUIREMENTS
Strong Python experience
Proven track record with trading bots or backtesting
Familiarity with structure-based or liquidity-based trading is a big advantage
Clear communication and clean code structure
Ability to deliver both backtest and live-ready code
WHAT I WILL PROVIDE
Full written strategy playbook
Visual examples of entries
Clarifications where needed
Testnet API keys
The project involves building:
A full Python implementation of the strategy
A backtesting engine using the same logic
A Bybit testnet/live trading module
A simple visual plotting interface to verify entries/exits
A config file with adjustable variables (timeframes, filters, risk settings, etc.)
This is not strategy creation — this is purely mechanical execution of existing rules.
CORE REQUIREMENTS
1. Python Strategy Automation
You will implement the logic from my playbook, including:
External structure alignment
Internal structure + iBOS
Discount/premium checks
Liquidity sweep detection
Reclaim candle validation
Momentum/confirmation entry logic
Liquidity-to-target filter
Swing-based stop loss + RR targets
All rules are mechanical and clearly defined (shared after shortlisting).
2. Backtesting Engine
Must use the exact same logic as live trading
Must run on historical candle data (CSV or CCXT)
Must output:
winrate
RR distribution
drawdown
PnL curve
Optional: chart with entries/exits
3. Bybit Integration
Testnet first (fake money)
Then live mode using the same code
Market/limit entries
SL/TP placement
Handling partial fills & order errors
Logging of every signal and trade
I will provide API keys for testnet.
4. Visual Plotting Interface
I need a simple way to see what the bot is doing to confirm accuracy.
A Plotly-based chart is ideal, showing:
Candles
Sweep levels
Reclaim candles
Structure lines
Entries & exits
SL/TP
Session windows
This must be easy to generate for any day or backtest segment.
5. Config File (Adjustable Variables)
A structured YAML/JSON config with adjustable settings, such as:
Symbol (BTCUSDT, ETHUSDT, etc.)
Timeframes (bias TF + entry TF)
Allowed directions (longs/shorts)
Session times
Sweep threshold
Reclaim rules
Confirmation rules
SL & TP parameters
Risk %
Plotting on/off
This allows me to tweak the strategy without editing the source code.
DEVELOPER REQUIREMENTS
Strong Python experience
Proven track record with trading bots or backtesting
Familiarity with structure-based or liquidity-based trading is a big advantage
Clear communication and clean code structure
Ability to deliver both backtest and live-ready code
WHAT I WILL PROVIDE
Full written strategy playbook
Visual examples of entries
Clarifications where needed
Testnet API keys