Multi-Factor Crypto Strategy Developer
Budget: €250 – €750 EUR
Project Overview
I am looking for an experienced quantitative trading developer with strong expertise in TradingView Pine Script, Python backtesting, and statistical strategy validation.
The goal is not to create another simple indicator based on RSI or MACD. Instead, I want to develop a data-driven multi-factor trading score for XRP that identifies high-probability buy zones, sell zones, and warning zones based on historical validation.
The project should focus on objective statistical evidence, not subjective technical analysis.
Objectives
The project consists of three phases:
Phase 1 – Historical Research & Validation
Analyze the historical performance of various technical indicators and determine which combinations have produced the highest probability trading signals.
The objective is to answer questions such as:
Which indicators historically identified XRP bottoms most accurately?
Which indicators successfully identified local or major tops?
Which combinations significantly reduced false signals?
Which indicators should receive the highest weighting?
Which market conditions produced the highest expectancy?
Phase 2 – Develop a Weighted Scoring Model
Based on the historical research, create a weighted scoring system ranging from 0 to 100 points.
Example:
Indicator Score
Daily RSI Oversold +10
Weekly RSI Oversold +10
Bullish MACD Cross +10
EMA Trend Alignment +10
BTC Dominance Confirmation +10
USDT Dominance Confirmation +15
XRP/BTC Relative Strength +10
Fibonacci Extension Support +15
Bullish Divergence +10
The weighting should be based on statistical performance rather than assumptions.
Phase 3 – TradingView Pine Script
Implement the validated scoring model as a TradingView Pine Script.
The script should display:
Overall Score (0–100)
Buy Zone
Strong Buy Zone
Sell Zone
Warning Zone
Long Entry Signal
Take Profit Signal
Alert Conditions
The script should be designed as a decision-support tool, not an automated trading bot.
Indicators to Evaluate
Please analyze and validate combinations of:
RSI (Daily & Weekly)
MACD
EMA 13 / 50 / 200
Fibonacci Retracements
Fibonacci Extensions
Trend-Based Fibonacci Extensions
BTC Dominance
USDT Dominance
XRP/BTC Relative Strength
Volume
Volume Profile
Point of Control (POC)
Bullish & Bearish Divergences
Market Structure
Trend Strength
Optional:
Elliott Wave recognition (only if it can be implemented objectively and rule-based)
Historical Backtesting Requirements
Backtest period:
2017 – Present
Please provide:
Win Rate
Profit Factor
Maximum Drawdown
Average Trade
Total Trades
Average Holding Time
Best Performing Indicator Combinations
Worst Performing Indicator Combinations
Buy & Hold Comparison
Equity Curve
Technical Requirements
Preferred Skills:
Pine Script v5/v6
Python
Pandas
Backtesting.py or similar frameworks
TradingView
Quantitative Trading
Crypto Markets
Important Requirements
The system should:
Avoid repainting signals
Use only objective and reproducible rules
Be statistically validated
Minimize false positives
Work primarily for XRP but be adaptable to other cryptocurrencies
Deliverables
Historical validation report
Statistical performance analysis
Weighted scoring model
Pine Script source code
Documentation explaining the scoring methodology
Recommendations for future improvements
Nice to Have
Experience with:
Smart Money Concepts (SMC)
Wyckoff
Volume Profile
Machine Learning
Crypto quantitative research
Multi-timeframe analysis
Project Goal
The final result should be a professional decision-support system that combines multiple statistically validated indicators into a single probability score.
The objective is to identify high-probability buying opportunities, optimal profit-taking zones, and periods of elevated market risk while avoiding decisions based on emotions or subjective chart interpretations.
Please include examples of previous quantitative trading systems, Pine Script projects, or backtesting work in your proposal.
I am looking for an experienced quantitative trading developer with strong expertise in TradingView Pine Script, Python backtesting, and statistical strategy validation.
The goal is not to create another simple indicator based on RSI or MACD. Instead, I want to develop a data-driven multi-factor trading score for XRP that identifies high-probability buy zones, sell zones, and warning zones based on historical validation.
The project should focus on objective statistical evidence, not subjective technical analysis.
Objectives
The project consists of three phases:
Phase 1 – Historical Research & Validation
Analyze the historical performance of various technical indicators and determine which combinations have produced the highest probability trading signals.
The objective is to answer questions such as:
Which indicators historically identified XRP bottoms most accurately?
Which indicators successfully identified local or major tops?
Which combinations significantly reduced false signals?
Which indicators should receive the highest weighting?
Which market conditions produced the highest expectancy?
Phase 2 – Develop a Weighted Scoring Model
Based on the historical research, create a weighted scoring system ranging from 0 to 100 points.
Example:
Indicator Score
Daily RSI Oversold +10
Weekly RSI Oversold +10
Bullish MACD Cross +10
EMA Trend Alignment +10
BTC Dominance Confirmation +10
USDT Dominance Confirmation +15
XRP/BTC Relative Strength +10
Fibonacci Extension Support +15
Bullish Divergence +10
The weighting should be based on statistical performance rather than assumptions.
Phase 3 – TradingView Pine Script
Implement the validated scoring model as a TradingView Pine Script.
The script should display:
Overall Score (0–100)
Buy Zone
Strong Buy Zone
Sell Zone
Warning Zone
Long Entry Signal
Take Profit Signal
Alert Conditions
The script should be designed as a decision-support tool, not an automated trading bot.
Indicators to Evaluate
Please analyze and validate combinations of:
RSI (Daily & Weekly)
MACD
EMA 13 / 50 / 200
Fibonacci Retracements
Fibonacci Extensions
Trend-Based Fibonacci Extensions
BTC Dominance
USDT Dominance
XRP/BTC Relative Strength
Volume
Volume Profile
Point of Control (POC)
Bullish & Bearish Divergences
Market Structure
Trend Strength
Optional:
Elliott Wave recognition (only if it can be implemented objectively and rule-based)
Historical Backtesting Requirements
Backtest period:
2017 – Present
Please provide:
Win Rate
Profit Factor
Maximum Drawdown
Average Trade
Total Trades
Average Holding Time
Best Performing Indicator Combinations
Worst Performing Indicator Combinations
Buy & Hold Comparison
Equity Curve
Technical Requirements
Preferred Skills:
Pine Script v5/v6
Python
Pandas
Backtesting.py or similar frameworks
TradingView
Quantitative Trading
Crypto Markets
Important Requirements
The system should:
Avoid repainting signals
Use only objective and reproducible rules
Be statistically validated
Minimize false positives
Work primarily for XRP but be adaptable to other cryptocurrencies
Deliverables
Historical validation report
Statistical performance analysis
Weighted scoring model
Pine Script source code
Documentation explaining the scoring methodology
Recommendations for future improvements
Nice to Have
Experience with:
Smart Money Concepts (SMC)
Wyckoff
Volume Profile
Machine Learning
Crypto quantitative research
Multi-timeframe analysis
Project Goal
The final result should be a professional decision-support system that combines multiple statistically validated indicators into a single probability score.
The objective is to identify high-probability buying opportunities, optimal profit-taking zones, and periods of elevated market risk while avoiding decisions based on emotions or subjective chart interpretations.
Please include examples of previous quantitative trading systems, Pine Script projects, or backtesting work in your proposal.