Every metric shows how we are systematically designed to outperform the market.
| Strategy | Annual Return | Sharpe Ratio | Win-Rate | Max Drawdown |
|---|---|---|---|---|
| Quantum Momentum | +149% | 1.97 | 71.4% | -17.9% |
| Markov Chain Scalper | +98% | 2.10 | 65.2% | -13.8% |
| Advanced Donchian | +125% | 2.16 | 74.6% | -19.5% |
| Trend Following ADX | +153% | 1.26 | 61.9% | -26.1% |
| 100+ Other Strategies | +100.0% | +2.00 | +70% | -15.0% |
The above data is the backtesting results from some of our algorithm strategies since 2020 (on average).
We trade using probability and structure, not predictions. Every move is driven by data, not emotion or noise.
We generate returns from market inefficiencies through disciplined, data-based research, not speculation.
We give retail investors access to institutional-grade quantitative systems with full structure, transparency, and discipline.
Our project is built on a robust system and code. Below is an example snippet of the code (simplified version).
class ExampleStrategy(Strategy):
@property
def slow_sma(self):
return ta.sma(self.candles, 200)
@property
def fast_sma(self):
return ta.sma(self.candles, 50)
def should_long(self) -> bool:
# Fast SMA above Slow SMA
return self.fast_sma > self.slow_sma
def should_short(self) -> bool:
# Fast SMA below Slow SMA
return self.fast_sma < self.slow_sma
def should_cancel_entry(self) -> bool:
return False
def go_long(self):
# Open long position and use entire balance to buy
qty = utils.size_to_qty(self.balance, self.price, fee_rate=self.fee_rate)
self.buy = qty, self.price
def go_short(self):
# Open short position and use entire balance to sell
qty = utils.size_to_qty(self.balance, self.price, fee_rate=self.fee_rate)
self.sell = qty, self.price
def update_position(self):
# If there exist long position, but the signal shows Death Cross, then close the position, and vice versa.
if self.is_long and self.fast_sma < self.slow_sma:
self.liquidate()
if self.is_short and self.fast_sma > self.slow_sma:
self.liquidate()
Quant Seeker is a private investment fund fully managed by algorithmic systems. Every trading decision is data-driven, structured, and free from emotional bias.
Unlike mutual funds or robo-advisors that follow passive mandates, Quant Seeker employs active trading strategies based on statistical signals, mathematical models, and market inefficiencies.
Yes. Quant Seeker is designed to make institutional-grade quantitative investing accessible to retail investors, lowering the traditional entry barriers of hedge funds.
Our system applies statistical and mathematical logic to identify high-probability setups, quantify risk, and execute trades automatically with consistency and precision.
Profits are distributed under a 40:60 model, with 40 percent to investors and 60 percent to the quant manager, governed by a High Watermark structure to ensure fairness and accountability.
No. There are no extra fees charged to investors. All costs related to servers, research, ecosystem development, and management are fully covered by the quant manager.
No investment is entirely risk-free. Quant Seeker focuses on risk-adjusted returns and capital preservation through diversification, strict drawdown limits, and disciplined execution.
The fund operates autonomously under predefined algorithmic parameters. Human oversight ensures the system performs as intended and adapts to evolving market conditions when necessary.
Investments are subject to a one-year minimum lock-up period to maintain portfolio stability. Early withdrawals are only permitted under exceptional circumstances through a formal investor meeting.
Capital adjustments are not allowed during the trading period. Investors may add or withdraw funds only during the December cut-off window, effective from January of the following year.
Performance is tracked in real time, benchmarked against market indices, and reviewed monthly. Detailed reports are delivered to all investors to ensure full transparency.
Currently an Analyst at BRI Danareksa Sekuritas, with over six years of multi-asset investment experience and two years in algorithmic trading. Certified in FMVA® and CMSA®, focusing on data-driven investment strategies.
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Investment slots are very limited. We conduct a selection process for strategic partner candidates. The first step is to complete the registration form for our review.