Daniel Nazaretian
About Me
I am an Actuarial Science student at Monash University with a keen interest in financial markets and trading. I’m drawn to the challenge of finding structure in noisy data: whether that’s building machine learning models to explore market behaviour, or competing in trading simulations.
Outside of this, I enjoy learning languages, currently studying Japanese at university, and Armenian in my own time. I bring the same pattern recognition I apply to chess and poker into how I think about risk and decision-making.
Projects
AFL Prediction Website
To be updated
Forex Time Macro Exploration - Built an exploratory machine learning model in R to test hypotheses and learn data-driven approaches to market behaviour at different times
Here are the variables I used for regression. I came up with these variables based on research of what would be most useful, and adapted my variables several times. I scraped this data from the Forex Factory website, and the TradingView app.
I found that when a variable is not statistically significant in an R regression output it is referred to as a non-significant predictor or insignificant variable.
Variables used
str(macrodata)
# 'data.frame': 206 obs. of 13 variables:
# $ Return.close.10 : int 0 0 1 0 1 0 0 0 0 0 ...
# $ Largest.move : num 14.2 -18.1 19.1 -16.5 15.9 ...
# $ Macro.position : int 1 2 1 2 1 2 1 2 1 2 ...
# $ ny_Session : chr "PM" "PM" "AM" "AM" ...
# $ Severe.news : int 0 0 0 0 0 0 0 0 1 1 ...
# $ Major.news : int 1 1 1 1 0 0 4 4 2 2 ...
# $ Moderate.news : int 4 4 4 4 0 0 4 4 2 2 ...
# $ Hour..UTC. : int 1 3 9 10 9 10 1 3 9 10 ...
# $ Day : int 6 6 6 6 2 2 2 2 3 3 ...
# $ month_end : int 0 0 0 0 1 1 1 1 1 1 ...
# $ Movement._before : chr "Bull" "Bull" "Bear" "Bull" ...Linear regression
training <- macrodata[1:14, ]
test <- macrodata[15:29, ]
fit <- lm(Return.close.10 ~ Largest.move + Macro.position +
ny_Session + Severe.news + Major.news + Moderate.news +
non.US.bank.holiday + US.News.within.hour +
Hour..UTC. + Day + Month_end + Movement._before,
data = training)Overall, no single macroeconomic variable was found to be a statistically significant predictor of returns, suggesting that forex price behaviour at specific times may be more complex than simple linear relationships can capture.
Nous Data Analytics Virtual Experience
Performed exploratory analysis of ABS datasets in R, identifying correlations between teacher-student ratios and school performance
colnames(data)[2] <- "State"
colnames(data)[5] <- "ST_Ratio"
QLD_all <- data %>% filter(State == "QLD")
mean(QLD_all$ST_Ratio)
library(ggplot2)
ggplot(QLD_all, aes(x = factor(Year), y = ST_Ratio, fill = Affiliation)) +
geom_bar(stat = "identity", position = position_dodge()) +
labs(x = "Year", y = "Student-Teacher Ratio",
title = "Student-Teacher Ratios by Affiliation in QLD") +
theme_minimal()
Competitions
CME Group Trading Competition- August
Test-drive the world’s top futures products in Trading Challenges using real-time market data in a risk-free simulated environment.
Top 6%
Challenges
The biggest challenge was making trading decisions under pressure with real time market data moving constantly. Unlike studying markets in theory, the competition forced us to act quickly and commit to positions without overthinking. Reading and interpreting charts in the moment, such as identifying trends, entry points, and when to cut losses, was far harder in practice than it looked on paper. There were moments where the market moved against my position and the instinct to panic and exit early was difficult to resist.
Economics Student Society Australia Consulting Competition
Flow Traders Trading Challenge
To be updated
UNIHACK 2026
What it is
UNIHACK is one of Australia’s largest student hackathons, funded by the European Union. Competing as part of a small team over a 48-hour weekend, the challenge is to design and build a working software solution from scratch under time pressure.
My role
As the non-programmer on the team, my contribution focused on problem framing, solution design, and presenting the final product. I also contributed code snippets where I could, working alongside teammates to understand and participate in the technical side.
Code snippet
(To be updated during/after the event)
# placeholder — update with actual code after UNIHACKResult
(To be updated after the event)
What I learned
(To be updated after the event)
Volunteering
Monash University Computing and Commerce Association October 2025 – Present
Publications Officer
Collaborated with other publications officers to create the 2026 First Year Guide | View on CCA Website
Articles will slowly be released throughout the year.
Here’s a sneak peek of one of my articles: What is it? Quantitative Trading Explained | View on CCA Website
Have you heard the words quantitative trading? Maybe you have heard about its reputation being one of the most difficult industries to be in, and get into. But what actually is it? And how can you prepare for it?….