Shiva came to the Breakout Strategies Masterclass with a software development background, an intermittent creative work pattern he’d learned to work with rather than fight, and a father who had introduced him to investing. Within six months of joining the programme, he had validated the framework with rigorous out-of-sample testing, automated his entire strategy-building process, and launched a live portfolio trading three stocks – with 15 more strategies in incubation.
His story is worth examining in detail not because of the speed – though the speed is impressive – but because of the methodology. Shiva approached the Masterclass as a process engineer, not just a student. He built in verification steps, developed confidence before going live rather than hoping for it, and made deliberate choices about what to customise and what to leave alone. Those distinctions are what made the difference between six months to live trading and the multi-year development loops that trap many systematic traders.
Why Automated Trading Fits Shiva’s Working Style
Shiva was candid about something most traders don’t admit: his natural level of motivation isn’t constant. He has periods of high creative energy – when strategy ideas come easily, when he can work for hours without effort – and periods where motivation is low and creativity is nearly absent.
For most career paths, this variability is a liability. For automated trading, it’s almost an advantage.
“When I’m highly motivated and highly creative, I can go full steam and build as many strategies as I can come up with. And then when that motivation dies away, it’s fine, because I have a suite of strategies that are running live, and I just go into maintenance mode.”
This maps directly onto what systematic trading requires at different stages. Development mode is creative, active, and reward-seeking. Live trading mode is passive, patient, and observational. Shiva’s natural rhythm – bursts of intense work followed by periods of lower engagement – is structurally compatible with that cycle in a way that a constant-output personality might not be.
The broader point: algorithmic trading is well suited to people who want to leverage periods of high productivity into persistent results that keep working during the low-productivity periods. The system runs whether you’re actively engaged with it or not.
How Shiva Built Confidence in the Framework
Rather than launching directly into building strategies for live trading, Shiva’s first move was to validate whether the Masterclass framework actually produced strategies that held up out-of-sample.
His method was systematic and smart. He blocked off all data from 2017 forward – treating it as an unseen out-of-sample set – and built 10 strategies using data from before 2017, following the Masterclass process exactly. The universe was ETFs (including index ETFs and commodity-linked ones like USO) and individual stocks selected from the IBD 50 as it stood at the beginning of 2017.
He used Robustness Levels 2 and 3, the strongest robustness classifications in the Masterclass framework. Critically, he didn’t look at any out-of-sample data until all 10 strategies were developed. This prevented any unconscious feedback loop between the test results and the strategy-building decisions.
Then he looked at the portfolio equity curve from 2017 forward. His test: could he identify where out-of-sample began without being told? If the equity curve flowed smoothly and continuously, with no visible transition from in-sample to out-of-sample behaviour, the framework had passed.
It passed. “The out-of-sample flows very smoothly from the in-sample.”
That result gave him something money can’t directly buy: genuine confidence in the process. Not optimism. Not hope. Actual evidence that the methodology produces strategies that perform consistently in unseen data. From that point, going live wasn’t a leap of faith – it was a logical next step.
Owning the Process Without Breaking It
One of the more nuanced points Shiva made is worth dwelling on. He respected the Masterclass framework as a proven system – Tomas runs the exact same process in his hedge fund, and the track record is real. Changing the core methodology would have been substituting his inexperience for years of validated research.
But he also recognised that ownership matters psychologically. Traders are, as Tomas noted with some amusement, “a little more than a little bit egoistic.” You need to feel that the process is yours, not just a set of rules you’re following on faith.
Shiva’s solution was elegant: he personalised everything around the core process without touching the core itself. His tracking system, his documentation format, his automation scripts, his approach to parameter setting – all of these were built to his own preferences and style. The strategy logic and robustness testing methodology were left exactly as the Masterclass prescribed.
He used an analogy that Tomas immediately loved: it’s like buying a new car. You don’t modify the engine because you don’t know enough about engines to improve it. But you organise the interior to suit yourself. The car performs exactly as the manufacturer designed; it just feels like yours.
This principle is generalisable. In any proven framework, the parts that have been validated through years of real trading are the parts you shouldn’t change. The parts that are genuinely personal – documentation, tracking, workflow, automation architecture – are the parts where customisation is appropriate and beneficial.
