  Algorithmic trading or Financial robots have been conquering the stock
markets with their ability to fathom complex statistical trading strategies.
But with the recent development of deep learning technologies, these strategies
are becoming impotent. The DQN and A2C models have previously outperformed
eminent humans in game-playing and robotics. In our work, we propose a
reinforced portfolio manager offering assistance in the allocation of weights
to assets. The environment proffers the manager the freedom to go long and even
short on the assets. The weight allocation advisements are restricted to the
choice of portfolio assets and tested empirically to knock benchmark indices.
The manager performs financial transactions in a postulated liquid market
without any transaction charges. This work provides the conclusion that the
proposed portfolio manager with actions centered on weight allocations can
surpass the risk-adjusted returns of conventional portfolio managers.