The Live Portfolio: What He Built and Why
For his first live portfolio, Shiva made several deliberate decisions that differ from the default approach many new traders take.
Single timeframe, 30 minutes only. Rather than testing each instrument across multiple timeframes (30 minutes, 60 minutes, 240 minutes), he used 30 minutes exclusively. His reasoning: with stocks, the universe of instruments is wide enough that if one doesn’t yield a good strategy at 30 minutes, moving on to the next instrument is more efficient than exhaustively testing multiple timeframes on the same one. This also reduces the risk of over-mining a single instrument until you find something that fits.
No second data stream. He kept the strategy logic clean and reduced the parameter count, which in turn reduced curve-fitting risk and kept each strategy’s statistical validity higher.
Long only on equities. He was comfortable with a long bias in stocks – it reflects the structural long-term drift of equity markets – and avoided shorts partly because accurately modelling historical short availability is difficult in stocks, which would have introduced back-test inaccuracy.
Stock selection from IBD and Zacks. Rather than randomly sampling the stock universe, he used the IBD 50 (his father’s analytical source) and Zacks Rank 1 stocks as a pre-filter for quality. He also filtered out stocks with recent splits and required sufficient liquidity. These filters reduced the search space and focused effort on instruments with genuine fundamental backing.
His first live portfolio launched with three stocks: Amazon, SPY, and CHTR. He later replaced CHTR with ABMD after discovering that actual live slippage exceeded his conservative back-test assumptions – a genuine live-trading learning that back-tests can’t fully model. He had 15 additional strategies in incubation at time of recording.
Two and a Half Months In: What the Results Actually Show
Two and a half months after launching, Shiva had just under 50 trades across three strategies. His portfolio was approximately at breakeven – a little up, a little down, moving sideways.
His interpretation of this was exactly right: “I attribute that to having only three systems. So you’re exposed to a lot more randomness when you do that. And two, just the amount of time.”
Fifty trades across three strategies is nowhere near enough to draw conclusions about performance. It’s a sample size that can’t distinguish genuine edge from noise. Breaking even with that sample size, in an incubation period that’s still too short to mean much, is actually a reasonable start.
Tomas noted that his own hedge fund’s new CTA programme, launched around the same time, was also essentially flat. This is normal for algorithmic portfolios in their first two to three months. The equity needs to develop. The sample needs to grow. The strategies need to show their character across different market conditions.
The Strategy Addition Protocol
Shiva had a disciplined system for deciding when to add new strategies to the live portfolio – a system he’d developed himself, which is a good example of legitimate process customisation.
Rather than adding strategies at equity highs (a common and generally unfortunate choice), he planned to wait for the equity to breach its previous high, pull back slightly, and then add. He also tied the number of new strategies he could add to the cumulative live trade count: 10 live trades per strategy. With 50 trades live, he could add up to five new strategies – if the other conditions were met.
He acknowledged he might raise that threshold to 15 trades per strategy as he learned more. The key point is that the decision is algorithmic, not discretionary. There’s a rule. The rule is followed. This is exactly the mindset that prevents the common mistake of adding strategies impulsively after a good run, then watching the portfolio destabilise.
Shiva’s Takeaways for Other Traders
When asked for advice for other traders, Shiva offered two points – one philosophical, one practical.
The philosophical one: most dissatisfaction comes from the gap between expectations and reality. The more precisely you can identify what you actually control, and calibrate your expectations to match that, the less suffering you create for yourself. This is fundamentally a Stoic framing – focus on what you can control, accept what you can’t.
In trading specifically, this means being honest about the likely trajectory of a new portfolio. The first two months could be breakeven or a small loss. The first three to four months depends heavily on trade count, market conditions at launch time, and whether you happened to start at an unlucky period. None of that reflects the quality of your strategies. It reflects the statistical reality of small samples and market randomness.
The practical one: automate as much of the strategy-building process as possible, as early as possible. “If you automate your entire process, you can sit back. You know that it’s going to keep churning.” The scarcity mindset – where each Level 3 strategy feels like a rare, irreplaceable find – is what leads traders to force strategies into working and compromise on quality. Automation removes that scarcity by making the process itself reliable and repeatable.
As Tomas summarised it: automation equals happiness. The formula is more nuanced than that in practice, but the direction is right.
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